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Appearing Smart, Confident and Motivated: A Lens Model Approach to Judgment Accuracy in an Educational Setting

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Title: Appearing Smart, Confident and Motivated: A Lens Model Approach to Judgment Accuracy in an Educational Setting
Language: English
Authors: Caroline V. Bhowmik (ORCID 0000-0003-3754-4135), Mitja D. Back, Steffen Nestler, Friedrich-Wilhelm Schrader
Source: Social Psychology of Education: An International Journal. 2025 28(1).
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 34
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Evaluative Thinking, Accuracy, Teacher Attitudes, College Students, Preservice Teachers, Nonverbal Communication, Cues, Student Motivation, Intelligence, Gender Differences, Assistive Technology, Student Characteristics, Gender Bias
DOI: 10.1007/s11218-025-10057-1
ISSN: 1381-2890
1573-1928
Abstract: Which behavioral and visual information do teachers rely on when judging relevant characteristics of their students and which cues should they rely on? Drawing on Brunswik's Lens Model (Perception and the representative design of psychological experiments, University of California Press, 1956. https://doi.org/10.1525/9780520350519), we investigated the role of students' expression of nonverbal behavioral cues (e.g., friendly facial expression) and physical appearance (e.g., wearing eyeglasses) and how this information is utilized during the judgment process by pre-service teachers and psychology students (N = 102). Perceivers provided ratings of students' (N = 45) academic self-concept, intelligence and motivation in brief nonverbal video clips showing one student each in a physics classroom. Numerous behavioral and physical cues (in total 165) were extracted from the stimulus material by two independent raters. Perceivers achieved highest accuracy for students' motivation, whereas intelligence was judged with the lowest accuracy. Lens model parameter analysis indicated that perceivers strongly relied on students' sex, an attentive and self-assured facial expression, and whether or not a student was wearing eyeglasses in their judgments. Cues that were actually related to students' characteristics, on the other hand, involved students' sex, a masculine and distinctive appearance, and a tensed as well as friendly facial expression. An overall favorable judgment for boys points into the direction of a gender bias. Implications for our understanding of teacher judgment processes and outcomes are discussed.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1470215
Database: ERIC
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  Value: <anid>AN0184915346;luo02may.25;2025May06.02:13;v2.2.500</anid> <title id="AN0184915346-1">Appearing smart, confident and motivated: a lens model approach to judgment accuracy in an educational setting </title> <p>Which behavioral and visual information do teachers rely on when judging relevant characteristics of their students and which cues should they rely on? Drawing on Brunswik's Lens Model (Perception and the representative design of psychological experiments, University of California Press, 1956. https://doi.org/10.1525/9780520350519), we investigated the role of students' expression of nonverbal behavioral cues (e.g., friendly facial expression) and physical appearance (e.g., wearing eyeglasses) and how this information is utilized during the judgment process by pre-service teachers and psychology students (N = 102). Perceivers provided ratings of students' (N = 45) academic self-concept, intelligence and motivation in brief nonverbal video clips showing one student each in a physics classroom. Numerous behavioral and physical cues (in total 165) were extracted from the stimulus material by two independent raters. Perceivers achieved highest accuracy for students' motivation, whereas intelligence was judged with the lowest accuracy. Lens model parameter analysis indicated that perceivers strongly relied on students' sex, an attentive and self-assured facial expression, and whether or not a student was wearing eyeglasses in their judgments. Cues that were actually related to students' characteristics, on the other hand, involved students' sex, a masculine and distinctive appearance, and a tensed as well as friendly facial expression. An overall favorable judgment for boys points into the direction of a gender bias. Implications for our understanding of teacher judgment processes and outcomes are discussed.</p> <p>Keywords: Teacher judgment accuracy; Brunswik's lens model; Diagnostic competence; Thin-slices of behavior; Self-concept; Intrinsic motivation; Psychology and Cognitive Sciences Psychology</p> <hd id="AN0184915346-2">Introduction</hd> <p>Teachers' judgments about students are ubiquitous phenomena in the school context and they pertain to a broad range of characteristics (e.g., abilities, self-concept, motivation). These judgments can have an impact on teachers' decisions and behavior, as well as on teacher-student interaction and students' perceptions, motivation, and behavior. Accurate teacher judgments and impressions are important for everyday instruction, such as adjusting the teaching content and instruction to students' aptitudes and prior knowledge (<emph>adaptive teaching</emph>; e.g., Hardy et al., [<reflink idref="bib23" id="ref1">23</reflink>]). Inaccurate or biased teacher perceptions on the other hand can have unfavorable effects on students' achievement, motivation and even life satisfaction (<emph>expectancy effects</emph>; e.g., Bergold & Steinmayr, [<reflink idref="bib7" id="ref2">7</reflink>]; Friedrich et al., [<reflink idref="bib21" id="ref3">21</reflink>]; Jussim & Harber, [<reflink idref="bib27" id="ref4">27</reflink>]). Teacher judgment accuracy is typically measured by comparing teachers' judgments of students' characteristics with students' actual characteristics (criteria) measured by tests or some kind of self-report data. Whereas the majority of teacher' judgment accuracy research has been focused on judgments of students' academic achievement (for an overview, see Südkamp et al., [<reflink idref="bib62" id="ref5">62</reflink>]), more recent research extends this line of research by including a diverse set of other student characteristics that are expected to play a relevant role for students' learning processes and outcomes, among which are academic self-concept, intrinsic motivation as well as cognitive abilities (see Urhahne & Wijnia, [<reflink idref="bib65" id="ref6">65</reflink>] for a recent review as well as for instance Machts et al., [<reflink idref="bib38" id="ref7">38</reflink>]; Spinath, [<reflink idref="bib60" id="ref8">60</reflink>]).</p> <p>Traditionally, teachers' judgment accuracy is assessed at long-time acquaintance, i.e., for students whom the teacher knows for an extended period of time. In contrast, teachers' judgments at zero-acquaintance, which can be investigated on the basis of short behavioral episodes (<emph>zero acquaintance and thin-slices-of-behavior approach</emph>, Ambady & Rosenthal, [<reflink idref="bib4" id="ref9">4</reflink>]; Ambady et al., [<reflink idref="bib2" id="ref10">2</reflink>]), are to date poorly explored. Zero-acquaintance judgments in the school context refer to situations when a teacher meets a student for the first time and forms a first impression of the student. These first impressions are important because given that they are often stable (Darley & Fazio, [<reflink idref="bib16" id="ref11">16</reflink>]; Harris & Garris, [<reflink idref="bib24" id="ref12">24</reflink>]; Nickerson, [<reflink idref="bib49" id="ref13">49</reflink>]) they can determine subsequent judgments and long-term evaluations. Moreover, initial judgments can influence teachers' instructional and management decisions when teaching a school class for the first time, for example by identifying students who may rather support or disrupt a planned teaching sequence. There is a lack of knowledge about factors influencing teacher's judgment accuracy in a zero-acquaintance situation as well as about the judgment process per se, including the cognitive and behavioral aspects that lead to certain judgments (Schnitzler et al., [<reflink idref="bib57" id="ref14">57</reflink>]). In this study, we use Brunswik's Lens Model (BLM; Brunswik, [<reflink idref="bib13" id="ref15">13</reflink>]) to analyze factors that contribute to judgment accuracy with respect to important student characteristics (intelligence, self-concept and motivation). Students' self-concept and intrinsic motivation were chosen as criterion variables given the demonstrated strong relevance of motivational and emotional characteristics for students' learning processes and academic achievement in previous research (e.g., Möller et al., [<reflink idref="bib46" id="ref16">46</reflink>]), while at the same time, teachers seem to face difficulties in identifying suitable indicators in their judgments of students' motivational characteristics (see Urhahne & Winja, [<reflink idref="bib65" id="ref17">65</reflink>]). Intelligence ratings, on the other hand, have shown to be linked to perceptual biases and a utilization of less valid information in other scientific contexts (Murphy et al., [<reflink idref="bib43" id="ref18">43</reflink>]). The present research therefore aims to shed light onto such judgment processes that may lead to more or less accurate judgments and interpersonal consequences in the educational context.</p> <hd id="AN0184915346-3">Teacher judgments and judgment accuracy at zero-acquaintance</hd> <p>While the initial and early research on teacher judgment accuracy has been focused on judgments of students' academic achievement as the outcome of a learning activity, one can observe an increasing interest in teacher judgments of students' non-cognitive and emotional-motivational characteristics since the turn of the century. A teacher's accurate assessment of such characteristics is considered an important requirement for adapting and tailoring both instruction and classroom interaction to students' individual learning prerequisites (Praetorius et al., [<reflink idref="bib50" id="ref19">50</reflink>]). Academic self-concept is defined as mental representation of a student's academic abilities, which is developed through interactions with the environment (Shavelson et al., [<reflink idref="bib59" id="ref20">59</reflink>]). Students' perceptions about themselves as learners have shown to predict a wide range of educational outcomes, such as for instance academic behavior and choices, educational aspirations, as well as academic achievement and vice-versa (see works by Marsh, [<reflink idref="bib41" id="ref21">41</reflink>]; Marsh & Craven, [<reflink idref="bib40" id="ref22">40</reflink>]). Whereas the general academic self-concept describes a student's overall (positive or negative) evaluation of his or her academic ability, the subject-specific academic self-concept in physics refers to a student's perception of his or her academic ability in the physics discipline. As previous research has highlighted the subject-specifity of the construct (see e.g., Marsh, [<reflink idref="bib41" id="ref23">41</reflink>]), we included both broad and the subject-specific self-concept in physics. Intrinsic motivation in physics refers to the extent to which a student enjoys activities in physics and attending physics lessons at school. It was included as criterion variable given the importance that has been placed on motivation for facilitating desirable educational outcomes (e.g., Kriegbaum et al., [<reflink idref="bib33" id="ref24">33</reflink>]) but also because teachers' judgments of students' motivation can, among others, be included in influential decisions about students' academic prospects (Urhahne & Winja, [<reflink idref="bib65" id="ref25">65</reflink>]). On the theoretical level, intrinsic motivation and academic self-concept are considered as closely related constructs and teachers' accurate perceptions of such characteristics at an early point of an interaction can help to adapt subsequent instruction and communication with the students accordingly. For instance, if a teacher assesses a students' low self-concept at the beginning of a learning sequence or when meeting a student for the first time, he or she can differentiate the learning material and provide feedback accordingly with the aim to foster this individual students' motivation and the way the student thinks about him- or herself as a learner.</p> <p>Most frequently, accuracy is measured by correlating an individual teacher's judgments and the actual characteristics of the students in his or her class (i.e., <emph>rank order accuracy</emph>, also referred to as <emph>rank component</emph>; Helmke & Schrader, [<reflink idref="bib25" id="ref26">25</reflink>]; Praetorius et al., [<reflink idref="bib50" id="ref27">50</reflink>]; Südkamp et al., [<reflink idref="bib62" id="ref28">62</reflink>]). For traditional studies that serve as a reference, meta-analyses indicate an average accuracy of <emph>r</emph> = 0.63 for students' academic achievement (Südkamp et al., [<reflink idref="bib62" id="ref29">62</reflink>]) and <emph>r</emph> = 0.50 for cognitive ability (Machts et al., [<reflink idref="bib38" id="ref30">38</reflink>]). Accuracies of non-cognitive student characteristics are usually lower (0.29 ≤ <emph>r</emph> ≤ 0.55 for academic self-concept and 0.10 ≤ <emph>r</emph> ≤ 0.20 for school anxiety and motivation; Praetorius et al., [<reflink idref="bib51" id="ref31">51</reflink>], [<reflink idref="bib50" id="ref32">50</reflink>]; Spinath, [<reflink idref="bib60" id="ref33">60</reflink>]; Urhahne et al., [<reflink idref="bib64" id="ref34">64</reflink>]). Teachers are expected to estimate their students' cognitive ability as well as a wide range of non-cognitive characteristics, such as for instance academic self-concept or intrinsic motivation, at least implicitly on a day-to-day basis (Machts et al., [<reflink idref="bib38" id="ref35">38</reflink>]). Such daily estimations and initial impressions to a large extend contribute to the activities performed in the classroom as well as the teacher-student interaction (Machts et al., [<reflink idref="bib38" id="ref36">38</reflink>]). (Initial) teacher judgments can then inform teacher expectations that based on their stability can inform further judgments and evaluations and lead to judgmental biases, such as for instance the gender bias (Bergold & Steinmayr, [<reflink idref="bib7" id="ref37">7</reflink>]; Leaper & Starr, [<reflink idref="bib35" id="ref38">35</reflink>]).</p> <p>Research in social and personality psychology suggests that a thin-slices of behavior and zero-acquaintance (Ambady & Rosenthal, [<reflink idref="bib4" id="ref39">4</reflink>]; Ambady et al., [<reflink idref="bib2" id="ref40">2</reflink>]) approach could be an important extension of traditional accuracy research in the context of education. Studies in which perceivers do not know the targets (zero-acquaintance) and observation is restricted to short behavioral episodes (thin-slice of behavior) could be a promising tool to study teachers' first impressions of students' personality characteristics which are relevant to learning (Bhowmik et al., [<reflink idref="bib9" id="ref41">9</reflink>]). This stems from the possibility to investigate judgments in educational settings, such as teacher judgments, while keeping other influencing factors, including previous information about students' social background or individual situation and behavior, constant. Applying the thin-slice and/or zero-acquaintance approach to study teacher judgment accuracy thereby represents a shift from knowledge- and experience-based teacher judgments to initial (i.e., first) impressions of teachers, which represent the basis for subsequent teacher expectations and judgments (Ambady et al., [<reflink idref="bib1" id="ref42">1</reflink>]). In addition, utilizing brief videos of the students results in the possibility to inspect students' detailed behavior and appearance that helps drawing a comprehensive picture about the information perceivers utilize for their judgments (and in contrast to this, the information they "should have used").</p> <p>Previous research in social and personality psychology using a zero-acquaintance or thin-slices-of-behavior approach has shown that personality characteristics, such as the Big Five, and other characteristics, such as intelligence, are usually perceived with substantial accuracy (Ambady & Rosenthal, [<reflink idref="bib4" id="ref43">4</reflink>]; Borkenau et al, [<reflink idref="bib11" id="ref44">11</reflink>]; Murphy, [<reflink idref="bib42" id="ref45">42</reflink>]; Murphy et al., [<reflink idref="bib43" id="ref46">43</reflink>]; Reynolds & Gifford, [<reflink idref="bib55" id="ref47">55</reflink>]). In this research, judgments are mainly based on indicators related to the physical appearance and general behavioral pattern of a target, e.g., gestures. The application of brief videos as stimulus material for the perceivers is based on a large amount of research showing that even very short glimpses of expressive behavior can already provide valid information about social variables, such as personality or emotion (Ambady & Rosenthal, [<reflink idref="bib4" id="ref48">4</reflink>]; Murphy et al., [<reflink idref="bib45" id="ref49">45</reflink>], [<reflink idref="bib44" id="ref50">44</reflink>]).</p> <p>So far, only few studies addressed judgments based on minimal information and first impression accuracy in an educational setting. A pioneering study was conducted by Ambady and Rosenthal ([<reflink idref="bib3" id="ref51">3</reflink>]), who demonstrated that teacher evaluations at the end of a school term could accurately be predicted from video sequences that lasted only 30 s. In a more recent study by Praetorius et al. ([<reflink idref="bib50" id="ref52">50</reflink>]), teacher judgment accuracy regarding students' academic self-concept was investigated based on 30 s videos of students and no prior acquaintance between teachers and students. The average accuracy values in the four zero-acquaintance samples ranged between <emph>r</emph> = 0.31 and <emph>r</emph> = 0.39, which corresponds roughly to the average accuracy achieved in a natural classroom sample (<emph>r</emph> = 0.29, <emph>SD</emph> = 0.34). This result shows that teachers were indeed able to judge the rank order of unacquainted students to some degree and, remarkably, knowing a student well did not increase teachers' accuracy outcomes. In a study by Lansu and Berg ([<reflink idref="bib34" id="ref53">34</reflink>]), different groups of perceivers (teachers, students, and young adults) judged students' likeability, popularity, prosocial behaviour, aggression, and level of exclusion based on brief 20-s-long videos. The researchers found better than chance accuracy outcomes based on thin-slices of behavior for students' popularity (<emph>r</emph> = 0.16) and prosocial behavior (<emph>r</emph> = 0.21), but not for students' aggression (<emph>r</emph> = − 0.14) and level of exclusion (<emph>r</emph> = − 0.09). Similar to the findings by Praetorius et al. ([<reflink idref="bib50" id="ref54">50</reflink>]), familiarity with the social context of the targets did not benefit judgment accuracy. In another recent zero-acquaintance study (Bhowmik et al., [<reflink idref="bib9" id="ref55">9</reflink>]) small to moderate rank order accuracies were obtained for the broad academic self-concept, domain-specific self-concept and intrinsic motivation in physics, and intelligence.</p> <hd id="AN0184915346-4">Judgment accuracy within the BLM framework</hd> <p>Previous studies on teacher judgment accuracy did only rarely examine the information judgements are based on. Thus, it remains an open question how teachers obtain a more or less accurate judgment. Brunswik's ([<reflink idref="bib13" id="ref56">13</reflink>]) Lens Model (BLM) offers a theoretical and methodological framework to identify cues teachers rely on when they judge their students. It is a conceptual model that has been extensively employed to study the processes that are involved when individuals form their accurate or inaccurate impressions of others (see Fig. 1 for a graphical illustration of the model and Back & Nestler, [<reflink idref="bib5" id="ref57">5</reflink>]; Nestler & Back, [<reflink idref="bib47" id="ref58">47</reflink>]). The basic idea of the lens model is that in order to infer other individuals' personality dispositions or inner states, such as their motivation or emotion, which are not directly observable, a perceiver has to use observable information (cues; see the middle part of Fig. 1) that is related to these dispositions or inner states (i.e., the criterion to judge). Whether or not certain cues are related to the criterion, that is, the actual value of the targets' characteristics, such as for instance students' self-reported motivation, is referred to as <emph>cue validity.</emph> The extent to which a given set of cues and hence the amount of visible information is generally able to predict the targets' criterion values is called <emph>predictability.</emph> The strength with which perceivers utilize a certain cue for their judgment is referred to as <emph>cue utilization</emph> and the extent to which a perceiver applies the same judgment strategy consistently, i.e., utilizes the same set of cues with equal strength across all targets, is called <emph>response consistency</emph>. Finally, the extent to which a perceiver utilizes valid cues is called <emph>cue sensitivity</emph> or <emph>matching</emph>, the parameter of a lens model that describes how well a perceiver's judgment model fits with the model of the criterion. The basic tenet of the BLM then is that a perceiver is more accurate, the more valid cues exist in the judgment context (predictability), the more consistently cues are utilized (response consistency), and the more sensitive she or he is towards differences in the validity of individual cues (i.e., the more valid cues she or he uses; cue sensitivity) (Nestler & Back, [<reflink idref="bib47" id="ref59">47</reflink>]).</p> <p>Graph: Fig. 1 Diagram of a lens model to describe personality judgments. Note. The figure was adapted from "Applications and Extensions of the Lens Model to Understand Interpersonal Judgments at Zero Acquaintance" by S. Nestler und M. D. Back, 2013, Current Directions in Psychological Science, 22(<reflink idref="bib5" id="ref60">5</reflink>), p. 375</p> <p>Previous research applying BLM has shown that cues extracted from thin-slices of behavior do not only predict judgments but are also related to the actual target characteristics (Back et al., [<reflink idref="bib6" id="ref61">6</reflink>]; see Breil et al., [<reflink idref="bib12" id="ref62">12</reflink>] for an overview; Karelaia & Hogarth, [<reflink idref="bib30" id="ref63">30</reflink>]; Reynolds & Gifford, [<reflink idref="bib55" id="ref64">55</reflink>]). However, so far only few studies have applied the lens model in teacher judgment accuracy research (Förster & Böhmer, [<reflink idref="bib19" id="ref65">19</reflink>]). In an early study, Cooksey et al. ([<reflink idref="bib15" id="ref66">15</reflink>]) investigated cues that teachers utilize when judging reading comprehension of kindergarten children. Whereas teacher students arrived at accurate judgments on average, they showed differences in the validity of the information they used in the judgment process. In a more recent study, Marksteiner et al. ([<reflink idref="bib39" id="ref67">39</reflink>]) explored cues that pre-service teachers use to identify cheating, operationalized via true vs. invented stories. Results show that the cues reported by the pre-service teachers predicted their judgments, but were not predictive of the objective cheating status (Marksteiner et al., [<reflink idref="bib39" id="ref68">39</reflink>]). In a study by Schnitzler et al. ([<reflink idref="bib57" id="ref69">57</reflink>]) pre-service teachers rated student profiles (i.e., strong, struggling, overestimating, underestimating, uninterested) using 11-min-long videos of a classroom situation. Results showed that judgment accuracy was related to the utilization of specific combinations of cues. However, information regarding cue utilization was based on pre-service teachers' self-reports. Thus, these results only show which cues people <emph>think</emph> they used, but not which cues they actually used. To answer this question, one has to assess all cues that may be relevant in a way that is independent from the perceivers.</p> <hd id="AN0184915346-5">The present research</hd> <p>In this study, we explore nonverbal behavioral cues (e.g., gestures, facial expressions) and physical cues (e.g., clothing style, eyeglasses) perceivers use when judging unknown students' intelligence, self-concept, and motivation after a short period of observation. These characteristics were chosen, because they are particularly important for teacher-student interactions as well as students' academic outcomes (Kriegbaum et al., [<reflink idref="bib33" id="ref70">33</reflink>]; Machts et al., [<reflink idref="bib38" id="ref71">38</reflink>]; Möller et al., [<reflink idref="bib46" id="ref72">46</reflink>]).</p> <p>Perceivers' (teacher and psychology students in the role of teachers) judged students shown in brief video clips with respect to the aforementioned characteristics. To find out cues describing students' appearance and behavior, a coding manual from earlier research (Back et al., [<reflink idref="bib6" id="ref73">6</reflink>]; Nestler et al., [<reflink idref="bib48" id="ref74">48</reflink>]) was used and adapted to cover all relevant cue domains. To obtain cue values, the videos were rated independently by two trained raters. In this study, only appearance and nonverbal cues were used but no verbal cues as in a first step the aim was to find out if basic visual information is sufficient to predict judgments and criteria.</p> <p>In order to provide an opportunity to observe a great amount of highly visible cues, we chose a setup in which a student (target) works independently on a physics experiment. Doing experiments results in much more observable behaviors than seatwork or group discussions. Given that the experimental situation was identical for each target, we ensured a constant research setting in which variability was mostly due to differences between the targets themselves.</p> <p>By applying a zero-acquaintance and thin-slice of behavior approach, we aimed at capturing some central features of first impression formation that translate to actual situations in which teachers are confronted with previously unknown students for the first time. Investigating teacher judgments with no prior acquaintance between teachers and students bears the potential to disentangle the judgment process per se, independent of previous teacher-student interactions and knowledge or information about the students.</p> <p>To examine accuracy and its components, we followed the BLM logic with the aim to investigate the following questions:</p> <p>(<reflink idref="bib1" id="ref75">1</reflink>) How accurate are perceivers' judgments of students' academic self-concept, intrinsic motivation and intelligence based on brief videos and no prior acquaintance? (<reflink idref="bib2" id="ref76">2</reflink>) Does the environment contain information that is relevant to the judgment task (cue validity and predictability)? Does the validity of the individual cues differ across the four investigated characteristics? (<reflink idref="bib3" id="ref77">3</reflink>) Does the perceiver utilize the given information in his or her judgment and does he or she use this particular information in the same manner across targets and situations (cue utilization and response consistency)? Which cues does the perceiver utilize and are there differences in cue utilization across the four investigated characteristics? (<reflink idref="bib4" id="ref78">4</reflink>) Is the information a perceiver utilizes for his or her judgments valid (cue sensitivity)?</p> <hd id="AN0184915346-6">Method</hd> <p></p> <hd id="AN0184915346-7">Procedure and design</hd> <p>Perceivers watched brief videos (i.e., 45 s length), showing one of 45 students (target subjects) each while working on a physics experiment task during a school visit at the university's physics laboratory. The data collection was conducted through a computer-based procedure: Each perceiver received a laptop and after a brief trial round with two videos, which were excluded from the later analyses, the actual judgment task was carried out. Perceivers were then shown the video snippets of all 45 targets and were prompted by the program after each video to provide their judgments regarding targets' intelligence, academic self-concept in physics, intrinsic motivation in physics, and the broad academic self-concept. To control for sequence effects, all videos, as well as the appearance of the criterion variables, were randomized. Moreover, all videos were muted before they were used for data collection so that audible speech could not serve as a cue in our study.</p> <p>The criterion data—both the targets' self-reports and the intelligence test—was assessed during a school visit of the research team prior to visiting the university. Moreover, demographic information of the perceivers was collected during a separate day prior to the actual judgment task.</p> <hd id="AN0184915346-8">Target subjects</hd> <p>Students (<emph>n</emph> = 45, 42% female, 14 to 17 years, <emph>M</emph><subs>age</subs> = 15.6, <emph>SD</emph><subs>age</subs> = 0.68) in tenth grade served as targets. They were part of a larger group of 10th grade students (<emph>N</emph> = 244) from ten secondary schools in Germany, who were visiting the university's physics laboratory. During their visit, the students conducted several brief experiments dealing with the process of generating energy using solar panels. Each class visiting the laboratory was divided in three sub-groups with on average 8 till 13 students. They first watched a brief introductory film and then each student received a brochure containing descriptions about the experiments and the respective tasks. Learning dyads were formed and each of the dyads received an experiment kit with all material necessary for conducting the experiments, such as cables, a module plate, a small solar panel, a transformer and a motor. The student dyads were then video recorded while independently working on the experiments. From the resulting 90-min-long video material, 45 nonverbal, brief videos (45 s each) were extracted showing one student at a time. When deciding on the length of the thin-slices, we relied on evidence from past research (Carney et al., [<reflink idref="bib14" id="ref79">14</reflink>]) showing that 45-s long videos can predict an individual's personality characteristics sufficiently well. The study in the educational context by Ambady and Rosenthal ([<reflink idref="bib3" id="ref80">3</reflink>]) showed that even shorter video slices of teachers' non-verbal behavior can lead to to highly accurate predictions of students' global evaluations at the end of a semester. Moreover, we were also taking the time and resources into account that are necessary to collect the judgments and, subsequently, to conduct the behavior coding. When selecting the video snippets for the study, we aimed at ensuring that differences between targets in the criterion variables are sufficiently large. Based on students' self-report data that was collected before the school visits at the university and the results of the assigned intelligence test, targets in the final stimulus material displayed a high variance of intelligence, motivation and academic self-concept. During the video selection process, we also ensured that the video snippets are comparable and of the same quality. This involved a comparable share of male and female students as well as ensuring that each of the selected video snippets show a mostly frontal gazing student. We also ensured that there is no interaction with other students and/or the teacher visible in the video and that all video snippets have a comparable resolution and light. Informed consent of both the participating students and parents was obtained prior to the conduction of this study.</p> <hd id="AN0184915346-9">Perceiver subjects</hd> <p>In total, <emph>N</emph> = 102 undergraduate students (72% female, 18 to 32 years, <emph>M</emph><subs>age</subs> = 23.05, <emph>SD</emph><subs>age</subs> = 2.63) enrolled in the teacher (<emph>n</emph> = 78; 68% female, 18 to 31 years, <emph>M</emph><subs>age</subs> = 23.29, <emph>SD</emph><subs>age</subs> = 2.49) and psychology (<emph>n</emph> = 24; 83% female, 19 to 32 years, <emph>M</emph><subs>age</subs> = 22.25, <emph>SD</emph><subs>age</subs> = 2.94) study programs at the University of Kaiserslautern-Landau, Germany, served as perceivers in our study. They received course credit (psychology students) or a gift voucher (pre-service teachers) in exchange for their participation. All perceivers were previously unacquainted to the students.</p> <hd id="AN0184915346-10">Criterion measures</hd> <p>Targets' <emph>general academic self-concept</emph> was assessed by using the <emph>DISK-Gitter</emph> (Rost et al., [<reflink idref="bib56" id="ref81">56</reflink>]) containing seven items (e.g., "I have a good feeling regarding my performance at school") with categories ranging from 1 (<emph>do not agree</emph>) to 6 (<emph>fully agree</emph>). Targets' <emph>academic self-concept in physics</emph> was measured using a three-item scale (e.g., "I am talented for physics"; Seidel et al., [<reflink idref="bib58" id="ref82">58</reflink>]). Answering categories ranged from 1 (<emph>do not agree</emph>) to 4 (<emph>fully agree</emph>). Targets' <emph>intrinsic motivation</emph> for physics was measured using the <emph>PISA 2006</emph> (Frey et al., [<reflink idref="bib20" id="ref83">20</reflink>]) <emph>scale</emph> (e.g. "I enjoy attending physics lessons in school") comprising three items. Answering categories ranged from 1 (<emph>never</emph>) to 4 (<emph>almost always</emph>). <emph>Intelligence</emph> as it is applied in this study refers to the ability to infer relations and regularities and was measured by the <emph>IST-Screening</emph> (Liepmann et al., [<reflink idref="bib37" id="ref84">37</reflink>]). This test consisted of three subtests: word analogies, numerical series, and matrices. Raw scores were transformed into IQ-Scores based on the test manual of the IST-Screening. Descriptive statistics, including the reliability of the criterion measures can be extracted from Table 2.</p> <hd id="AN0184915346-11">Perceiver judgments</hd> <p>For the perceiver judgments, we selected the most suitable items from the original scales based on internal consistency measures. Reducing the number of items from the self-report scales for the perceiver judgments was necessary due to our interest in several student characteristics at a time and to prevent participant fatigue. This procedure is moreover based on previous teacher judgment accuracy research (e.g. Praetorius et al., [<reflink idref="bib51" id="ref85">51</reflink>], [<reflink idref="bib50" id="ref86">50</reflink>]; Spinath, [<reflink idref="bib60" id="ref87">60</reflink>]; Urhahne et al., [<reflink idref="bib64" id="ref88">64</reflink>]). Targets' general academic self-concept and the academic self-concept in physics were each rated based on three items from the original scales. For the general self-concept (The student "...has a good feeling about his/her engagement in school", "...thinks that it is easy to obtain good grades in school", and "...thinks that he or she knows the answers to questions faster than the others") the rating categories ranged from 1 (<emph>do not agree</emph>) to 6 (<emph>fully agree</emph>). For the ratings of targets' academic self-concept in physics, perceivers received the following introductory sentence according to the original scale (Seidel et al., [<reflink idref="bib58" id="ref89">58</reflink>]): "The students were asked to evaluate the physics lessons of the past school year, how do you assume this student has evaluated his or her ability in physics"? (The student thinks "...that he or she is gifted for physics", "...that physics is an easy subject", and "...that he or she has performed well in physics"). Here, answering categories ranged from 1 (<emph>do not agree</emph>) to 4 (<emph>fully agree</emph>). Targets' intrinsic motivation for physics was rated based on two items from the three-item scale that was applied in assessing targets' self-reports (The student "...enjoys attending physics lessons in school" and "...connects positive feelings with attending physics lessons in school") with a 4—point scale ranging from 1 (<emph>fully disagree</emph>) to 4 (<emph>fully agree</emph>). In line with previous research investigating teacher judgments of students' intelligence (e.g., Spinath, [<reflink idref="bib60" id="ref90">60</reflink>]), targets' intelligence was rated using a 1-item scale ("In your opinion, how intelligent is this student?"). To compare perceiver ratings of targets' intelligence with students' actual IQ values that were calculated from the test results based on the manual of the IST-Screening, the actual IQ values were rescaled in the statistical software program R from a continuous variable to a Likert scale with categories ranging from 1 (not intelligent) to 6 (very intelligent). Descriptive statistics regarding the perceiver judgments can be extracted from Table 2.</p> <hd id="AN0184915346-12">Cue measures</hd> <p>The selection of cues was based on evidence from earlier research applying the BLM (Back et al., [<reflink idref="bib6" id="ref91">6</reflink>]; Breil et al., [<reflink idref="bib12" id="ref92">12</reflink>]; Nestler et al., [<reflink idref="bib48" id="ref93">48</reflink>]; Stopfer et al., [<reflink idref="bib61" id="ref94">61</reflink>]). Following this, a coding manual was developed with the aim to cover all possibly relevant cue areas. The manual therefore included numerous static and dynamic cues to depict as much information from the brief videos as possible. Essentially, following BLM standards, we intended to extract everything visible that could be potentially relevant to predict either the actual student characteristic or the judgments of the perceiver. While static cues described the physical appearance of a student, such as body size, hair color or whether he or she was wearing eyeglasses, the dynamic cue section covered various visible aspects of students' expressive behavior. This section was moreover divided into two sub categories, i.e., students' facial expression (i.e., smiling or attentive facial expression) and gestures (i.e., tensed or expressive gestures). Both facial expression and gesture cues were defined orienting on essential psychological dimensions: expressivity vs. introversion, negative affection, aggressivity and arrogance, agreeableness and warmth, dominance and self-assuredness, and motivation and attentiveness. For instance, the cue <emph>attentive facial expression</emph> was coded as part of the psychological dimension "motivation and attentiveness", whereas in the gestures sub-category, the cue <emph>tensed gestures</emph> was coded as a part of the psychological dimension "negative affection". The dynamic cues were rated based on 6-point Likert scales and the static cues were coded based on metric scales, except for the cue <emph>eye glasses</emph> and students' <emph>sex</emph>, which were dichotomous cues. Students' sex was coded with 0 for boys and 1 for girls. In some occasions, such as for instance for the cue <emph>smiling</emph>, despite the rating of the intensity on a 6-point Likert scale, we moreover included the number as well as the warmth and the duration of the smiles observable in the brief videos. Moreover, some cues were recoded before aggregation, such as for instance the cue describing whether or not a student was wearing make-up. In this case, the recoded version of the cue correlated highly with students' <emph>masculinity</emph>.</p> <p>Based on the resulting coding manual, the video material (<emph>N</emph> = 45 videos/targets) was coded by two trained and independent raters resulting in a targets (rows) x cues (columns) data matrix. The raters were familiarized with the coding manual prior to the actual rating process and were asked to code two trial video snippets, both of which were not included in the later analyses. Given that each of the two raters coded all of the 45 videos, Cronbach's Alpha could be calculated as indicator for the inter-rater reliability, which can overall be considered high with α = 0.84 for nonverbal (i.e., dynamic) and α = 0.89 for physical (i.e., static) cues. All cues were then <emph>z</emph>-standardized within targets' sex to ensure that mean differences within the cues between the boys and girls in the sample are excluded. As many variables (cues) were statistically related within as well as across dimensions and to be able to run the lens model analyses accordingly, we attempted to reduce the complexity and number of available cues by combining some of them into larger cue aggregates based on theoretical considerations (see Breil et al., [<reflink idref="bib12" id="ref95">12</reflink>]; Nestler et al., [<reflink idref="bib48" id="ref96">48</reflink>]; Stopfer et al., [<reflink idref="bib61" id="ref97">61</reflink>] for similar approaches). For instance, the static cue <emph>masculinity</emph> integrates different other cues, such as shortness of hair, absence of make-up as well as a masculine clothing style. The same applies to <emph>expressive gestures,</emph> which is an aggregate of different cues that were related to this category describing that the student displayed vivid, fast and active, in parts also insulting gestures. Other cues were included as single cues which did not need aggregation, such as for instance an <emph>attentive facial expression</emph> (dynamic cue) or students' body size (static cue). The calculations resulted in 17 final cues, of which 7 are cue aggregates (see Table 1 for an overview of the cues and resulting aggregates). The intercorrelations between the final cues are displayed in the Appendix.</p> <p>Table 1 Final cue categories and internal consistencies (α) of cue aggregates</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Cue category (<italic>α</italic>)</p></th><th align="left"><p>Cues before aggregation and single cue measures</p></th><th align="left"><p>Cue category (<italic>α</italic>)</p></th><th align="left"><p>Cues before aggregation and single cue measures</p></th></tr><tr><th align="left" colspan="2"><p>Static cues</p></th><th align="left" colspan="2"><p>Dynamic cues</p></th></tr></thead><tbody><tr><td align="left" rowspan="2"><p>Attractiveness (0.88)</p></td><td align="left"><p>Hair: tidy, tied together, attractive, straight</p><p>Body: slim, tidy</p></td><td align="left"><p>Facial expression</p></td><td align="left" /></tr><tr><td align="left"><p>Face: attractive, clear</p><p>Clothing: tidy, attractive, modern</p></td><td align="left"><p>Friendly (0.96)</p></td><td align="left"><p>Number, duration, warmth and intensity of smiling</p><p>Friendly facial expression</p></td></tr><tr><td align="left"><p>Masculinity (0.86)</p></td><td align="left"><p>Masculine clothes, hair, face, body, absence of make-up, shortness of hair</p></td><td align="left"><p>Self-assured, non-shy (0.65)</p></td><td align="left"><p>Non-shy gaze</p><p>Self-assured facial expression</p></td></tr><tr><td align="left"><p>Dark appearance (0.65)</p></td><td align="left"><p>Dark face</p><p>Dark hair</p></td><td align="left"><p>Communicative</p></td><td align="left"><p>Single cue (gaze to interaction partner)</p></td></tr><tr><td align="left"><p>Distinctive clothing style (0.72)</p></td><td align="left"><p>Distinctive clothing</p><p>Colourful clothing</p></td><td align="left"><p>Attentive</p></td><td align="left"><p>Single cue</p></td></tr><tr><td align="left"><p>Distinctive hair</p></td><td align="left"><p>Single cue</p></td><td align="left"><p>Gestures</p></td><td align="left" /></tr><tr><td align="left"><p>Distinctive face</p><p>Mature face</p><p>Body size</p><p>Apparent social background</p></td><td align="left"><p>Single cue</p><p>Single cue</p><p>Single cue</p><p>Single cue</p></td><td align="left"><p>Expressive (0.95)</p></td><td align="left"><p>Fast gestures</p><p>Active movements</p><p>Expressive hand gestures</p><p>Insulting gestures</p></td></tr><tr><td align="left"><p>Eye glasses</p></td><td align="left"><p>Single cue</p></td><td align="left"><p>Tensed</p></td><td align="left"><p>Single cue</p></td></tr><tr><td align="left"><p>Sex (boys: 0, girls: 1)</p></td><td align="left"><p>Single cue</p></td><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Cronbach's Alpha is defined only for cue aggregates</p> <hd id="AN0184915346-13">Lens model analyses</hd> <p>All analytical procedures described below were conducted for each target characteristic separately. To obtain the lens model parameters, we adhered to the lens model equation[<reflink idref="bib1" id="ref98">1</reflink>] (<emph>r</emph><subs>a</subs> = <emph>r</emph><subs>m</subs><emph>R</emph><subs>e</subs><emph>R</emph><subs>s</subs>; see Karelaia & Hogarth, [<reflink idref="bib30" id="ref99">30</reflink>]; Kaufmann, [<reflink idref="bib31" id="ref100">31</reflink>]; Tucker, [<reflink idref="bib63" id="ref101">63</reflink>] for a more detailed elaboration of the lens model equation), in which the accuracy of a perceiver (<emph>r</emph><subs><emph>a</emph></subs>) regarding a student characteristic can be understood as the product of his or her response consistency (<emph>R</emph><subs>s</subs>), a predictable environment (<emph>R</emph><subs>e</subs>), and his or her sensitivity (<emph>r</emph><subs>m</subs>).[<reflink idref="bib2" id="ref102">2</reflink>] In all regression analyses, static and dynamic cues were integrated in the same model.</p> <hd id="AN0184915346-14">Judgment accuracy</hd> <p> <emph>Judgment accuracy</emph> is measured by the correlation between perceiver judgments of the targets and the targets' criterion values. <emph>Single-perceiver accuracy</emph> refers to the average correlation between a single perceiver's judgments of the different targets and the criterion values of these targets. To compute the average of single-perceiver accuracies, single-perceiver correlations were Fisher- <emph>z</emph> transformed, averaged and transformed back into correlations. <emph>Average single-perceiver accuracy</emph> refers to the average of the single perceiver accuracies. Hence, the average values of the single-perceiver accuracies characterize the mean accuracy of individual perceivers.</p> <hd id="AN0184915346-15">Cue utilization</hd> <p> <emph>Cue utilization</emph> is obtained by regressing a single perceiver's judgments of the targets as dependent variable on the 17 cue values as independent variables (i.e., single-perceiver utilization). The regression weight of a cue indicates the utilization of this specific cue for the specific perceiver. By contrast, average-perceiver cue-utilization refers to the regression model of the average perceiver judgments on the aggregated cues.</p> <p>In addition to the regression models, correlation coefficients were calculated accordingly to obtain cue utilization values for a single and an average perceiver.</p> <hd id="AN0184915346-16">Response consistency</hd> <p>A single perceiver's <emph>response consistency</emph> (<emph>R</emph><subs>s</subs>) is measured by the correlation between the actual judgments and the judgments predicted by the cues. It is a measure of how good perceivers' judgments are approximated by a linear model or decision rule. Response consistency would be perfect when a perceiver would use the same linear decision rule, i.e., equal weights for each target. Varying weights for the same cue for different targets would result in a departure from a general linear decision rule and a less-than-perfect linear prediction.</p> <hd id="AN0184915346-17">Cue validity and predictability</hd> <p> <emph>Cue validity</emph> describes the extent to which a cue predicts the criterion. To obtain cue validities<emph>,</emph> multiple regressions were computed in which the targets' criterion values of a construct were regressed on the respective cues. The resulting regression weights are indicators of the cue validities. The regression model characterizes the model of the environment for a specific criterion. <emph>Predictability</emph> (<emph>R</emph><subs>e</subs>) is the correlation between the predicted value of the model with the actual target values, indicating the degree to which the cues in total predict the criterion. In addition to the regression analysis carried out to determine cue validity, we also computed correlation-based cue validity by correlating targets' criterion values with the cue values of the 17 cues.</p> <hd id="AN0184915346-18">Cue sensitivity</hd> <p> <emph>Cue sensitivity</emph> or <emph>matching</emph> (<emph>r</emph><subs><emph>m</emph></subs>) refers to the relation between the model of the perceiver and the model of the environment. Matching scores were calculated by computing the correlation between the predicted values of the regression model in which a perceiver's judgments of a respective criterion were regressed on the cues and the predicted values of the regression model in which targets' actual values were regressed on the cues. Matching is a measure of cue sensitivity as it shows how much the utilization of cues by the perceiver correspond to their validity (i.e., how well perceivers use cues according to their validity).</p> <p>Lens model analyses were carried out with the statistical software <emph>R</emph>, version 4.0.0 (R Development Core Team, [<reflink idref="bib53" id="ref103">53</reflink>]) and the packages <emph>plyr</emph> (Wickham, [<reflink idref="bib66" id="ref104">66</reflink>])<emph>, psych</emph> (Revelle, [<reflink idref="bib54" id="ref105">54</reflink>]) and <emph>quantpsych</emph> (Fletcher, [<reflink idref="bib18" id="ref106">18</reflink>]).</p> <hd id="AN0184915346-19">Results</hd> <p>Descriptive statistics of students' criterion values and perceiver judgments are described in Table 2. Table 3 presents results obtained from the lens model parameter analyses, including single-perceiver and average-perceiver accuracy, consistency, sensitivity and predictability coefficients for all assessed constructs.</p> <p>Table 2 Means (M), Standard Deviations (SD), Minimum (Min), Maximum (Max), Internal Consistencies (α) and Intercorrelations of Targets' Characteristics (upper line) and Perceiver Judgments (lower line)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Criterion variables</p></th><th align="left"><p><italic>M</italic></p></th><th align="left"><p><italic>SD</italic></p></th><th align="left"><p><italic>Min</italic></p></th><th align="left"><p><italic>Max</italic></p></th><th align="left"><p><italic>α</italic></p></th><th align="left"><p>1</p></th><th align="left"><p>2</p></th><th align="left"><p>3</p></th></tr></thead><tbody><tr><td align="left" colspan="9"><p>1. Academic self-concept</p></td></tr><tr><td align="left"><p> Targets characteristics</p></td><td char="." align="char"><p>4.00</p></td><td char="." align="char"><p>0.82</p></td><td char="." align="char"><p>2.50</p></td><td char="." align="char"><p>5.75</p></td><td align="left"><p>0.80</p></td><td align="left"><p>–</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Perceiver judgments</p></td><td char="." align="char"><p>3.34</p></td><td char="." align="char"><p>0.37</p></td><td char="." align="char"><p>2.65</p></td><td char="." align="char"><p>4.34</p></td><td align="left"><p>–</p></td><td align="left"><p>–</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" colspan="9"><p>2. Academic self-concept in physics</p></td></tr><tr><td align="left"><p> Targets characteristics</p></td><td char="." align="char"><p>2.68</p></td><td char="." align="char"><p>0.77</p></td><td char="." align="char"><p>1.00</p></td><td char="." align="char"><p>4.00</p></td><td align="left"><p>0.85</p></td><td align="left"><p><bold>0.64</bold></p></td><td align="left"><p>–</p></td><td align="left" /></tr><tr><td align="left"><p> Perceiver judgments</p></td><td char="." align="char"><p>2.47</p></td><td char="." align="char"><p>0.19</p></td><td char="." align="char"><p>1.99</p></td><td char="." align="char"><p>2.99</p></td><td align="left"><p>–</p></td><td align="left"><p><bold>0.59</bold></p></td><td align="left"><p>–</p></td><td align="left" /></tr><tr><td align="left" colspan="9"><p>3. Intrinsic motivation physics</p></td></tr><tr><td align="left"><p> Targets characteristics</p></td><td char="." align="char"><p>2.76</p></td><td char="." align="char"><p>0.87</p></td><td char="." align="char"><p>1.33</p></td><td char="." align="char"><p>4.00</p></td><td align="left"><p>0.89</p></td><td align="left"><p><bold>0.52</bold></p></td><td align="left"><p><bold>0.75</bold></p></td><td align="left"><p>–</p></td></tr><tr><td align="left"><p> Perceiver judgments</p></td><td char="." align="char"><p>2.41</p></td><td char="." align="char"><p>0.28</p></td><td char="." align="char"><p>1.78</p></td><td char="." align="char"><p>5.16</p></td><td align="left"><p>–</p></td><td align="left"><p><bold>0.49</bold></p></td><td align="left"><p><bold>0.72</bold></p></td><td align="left"><p>–</p></td></tr><tr><td align="left" colspan="9"><p>4. Intelligence</p></td></tr><tr><td align="left"><p> Targets characteristics</p></td><td char="." align="char"><p>108.94</p></td><td char="." align="char"><p>12.77</p></td><td char="." align="char"><p>82.00</p></td><td char="." align="char"><p>134.50</p></td><td align="left"><p>–</p></td><td align="left"><p><bold>0.15</bold></p></td><td align="left"><p><bold>0.28</bold></p></td><td align="left"><p><bold>0.10</bold></p></td></tr><tr><td align="left"><p> Perceiver judgments</p></td><td char="." align="char"><p>3.89</p></td><td char="." align="char"><p>0.45</p></td><td char="." align="char"><p>2.93</p></td><td char="." align="char"><p>5.16</p></td><td align="left"><p>–</p></td><td align="left"><p><bold>0.42</bold></p></td><td align="left"><p><bold>0.38</bold></p></td><td align="left"><p><bold>0.29</bold></p></td></tr></tbody></table> </ephtml> </p> <p> <emph>N</emph> <subs>Target =</subs> 45; <emph>N</emph><subs>Perceiver</subs> = 102. Internal consistencies (α) refer to the complete student sample (<emph>N</emph> = 244). Intelligence: IQ-scale and reliabilities (Cronbach's α, split half, test–retest between.72 and.90) based on test validation studies for the IST-Screening. Bold correlations p ≤.05 (t-test, two-tailed)</p> <p>Table 3 Single-Perceiver Accuracy (SAcc), Average-Perceiver Accuracy (AAcc), Cue Sensitivity (CS), Response Consistency (RC) and Predictability (PR)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Criterion variables</p></th><th align="left"><p>SAcc</p></th><th align="left"><p>AAcc</p></th><th align="left"><p>Variance<sub>SAcc</sub></p></th><th align="left"><p>RC</p></th><th align="left"><p>Variance<sub>RC</sub></p></th><th align="left"><p>CS</p></th><th align="left"><p>Variance<sub>CS</sub></p></th><th align="left"><p>PR <italic>(R</italic><sup><italic>2</italic></sup>)</p></th></tr></thead><tbody><tr><td align="left"><p>1. Self-concept</p></td><td char="." align="char"><p><bold>0.05</bold></p></td><td char="." align="char"><p><bold>0.10</bold></p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p><bold>0.69</bold></p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>0.20</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.48</p></td></tr><tr><td align="left"><p>2. Self-concept physics</p></td><td char="." align="char"><p><bold>0.13</bold></p></td><td char="." align="char"><p><bold>0.24</bold></p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p><bold>0.72</bold></p></td><td char="." align="char"><p>0.03</p></td><td char="." align="char"><p>0.26</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p>0.49</p></td></tr><tr><td align="left"><p>3. Intrinsic motivation</p></td><td char="." align="char"><p><bold>0.23</bold></p></td><td char="." align="char"><p><bold>0.43</bold></p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p><bold>0.72</bold></p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p><bold>0.37</bold></p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p><bold>0.61</bold></p></td></tr><tr><td align="left"><p>4. Intelligence</p></td><td char="." align="char"><p><bold>− 0.03</bold></p></td><td char="." align="char"><p><bold>0.07</bold></p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p><bold>0.70</bold></p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p><bold>− 0.09</bold></p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.29</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>N</emph> <subs> <emph>Perceiver</emph> </subs> = 102; <emph>N</emph><subs><emph>Target</emph></subs> = 45. SAcc were Fisher-<emph>z</emph> transformed, averaged and transformed back into correlations. Bold correlations <emph>p</emph> ≤.05 (t-test; two-tailed)</p> <hd id="AN0184915346-20">Judgment accuracy</hd> <p>How accurately did perceivers judge targets' general and domain-specific academic self-concept, intrinsic motivation, and intelligence based on brief, nonverbal video clips? Mean accuracies were significant for all four students' characteristics, although mean accuracy scores were only low to medium.[<reflink idref="bib3" id="ref107">3</reflink>] On average, single perceivers were not successful at judging students' general academic self-concept (<emph>r</emph> = 0.05) and intelligence (<emph>r</emph> = − 0.03), but could to some extent judge students' academic self-concept in physics (<emph>r</emph> = 0.13) and intrinsic motivation in physics (<emph>r</emph> = 0.23) accurately. In contrast to individual perceivers (SAcc), accuracies of the average perceiver (AAcc) were higher, ranging from <emph>r</emph> = 0.10 for intelligence to <emph>r</emph> = 0.43 for intrinsic motivation in physics.</p> <hd id="AN0184915346-21">Predictability, response consistency, and cue sensitivity</hd> <p>The degree to which the employed set of cues was suitable to predict targets' characteristics differed widely across the four selected judgment criteria. Hence, <emph>predictability</emph> (<emph>R</emph><subs><emph>e</emph></subs>) was most evident for students' intrinsic motivation in physics (<emph>R</emph><sups>2</sups> = 0.61), but for students' intelligence predictability was rather low (<emph>R</emph><sups>2</sups> = 0.29). For students' self-concept variables, predictabilities were nearly identical for the academic self-concept in physics (<emph>R</emph><sups>2</sups> = 0.49) and the general academic self-concept (<emph>R</emph><sups>2</sups> = 0.48).</p> <p> <emph>Response consistency (R</emph> <subs>s</subs>) was high across all assessed constructs, ranging from <emph>r</emph> = 0.69 to <emph>r</emph> = 0.72, that is, perceivers applied their cue knowledge consistently and utilized the cues in a comparable manner across targets to judge targets' characteristics. In other words, perceivers used more or less the same linear decision rule for a specific criterion for each target, essentially giving the same weight to the same cue across all targets.</p> <p>Perceivers' <emph>cue sensitivity</emph> values (<emph>r</emph><subs><emph>m</emph></subs>) for the four constructs ranged from <emph>r</emph> = − 0.09 for intelligence to <emph>r</emph> = 0.37 for intrinsic motivation. This indicates that perceivers did not use valid cues when judging intelligence, but to some extent they used valid information in the case of intrinsic motivation and the academic self-concept in physics. In accordance with the fact that the highest accuracy outcomes were obtained for intrinsic motivation in physics, cue sensitivity scores were also highest for this construct, followed by the academic self-concept in physics.</p> <p>Predictability, response consistency, and cue sensitivity are summary measures of the lens model that are all based on the availability and utilization of cues. In the following, we present results for cue utilization and cue validity that provide a more detailed insight. The results are based on multiple regressions with the cues as independent variables and either judgment or criterion of each of the four constructs as dependent variables. The regression analyses included both static and dynamic cues.</p> <hd id="AN0184915346-22">Cue utilization</hd> <p>Cue utilization and cue validity scores for each construct are displayed in Table 4.</p> <p>Table 4 Cue Validity (CV and CV<subs>Corr</subs>), Single-Perceiver (CU), and Average-Perceiver (CU<subs>Corr</subs>) Cue Utilizations for Students' Broad Academic Self-Concept, Academic Self-Concept Physics, Intrinsic Motivation Physics, and Intelligence</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Cue vali-dity (CV)</p></th><th align="left"><p>CV<sub>Corr</sub></p></th><th align="left"><p>Cue utili-zation CU)</p></th><th align="left"><p>CU<sub>Aver</sub></p></th><th align="left"><p>CU<sub>Corr</sub></p></th><th align="left"><p>Cue vali-dity (CV)</p></th><th align="left"><p>CV<sub>Corr</sub></p></th><th align="left"><p>Cue Utili-zation CU)</p></th><th align="left"><p>CU<sub>Aver</sub></p></th><th align="left"><p>CU<sub>Corr</sub></p></th></tr></thead><tbody><tr><td align="left"><p>Cue Measure</p></td><td align="left" colspan="5"><p>A. Broad academic self-concept</p></td><td align="left" colspan="5"><p>B. Academic Self-Concept Physics</p></td></tr><tr><td align="left"><p>Attractiveness</p></td><td char="." align="char"><p>− 0.22</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p>0.03</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.12</p></td><td char="." align="char"><p>− 0.23</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>0.03</p></td><td char="." align="char"><p>0.05</p></td></tr><tr><td align="left"><p>Masculinity</p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p>− 0.01</p></td><td char="." align="char"><p>0 0.17</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>0.05</p></td></tr><tr><td align="left"><p>Dark appearance</p></td><td char="." align="char"><p>− 0.12</p></td><td char="." align="char"><p>− 0.07</p></td><td char="." align="char"><p><bold>0.11</bold></p></td><td char="." align="char"><p><bold>0.26</bold></p></td><td char="." align="char"><p>0.13</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>− 0.16</p></td><td char="." align="char"><p><bold>0.13</bold></p></td><td char="." align="char"><p><bold>0.24</bold></p></td><td char="." align="char"><p>0.07</p></td></tr><tr><td align="left"><p>Distinctive cloth. style</p></td><td char="." align="char"><p>− 0.29</p></td><td char="." align="char"><p>− 0.32</p></td><td char="." align="char"><p><bold>0.04</bold></p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>− <bold>0.37</bold></p></td><td char="." align="char"><p>− <bold>0.35</bold></p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.02</p></td></tr><tr><td align="left"><p>Distinctive hair</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>− <bold>0.07</bold></p></td><td char="." align="char"><p>− 0.16</p></td><td char="." align="char"><p>− 0.24</p></td><td char="." align="char"><p><bold>0.36</bold></p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>− <bold>0.04</bold></p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>− 0.19</p></td></tr><tr><td align="left"><p>Distinctive face</p></td><td char="." align="char"><p>− <bold>0.45</bold></p></td><td char="." align="char"><p>− 0.29</p></td><td char="." align="char"><p>− <bold>0.07</bold></p></td><td char="." align="char"><p>− 0.17</p></td><td char="." align="char"><p>− 0.19</p></td><td char="." align="char"><p>− 0.22</p></td><td char="." align="char"><p>− 0.26</p></td><td char="." align="char"><p>− <bold>0.02</bold></p></td><td char="." align="char"><p>− 0.22</p></td><td char="." align="char"><p>− 0.19</p></td></tr><tr><td align="left"><p>Mature face</p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>− <bold>0.05</bold></p></td><td char="." align="char"><p>− 0.12</p></td><td char="." align="char"><p>− 0.17</p></td><td char="." align="char"><p>− 0.06</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p>− <bold>0.05</bold></p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>− 0.10</p></td></tr><tr><td align="left"><p>Body size</p></td><td char="." align="char"><p>0.18</p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>0.09</p></td><td char="." align="char"><p><bold>0.34</bold></p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p><bold>0.08</bold></p></td><td char="." align="char"><p>0.13</p></td><td char="." align="char"><p>0.15</p></td></tr><tr><td align="left"><p>Apparent social backg</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>0.10</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>0.18</p></td><td char="." align="char"><p>0.27</p></td><td char="." align="char"><p>0.15</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p>0.09</p></td></tr><tr><td align="left"><p>Eye glasses</p></td><td char="." align="char"><p>0.18</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p><bold>0.10</bold></p></td><td char="." align="char"><p><bold>0.25</bold></p></td><td char="." align="char"><p>0.22</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p><bold>0.12</bold></p></td><td char="." align="char"><p><bold>0.23</bold></p></td><td char="." align="char"><p>0.22</p></td></tr><tr><td align="left"><p>Sex (boys: 0, girls: 1)</p></td><td char="." align="char"><p>− <bold>0.34</bold></p></td><td char="." align="char"><p>− 0.31</p></td><td char="." align="char"><p>− <bold>0.22</bold></p></td><td char="." align="char"><p>− <bold>0.50</bold></p></td><td char="." align="char"><p>− <bold>0.50</bold></p></td><td char="." align="char"><p>− <bold>0.33</bold></p></td><td char="." align="char"><p>− <bold>0.33</bold></p></td><td char="." align="char"><p>− <bold>0.34</bold></p></td><td char="." align="char"><p>− <bold>0.64</bold></p></td><td char="." align="char"><p>− <bold>0.64</bold></p></td></tr><tr><td align="left" colspan="11"><p>Facial expression</p></td></tr><tr><td align="left"><p> Friendly</p></td><td char="." align="char"><p><bold>0.52</bold></p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>− 0.03</p></td><td char="." align="char"><p>− 0.10</p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p>− 0.03</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>− 0.13</p></td><td char="." align="char"><p>0.07</p></td></tr><tr><td align="left"><p> Self-assured, non-shy</p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p><bold>0.12</bold></p></td><td char="." align="char"><p>0.29</p></td><td char="." align="char"><p><bold>0.36</bold></p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p><bold>0.10</bold></p></td><td char="." align="char"><p><bold>0.19</bold></p></td><td char="." align="char"><p>0.29</p></td></tr><tr><td align="left"><p> Communicative</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p><bold>0.01</bold></p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>0.10</p></td><td char="." align="char"><p>− 0.07</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>0.03</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.14</p></td></tr><tr><td align="left"><p> Attentive</p></td><td char="." align="char"><p>− 0.06</p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p><bold>0.10</bold></p></td><td char="." align="char"><p><bold>0.25</bold></p></td><td char="." align="char"><p><bold>0.31</bold></p></td><td char="." align="char"><p>0.10</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p><bold>0.15</bold></p></td><td char="." align="char"><p><bold>0.29</bold></p></td><td char="." align="char"><p><bold>0.33</bold></p></td></tr><tr><td align="left" colspan="11"><p>Gestures</p></td></tr><tr><td align="left"><p> Expressive</p></td><td char="." align="char"><p>− 0.14</p></td><td char="." align="char"><p>0.03</p></td><td char="." align="char"><p><bold>0.06</bold></p></td><td char="." align="char"><p>0.16</p></td><td char="." align="char"><p><bold>0.30</bold></p></td><td char="." align="char"><p>− 0.16</p></td><td char="." align="char"><p>− 0.10</p></td><td char="." align="char"><p><bold>0.07</bold></p></td><td char="." align="char"><p>0.15</p></td><td char="." align="char"><p><bold>0.29</bold></p></td></tr><tr><td align="left"><p> Tensed</p></td><td char="." align="char"><p><bold>0.42</bold></p></td><td char="." align="char"><p>0.23</p></td><td char="." align="char"><p><bold>0.05</bold></p></td><td char="." align="char"><p>0.12</p></td><td char="." align="char"><p>− 0.07</p></td><td char="." align="char"><p><bold>0.27</bold></p></td><td char="." align="char"><p>0.24</p></td><td char="." align="char"><p><bold>0.04</bold></p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>− 0.08</p></td></tr><tr><td align="left" /><td align="left" colspan="5"><p>C. Intrinsic Motivation Physics</p></td><td align="left" colspan="5"><p>D. Intelligence</p></td></tr><tr><td align="left"><p>Attractiveness</p></td><td char="." align="char"><p>− 0.19</p></td><td char="." align="char"><p>− 0.13</p></td><td char="." align="char"><p>− 0.01</p></td><td char="." align="char"><p>− 0.01</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>− 0.38</p></td><td char="." align="char"><p>− 0.19</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>− 0.03</p></td><td char="." align="char"><p>0.09</p></td></tr><tr><td align="left"><p>Masculinity</p></td><td char="." align="char"><p><bold>0.32</bold></p></td><td char="." align="char"><p>0.25</p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>0.03</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p>0.32</p></td><td char="." align="char"><p><bold>0.31</bold></p></td><td char="." align="char"><p>− <bold>0.05</bold></p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>− 0.10</p></td></tr><tr><td align="left"><p>Dark appearance</p></td><td char="." align="char"><p>− 0.16</p></td><td char="." align="char"><p>− <bold>0.29</bold></p></td><td char="." align="char"><p><bold>0.11</bold></p></td><td char="." align="char"><p>0.19</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>− 0.03</p></td><td char="." align="char"><p>− 0.13</p></td><td char="." align="char"><p><bold>0.17</bold></p></td><td char="." align="char"><p><bold>0.38</bold></p></td><td char="." align="char"><p>0.16</p></td></tr><tr><td align="left"><p>Distinctive cloth. style</p></td><td char="." align="char"><p>− 0.22</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p><bold>0.05</bold></p></td><td char="." align="char"><p>0.09</p></td><td char="." align="char"><p>0.09</p></td><td char="." align="char"><p>− 0.25</p></td><td char="." align="char"><p>− 0.19</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>− 0.01</p></td><td char="." align="char"><p>− 0.08</p></td></tr><tr><td align="left"><p>Distinctive hair</p></td><td char="." align="char"><p><bold>0.29</bold></p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>− <bold>0.06</bold></p></td><td char="." align="char"><p>− 0.12</p></td><td char="." align="char"><p>− 0.19</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>− 0.03</p></td><td char="." align="char"><p>− 0.19</p></td></tr><tr><td align="left"><p>Distinctive face</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>− <bold>0.08</bold></p></td><td char="." align="char"><p>− 0.15</p></td><td char="." align="char"><p>− 0.10</p></td><td char="." align="char"><p>0.18</p></td><td char="." align="char"><p>− 0.08</p></td><td char="." align="char"><p>− <bold>0.15</bold></p></td><td char="." align="char"><p>− <bold>0.34</bold></p></td><td char="." align="char"><p>− <bold>0.36</bold></p></td></tr><tr><td align="left"><p>Mature face</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>0.12</p></td><td char="." align="char"><p>− <bold>0.06</bold></p></td><td char="." align="char"><p>− 0.11</p></td><td char="." align="char"><p>− 0.13</p></td><td char="." align="char"><p>− 0.12</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>− <bold>0.12</bold></p></td><td char="." align="char"><p>− <bold>0.29</bold></p></td><td char="." align="char"><p>− <bold>0.29</bold></p></td></tr><tr><td align="left"><p>Body size</p></td><td char="." align="char"><p><bold>0.29</bold></p></td><td char="." align="char"><p>0.27</p></td><td char="." align="char"><p><bold>0.07</bold></p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>0.19</p></td><td char="." align="char"><p>0.09</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p><bold>0.06</bold></p></td><td char="." align="char"><p>0.12</p></td><td char="." align="char"><p>− 0.02</p></td></tr><tr><td align="left"><p>Apparent social backg</p></td><td char="." align="char"><p>0.10</p></td><td char="." align="char"><p>− 0.07</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>− 0.01</p></td><td char="." align="char"><p>0.12</p></td><td char="." align="char"><p>0.20</p></td><td char="." align="char"><p>− 0.06</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>0.21</p></td></tr><tr><td align="left"><p>Eye glasses</p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p><bold>0.12</bold></p></td><td char="." align="char"><p><bold>0.22</bold></p></td><td char="." align="char"><p>0.23</p></td><td char="." align="char"><p>− 0.12</p></td><td char="." align="char"><p>− 0.06</p></td><td char="." align="char"><p><bold>0.06</bold></p></td><td char="." align="char"><p>0.16</p></td><td char="." align="char"><p>0.14</p></td></tr><tr><td align="left"><p>Sex (boys: 0, girls:1)</p></td><td char="." align="char"><p>− <bold>0.48</bold></p></td><td char="." align="char"><p>− <bold>0.31</bold></p></td><td char="." align="char"><p>− <bold>0.30</bold></p></td><td char="." align="char"><p>− <bold>0.55</bold></p></td><td char="." align="char"><p>− <bold>0.55</bold></p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>− <bold>0.18</bold></p></td><td char="." align="char"><p>− <bold>0.42</bold></p></td><td char="." align="char"><p>− <bold>0.42</bold></p></td></tr><tr><td align="left" colspan="11"><p>Facial expression</p></td></tr><tr><td align="left"><p> Friendly</p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p>− <bold>0.05</bold></p></td><td char="." align="char"><p>0.01</p></td><td char="." align="char"><p>0.22</p></td><td char="." align="char"><p>− 0.16</p></td><td char="." align="char"><p>− 0.23</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>0.07</p></td></tr><tr><td align="left"><p> Self-assured, non-shy</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p><bold>0.08</bold></p></td><td char="." align="char"><p>0.16</p></td><td char="." align="char"><p>0.28</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p><bold>0.07</bold></p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>0.26</p></td></tr><tr><td align="left"><p> Communicative</p></td><td char="." align="char"><p>− 0.03</p></td><td char="." align="char"><p>0.12</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>− 0.01</p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>− 0.20</p></td><td char="." align="char"><p>− 0.22</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p>0.12</p></td></tr><tr><td align="left"><p> Attentive</p></td><td char="." align="char"><p>0.20</p></td><td char="." align="char"><p>0.27</p></td><td char="." align="char"><p><bold>0.21</bold></p></td><td char="." align="char"><p><bold>0.39</bold></p></td><td char="." align="char"><p><bold>0.47</bold></p></td><td char="." align="char"><p>0.09</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p><bold>0.10</bold></p></td><td char="." align="char"><p><bold>0.37</bold></p></td><td char="." align="char"><p><bold>0.36</bold></p></td></tr><tr><td align="left" colspan="11"><p>Gestures</p></td></tr><tr><td align="left"><p> Expressive</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p>0.23</p></td><td char="." align="char"><p><bold>0.07</bold></p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p><bold>0.39</bold></p></td><td char="." align="char"><p>− 0.14</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p><bold>0.06</bold></p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p>0.17</p></td></tr><tr><td align="left"><p> Tensed</p></td><td char="." align="char"><p>0.25</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p><bold>0.07</bold></p></td><td char="." align="char"><p>0.13</p></td><td char="." align="char"><p>− 0.06</p></td><td char="." align="char"><p>0.00</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p><bold>0.05</bold></p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>0.03</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>N</emph> <subs> <emph>Perceiver</emph> </subs> = 102; <emph>N</emph><subs><emph>Target</emph></subs> = 45. CV Cue Validity (regression coefficients), CV<subs>Corr</subs> Correlations Cues-Criterion values, CU Single Perceiver Utilization (regression coefficients), CU<subs>Aver</subs> Average Perceiver Cue Utilizations (regression coefficients), CU<subs>corr</subs> Correlations Cues-Average Criterion values. Bold coefficients <emph>p</emph> ≤.05 (<emph>t</emph>-Test, two-tailed)</p> <hd id="AN0184915346-23">Single perceiver</hd> <p>Which cues were given particular importance when judging different target characteristics? In other words, what kind of information did perceivers rely on and how much weight did they give to different kinds of information?</p> <p>Overall, the selected set of cues significantly predicted perceivers' judgments. Hence, perceivers relied on the behavioral and physical cues in their judgments of students' general (<emph>R</emph><sups>2</sups> = 0.43) and subject-specific academic self-concept (<emph>R</emph><sups>2</sups> = 0.58), intrinsic motivation in physics (<emph>R</emph><sups>2</sups> = 0.55) and intelligence (<emph>R</emph><sups>2</sups> = 0.44).</p> <p> <emph>Static cues</emph> In the case of single perceivers, cue utilization values for the individual cues were rather low. Regarding the cues describing a student's physical appearance (i.e., static cues), the cue utilizations of the cues sex, dark appearance, distinctive face, mature face, and eyeglasses were significantly different from zero for all four examined constructs. That is, perceivers rated targets' self-concept, motivation, and intelligence higher when the targets had a dark appearance and wore eyeglasses, and rated targets with a distinct and mature appearing face lower. A cue that contributed especially strong to perceivers' judgments of all four constructs was targets' sex: Male targets were perceived as having a higher academic self-concept, a higher intrinsic motivation, and a higher intelligence.</p> <p> <emph>Dynamic cues</emph> For the case of students' behavior (i.e., dynamic cues), perceivers regarded students' attentive and self-assured, non-shy facial expression as well as expressive and tensed gestures as indicators for higher levels of all four assessed constructs. All these relations were positive and there is also no indication for a differential use of these cues across the four constructs.</p> <hd id="AN0184915346-24">Average perceiver</hd> <p>Average-perceiver cue utilizations are consistent with these results. On a descriptive level, relations for the average perceiver were stronger than the coefficients obtained from the single-perceiver regression analysis described above.</p> <p> <emph>Static cues</emph> A dark appearance, as well as wearing eyeglasses and students' sex were significant predictors across all investigates constructs, except for wearing eyeglasses in relation to students' intelligence and a dark appearance in relation to students' intrinsic motivation in physics. For the case of intelligence, a distinctive and mature face played a significant role for the perceiver judgments, in addition to an attentive facial expression.</p> <p> <emph>Dynamic cues</emph> Average-perceiver regression coefficients were particularly high and significant for students' attentive facial expressions across all constructs. For the academic self-concept in physics, a self-assured facial expression moreover significantly predicted the perceiver judgment.</p> <hd id="AN0184915346-25">Cue validity</hd> <p>Which cues were valid for students' academic self-concept, motivation and intelligence and which differences in cue validity could be identified across the four investigated student characteristics?</p> <p> <emph>Static cues</emph> Targets' sex had a rather strong association with students' academic self-concept (both general and domain specific for physics) and intrinsic motivation in physics: Girls in our sample had lower self-reported self-concept and motivation compared to the boys. Targets' sex was unrelated to intelligence. Despite controlling for targets' sex, body size (i.e., being tall), as well as masculinity were cues with a substantial validity for intrinsic motivation in physics (<emph>b</emph> ≥ 0.30). Controlling a characteristic for targets' sex means that boys and girls are only compared within their sex group irrespective of the difference in the group means. Hence, taller boys as well as taller girls and more masculine appearing boys as well as girls indicated higher intrinsic motivation levels. Body size was also predictive for the domain-specific self-concept in physics, alongside distinctive hair. Distinctive hair was referring to the hair style of a student and indicated to what extent the hair was rather styled in an unobtrusive or flamboyant manner. Beyond target's hair style, also the distinctiveness or peculiarity of a target's clothing style was a valid cue, however, it was significantly associated with a lower academic self-concept in physics.</p> <p> <emph>Dynamic cues</emph> Overall, we found only very few valid behavioral cues compared to static cues. Among the dynamic cues, a friendly facial expression, as well as tensed gestures positively predicted students' values on the general academic self-concept scale. Tensed gestures moreover predicted students' self-concept in physics.</p> <hd id="AN0184915346-26">Summary of findings</hd> <p>Taken together, differences in judgment accuracy across the four investigated student characteristics could be associated with perceivers' utilization of more or less valid cues. Such cues could contribute positively or negatively to the student characteristic.</p> <p>For students' intelligence, the construct with the lowest accuracy outcomes, there were no valid cues available. As a consequence, perceivers relied on other, non-relevant information. Positive weights were allocated to an attentive facial expression, a dark appearance, being tall, wearing eyeglasses, a self-assured facial expression and expressive and tensed gestures. Negative weights were given to sex, masculinity and a distinct and mature face. In sum, boys as well as students appearing masculine, average and juvenile within their sex group were judged as more intelligent.</p> <p>For the case of students' broad academic self-concept, which was also judged with comparatively low accuracy, some valid cues were available to the perceivers. Such cues involved students' sex and distinctive face, which negatively predicted students' self-concept, whereas students' friendly expression and tensed gestures were associated with higher broad self-concept outcomes. However, apart from sex, perceivers did not allocate equal much weight to such valid cues, which may have led to the rather low accuracy. In particular, perceivers did not rely on students' friendliness as an important cue and also did not allocate sufficient importance to tensed gestures of the students in the videos. Instead, perceivers relied on several other sources of information which were not valid, such as an attentive or self-assured facial expression or whether or not a student was wearing eyeglasses.</p> <p>For the case of the two subject related student characteristics, i.e., academic self-concept and intrinsic motivation in physics, the models of the perceivers were more closely related to the model of the environment, which also became visible in the predictability and cue sensitivity outcomes. In the case of students' academic self-concept in physics, perceivers were able to identify the importance of students' body size, sex and tensed gestures but missed out on the negative relationship of distinctive clothes and positive relationship of distinctive hair with students' academic self-concept in physics. For students' intrinsic motivation in physics, which was perceived with the highest accuracy, sex was the most valid cue with the largest negative effect size across all investigated characteristics, followed by masculinity, being tall and a distinctive hair style. Perceivers were able to identify the relationship of being male and tall with higher intrinsic motivation values in physics, but simultaneously misinterpreted other student characteristics as relevant indicators for students' intrinsic motivation in physics, in particular students' attentive facial expression, eyeglasses and a dark physical appearance.</p> <hd id="AN0184915346-27">Discussion</hd> <p>Based on BLM, we investigated the accuracy of teachers' judgments about students' self-concept, motivation, and intelligence in a zero-acquaintance situation. Student characteristics, such as the above-mentioned, are distal variables that cannot be observed directly but must be inferred by using proximal variables, that is, appearance-related or behavioral cues. In BLM, accurate judgments result if perceivers consistently utilize cues in their judgments that actually predict students' characteristics. The cues that were available in the brief videos shown to the perceivers, are characteristics of physical appearance, gestures, and facial expression, but no audible or speech information. We wanted to find out if and to what extent these cues actually predict the criteria and are utilized by the perceivers when a zero-acquaintance and thin-slices-of-behavior approach is applied. By utilizing brief videos of students that were previously unknown to the perceivers we intended to focus on the judgment process per se and independent as well as isolated of possible previous teacher-student interaction or information about the students.</p> <p>Overall, the number of valid cues was rather low in the present study, i.e., the selected cues were only partially related to students' self-report data and objective intelligence test results. Notably, we observed substantial differences in respect to cue validity and sensitivity for the four investigated constructs and students' intelligence was the characteristic that was most difficult to discern for the perceivers. In contrast, students' intrinsic motivation has been "most available" to the perceivers, that is, all lens model parameter calculations resulted in comparatively high estimates.</p> <hd id="AN0184915346-28">Judgment accuracy</hd> <p>As was demonstrated by zero-acquaintance research in personality psychology, judgments are often accurate, even in situations with minimal information (Ambady & Rosenthal, [<reflink idref="bib4" id="ref108">4</reflink>]; Ambady et al., [<reflink idref="bib2" id="ref109">2</reflink>]; Back & Nestler, [<reflink idref="bib5" id="ref110">5</reflink>]; Murphy et al., [<reflink idref="bib45" id="ref111">45</reflink>]). In the present study, however, judgment accuracy outcomes were comparatively low across all investigated constructs, with an exception for students' intrinsic motivation, which was judged with moderate accuracy. This result is comparable to accuracy outcomes of intrinsic motivation judgments in previous classroom studies (Spinath, [<reflink idref="bib60" id="ref112">60</reflink>]; Urhahne et al., [<reflink idref="bib64" id="ref113">64</reflink>]). For intrinsic motivation, valid cue information may be easily recognizable and highly visible so that even a short exposition is sufficient for a rather accurate judgment. Knowing the student for a longer period of time does not seem to provide much additional benefit to judgment accuracy (Praetorius et al., [<reflink idref="bib50" id="ref114">50</reflink>]). With respect to students' self-concept, the accuracy outcomes in our study were lower than in the study by Praetorius et al. ([<reflink idref="bib50" id="ref115">50</reflink>]). This difference may be due to the fact that the perceivers in the study by Praetorius and colleagues could in addition utilize verbal information from the targets. For intelligence, the low judgment accuracy we obtained in our study also differs from previous classroom research (Machts et al., [<reflink idref="bib38" id="ref116">38</reflink>]). Several reasons may explain the low accuracy outcomes for perceiver judgments of students' intelligence and the overall weak relation of the selected set of cues with targets' measured intelligence. To begin with, the physics experiments the targets had to perform may not have been entirely suitable to make differences in intelligence visible. Another contributing factor could be the fact that the video material was non-verbal (see also previous work by Borkenau & Liebler, [<reflink idref="bib8" id="ref117">8</reflink>] and Reynolds & Gifford, [<reflink idref="bib55" id="ref118">55</reflink>]). In their lens model study, Reynolds and Gifford ([<reflink idref="bib55" id="ref119">55</reflink>]), demonstrated based on a controlled experiment that intelligence can be more accurately perceived from auditory (e.g., speech rate and number of words) than from visual cues. Furthermore, previous research has shown a general tendency of perceivers to rely on less valid cues in their assessment of intelligence (see Murphy et al., [<reflink idref="bib43" id="ref120">43</reflink>]). Finally, the fact that only one item was used to judge students' intelligence could have played a role. If this item does not correspond to perceivers ' understanding of the construct of intelligence, this may result in lower accuracy.</p> <hd id="AN0184915346-29">Predictability, response consistency, and cue sensitivity</hd> <p>Predictability, response consistency, and cue sensitivity (matching) all contribute to accuracy. First of all, a predictable environment is a basic prerequisite for accurate judgments. In our study, we found some evidence for predictability of the selected cues, and hence, some of the cues that were rated based on the brief videos of the students were able to predict the different characteristics of the students quite well. Because the environment can't be controlled by the perceiver, response consistency and cue sensitivity are the only means by which a perceiver can influence his or her judgment accuracy. Notably, response consistency was rather high, indicating that perceivers used their judgment strategy in a consistent manner across different targets and characteristics. However, being consistent in one's judgment strategy only leads to an accurate judgment, when this strategy is characterized by a perceivers' ability to select and utilize the cues that are valid for a certain characteristic (i.e., cue sensitivity). Cue sensitivity is high when the judgment model (i.e., perceiver judgments) matches the environmental model (i.e., students' actual self-report and standardized test data). In the present study we found differences in cue sensitivity between the individual constructs. Hence, perceivers were more receptive to relevant information in their judgments of students' intrinsic motivation compared to students' intelligence. In the case of intelligence, the judgment model apparently did not match the environmental model sufficiently enough. This finding is in line with previous work showing that cues that perceivers expect to be an indicator of intelligence differ from such cues that are actually associated with intelligence (Murphy et al., [<reflink idref="bib43" id="ref121">43</reflink>]). Considering the rather low accuracy outcomes in the present research overall, one can conclude that cue sensitivity being comparatively low could be the main factor explaining this result.</p> <hd id="AN0184915346-30">Cue utilization</hd> <p>Lens model analyses showed that perceivers' judgments of the four student characteristics can be predicted by the utilization of certain cues. Whereas the majority of cues was utilized by the perceivers for their judgments across all criteria, however with different weights, some cues were only utilized for one or two of the investigated characteristics. For instance, a friendly facial expression was only assumed to play a role for students' academic self-concept in physics (however, negatively) and a distinctive clothing style was only expected to be related to students' intrinsic motivation in physics and the broad academic self-concept. However, looking friendly was actually related to the general academic self-concept and therefore this is a cue that would have been needed to be utilized for the judgment of this construct instead. Our study moreover demonstrated that some cues generally seem to be favored in the judgment process by the perceivers, such as a self-assured and attentive facial expression (see also Murphy et al., [<reflink idref="bib43" id="ref122">43</reflink>]), as well as expressive and tensed gestures and whether or not a student is wearing eyeglasses. The fact that wearing eyeglasses was considered as an important indicator across all constructs is in line with our expectations given that previous personality research has recognized the connection between wearing eyeglasses and the perception of intelligence or related constructs in individuals (Leder et al., [<reflink idref="bib36" id="ref123">36</reflink>]).</p> <p>Among the cues underlying our analyses, students' sex is of special interest. Perceivers utilized this cue for their judgments of all four student characteristics, and cue validity of sex was particularly high for students' self-concept and motivation, but low and not significant for students' intelligence. Thus, we found evidence for a <emph>gender bias</emph> with respect to perceiver judgments of students' intelligence in favor of the boys in our sample. A gender bias (Leaper & Starr, [<reflink idref="bib35" id="ref124">35</reflink>]), as it has been identified in the present research, implies different behavior towards female compared to male individuals based on different socially constructed expectations, often in favor of men or boys. In the school context, the <emph>gender grading bias</emph> (also: gender grading gap) has been receiving substantial attention, indicating differences in grades based on pupils' sex, even when the levels of academic performance are equivalent (e.g., Doornkamp et al., [<reflink idref="bib17" id="ref125">17</reflink>]; Protivínský & Münich, [<reflink idref="bib52" id="ref126">52</reflink>]). The fact that boys received higher intelligence ratings in the present study corresponds to research on teacher expectations and evaluation demonstrating that female students often receive less favorable ratings from teachers and pre-service teachers in STEM fields (Bonefeld et al., [<reflink idref="bib10" id="ref127">10</reflink>]; Holder & Kessels, [<reflink idref="bib26" id="ref128">26</reflink>]; Keller, [<reflink idref="bib32" id="ref129">32</reflink>]). For self-concept and intrinsic motivation, too, boys were rated more favorably than girls. But the female students in our sample in fact reported lower self-concept and motivation levels. This finding can be interpreted considering the <emph>kernel of truth</emph> hypothesis, which is based on the idea that stereotypes about a certain group can contain elements that actually correspond to specific characteristics of this group (Jussim et al., [<reflink idref="bib28" id="ref130">28</reflink>], [<reflink idref="bib29" id="ref131">29</reflink>]). Hence, by evaluating students' sex as a potentially important source of information for their judgment of self-concept and motivation, perceivers displayed a certain degree of accuracy. In a nutshell, we observed a gender effect on both sides of the lens (i.e., cue validity and cue utilization). When interpreting the results, one also needs to take into account that the video setting took place in a physics context, where we traditionally deal with a masculinity attribution (Keller, [<reflink idref="bib32" id="ref132">32</reflink>]). Hence, this cue was utilized in accordance to societal expectation and in line with the masculinity attribution of the STEM subjects in general.</p> <hd id="AN0184915346-31">Cue validity</hd> <p>Overall, cue validity was rather low in this study, while in certain dimensions and for certain constructs, some cues in fact served as valid indicators for the investigated criteria. For instance, cues were less powerful to predict students' intelligence compared to the other student characteristics, which then led to lower accuracy outcomes. Consequently, the same set of cues can be more or less suitable for different criteria of interest and therefore, predictability of a set of cues, i.e., the degree to which the selected set of cues represent targets' actual characteristics, has shown to vary largely depending on the nature of the investigated construct. Interestingly, cues that were relevant for constructs with a focus on the physics domain (both intrinsic motivation in physics and the domain-specific academic self-concept) were related to sex despite the standardization of the cues within female and male students in the sample. Characteristics that are linked to students' sex include students' body size and masculinity. Although boys are taller than girls on average, for instance, body size is related to some of the constructs within both sex groups. Intrinsic motivation was the construct that most evidently showed the importance of cues that are related to students' sex, as in addition to sex as cue itself, masculinity and being tall (i.e., body size) were highly valid indicators for students' intrinsic motivation in physics within the two sex categories.</p> <p>The suitability of cues to aid the perceiver judgment may also depend on the nature of the cues, e.g., whether they refer to an individual's appearance or behavior and whether or not speech is included in the video or the information is non-verbal (Breil et al., [<reflink idref="bib12" id="ref133">12</reflink>]; Reynolds & Gifford, [<reflink idref="bib55" id="ref134">55</reflink>]). In our study, the proportion of valid cues was comparatively higher among the static cues than among the dynamic cues, which may have been more suitable for some student characteristics compared to others.</p> <hd id="AN0184915346-32">Limitations and further directions</hd> <p>First, by looking at teacher judgments based on minimal information, we attempted to mimic a situation in which a teacher meets a student for the first time given the potential relevance of such early impressions on the subsequent teacher-student interaction and future teacher evaluations. This approach appears to be very fruitful in identifying processes involved in teacher initial impressions as well as possible expectation biases that can have an impact on teachers' professional judgment, such as the gender bias. The fact that perceivers in our study strongly relied on students' sex for their judgments confirms the role of perceivers' implicit and different expectations towards boys and girls for their judgments across different student characteristics relevant for learning. Moreover, the observed differences in respect to the lens model parameters and also with regard to the cue validities indicate that the suitability of the applied cues to predict students' characteristics varied between the four student characteristics. Cue validity was overall rather low, particularly in respect to students' assessed intelligence. Low cue validity, however, is not a negative finding per se, but rather indicates that other relevant cues may have been overlooked. An important subsequent research step would therefore be to identify which cues, other than the ones included in the present research, can predict different student characteristics in teacher first impressions. In more general terms, additional relevant processes and information (i.e., cues) may have been involved in perceivers' judgment formation that were not covered in the present research. Future research should therefore attempt to identify such processes and to further address the apparent uniqueness of different student characteristics when it comes to teacher judgments based on minimal information. Furthermore, this research should then also take a deeper look into the (longitudinal) translation of more or less accurate teacher first impressions into actual teacher behavior in the classroom to bridge the gap between evidence regarding teacher first impressions and the subsequent teacher-student interaction and judgments, grading and evaluations that can have an essential impact on students' future academic and life prospects<emph>.</emph></p> <p>Second, participants in our study, both perceivers and targets were either enrolled in a German University program or attending a German school. Hence, generalizability of our findings to other countries or geographical regions is somewhat limited. Future studies might investigate whether or not such findings can be replicated in other countries or different cultural contexts.</p> <p>Third, the perceivers in our study were teacher students and psychology undergraduate students but not experienced teachers. Topics like personality, interaction, and judgment are not only important for experienced teachers, but are also important issues in both fields of study. It is an open question if and to what degree teaching experience has an additional benefit for zero-acquaintance judgments in a classroom situation. Therefore, in future research it would be interesting to include experienced teachers, to identify the possible role of teaching experience for this kind of judgment.</p> <p>Fourth, the present research took place in the context of a physics experiment. Physics as a school subject, as much as other STEM disciplines, has shown to be particularly vulnerable to judgmental biases. The gender bias revealed in the present research underlines the importance of establishing awareness programs and training, particularly for teachers in the STEM fields. Such activities could also help increasing the share of females that choose a career path in the natural sciences, given that career choices of young students are influenced by the experiences they make during their time in school (Leaper & Starr, [<reflink idref="bib35" id="ref135">35</reflink>]). We moreover believe that replicating our findings in other subject areas, such as language or math is an important task for future research.</p> <p>Finally, we chose to reduce the number of items from the original student self-report scales for the judgments given that we were interested in several student characteristics within the same study and needed to bear possible participant fatigue and exhaustion in mind. Reducing the number of items from the original scales is a standard procedure in interpersonal perception research, however, it may have influenced the results to some extent. Future research should therefore aim at assessing both judgment and criterium based on the same scale.</p> <hd id="AN0184915346-33">Conclusion</hd> <p>The present study revealed the potential of BLM and applying a zero-acquaintance and thin-slice of behavior research design to shed light on the judgment process itself, which has received little attention in previous research on teacher judgment accuracy. We were thereby able to identify cues that are valid for students' various characteristics relevant for learning and performance in school and the degree to which perceivers are susceptible to such information in their judgment.</p> <p>Whereas judgment accuracy was overall rather low, lens model parameter analysis offered valuable insights showing that an accurate teacher judgment depends not only on the available cues, but also on the extent to which they are utilized by the perceivers. For constructs with low (intelligence) and higher (intrinsic motivation in physics) accuracy, these outcomes were reflected in lower sensitivity values and also a lower predictability of the applied set of cues. Through looking at pre-service teachers' utilization of cues in their judgments, we were moreover able to identify a gender bias, particularly for students' intelligence in favor of the male students in our sample. Those findings are a reminder that gender discrimination, which has traditionally been present in the STEM disciplines, is an ongoing issue that needs further attention in research and practice.</p> <p>The findings of the present research indicate that whereas teachers may be fairly accurate in assessing some student characteristics at first glance, for the case of other characteristics, teacher judgment accuracy may benefit from longer interactions, such as for instance judgments of students' cognitive abilities. This leads to the quest to inform teachers that judgments can be erroneous and should be reflected on (and potentially corrected) regularly throughout the course of an interaction with a student. Moreover, the overall rather low validity of the selected cues underlines the importance to extent the present line of research to identify various kinds of information that teachers rely on in their judgments of students' different characteristics during their very first encounter and the information that would be relevant to consider for arriving at more accurate first impressions in the classroom.</p> <hd id="AN0184915346-34">Acknowledgements</hd> <p>We thank the teachers, students and pupils who participated in our study. Furthermore, we thank our research team and student assistants for their engagement in the data collection.</p> <hd id="AN0184915346-35">Author contributions</hd> <p>Caroline V. Bhowmik: Conceptualization, Investigation, Formal Analyses, Data Curation, Writing- Original Draft, Project Administration, Visualization, Writing—Review & Editing Mitja Back: Supervision, Validation, Writing—Review Steffen Nestler: Methodology, Software, Validation, Formal Analyses, Writing—Review & Editing Editing Friedrich-Wilhelm Schrader: Supervision, Writing- Original draft, Writing—Review & Editing, Validation.</p> <hd id="AN0184915346-36">Funding</hd> <p>Open access funding provided by Karlstad University. This research project was supported by the German Research Foundation (DFG) through the Graduate School "Teaching and Learning Processes" (Upgrade) (GRK 1561).</p> <hd id="AN0184915346-37">Data availability</hd> <p>All data as well as the related <emph>R</emph> codes will be made publicly available via the Open Science Framework (OSF).</p> <hd id="AN0184915346-38">Declarations</hd> <p></p> <hd id="AN0184915346-39">Conflict of interests</hd> <p>The authors have no competing interests to declare that are relevant to the content of this article.</p> <hd id="AN0184915346-40">Ethics approval</hd> <p>We received approval from the board members of the graduate school and adhered to the APA ethical standards at all stages of the research project.</p> <hd id="AN0184915346-41">Appendix</hd> <p></p> <hd id="AN0184915346-42">Intercorrelations of final Cue-Categories</hd> <p></p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Cues</p></th><th align="left"><p>1</p></th><th align="left"><p>2</p></th><th align="left"><p>3</p></th><th align="left"><p>4</p></th><th align="left" /><th align="left"><p>5</p></th><th align="left"><p>6</p></th><th align="left"><p>7</p></th><th align="left"><p>8</p></th><th align="left"><p>9</p></th><th align="left"><p>10</p></th><th align="left"><p>11</p></th><th align="left"><p>12</p></th><th align="left"><p>13</p></th><th align="left"><p>14</p></th><th align="left"><p>15</p></th><th align="left"><p>16</p></th><th align="left"><p>17</p></th></tr></thead><tbody><tr><td align="left"><p>Friendly facial expression (1)</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Self-assured non-shy facial expression (2)</p></td><td char="." align="char"><p>0.20</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Communicative facial expression (3)</p></td><td char="." align="char"><p>0.29</p></td><td char="." align="char"><p>-0.02</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Attentive facial expression (4)</p></td><td char="." align="char"><p>0.37</p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>0.35</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Expressive gestures (5)</p></td><td char="." align="char"><p>0.35</p></td><td char="." align="char"><p>0.29</p></td><td char="." align="char"><p>0.21</p></td><td char="." align="char"><p>0.42</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Tensed gestures (6)</p></td><td char="." align="char"><p>− 0.20</p></td><td char="." align="char"><p>− 0.38</p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Distinctive clothing style (7)</p></td><td char="." align="char"><p>0.19</p></td><td char="." align="char"><p>0</p></td><td char="." align="char"><p>0.18</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.13</p></td><td char="." align="char"><p>− 0.15</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Eyeglasses (8)</p></td><td char="." align="char"><p>0</p></td><td char="." align="char"><p>− 0.07</p></td><td char="." align="char"><p>0</p></td><td char="." align="char"><p>0.13</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>− 0.19</p></td><td char="." align="char"><p>0.16</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Distinctive hair (9)</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>− 0.13</p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p>− 0.07</p></td><td char="." align="char"><p>0.07</p></td><td char="." align="char"><p>0.33</p></td><td char="." align="char"><p>0.16</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Distinctive face (10)</p></td><td char="." align="char"><p>0.24</p></td><td char="." align="char"><p>− 0.28</p></td><td char="." align="char"><p>0.02</p></td><td char="." align="char"><p>0.03</p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p>0.15</p></td><td char="." align="char"><p>0.28</p></td><td char="." align="char"><p>0.23</p></td><td char="." align="char"><p>0.37</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Mature face (11)</p></td><td char="." align="char"><p>− 0.10</p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>− 0.20</p></td><td char="." align="char"><p>− 0.15</p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p>0</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>0.28</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Attractiveness (12)</p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>0.20</p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p>− 0.10</p></td><td char="." align="char"><p>0.08</p></td><td char="." align="char"><p>− 0.22</p></td><td char="." align="char"><p>− 0.35</p></td><td char="." align="char"><p>− 0.34</p></td><td char="." align="char"><p>− 0.08</p></td><td char="." align="char"><p>− 0.06</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Body size (13)</p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>0.22</p></td><td char="." align="char"><p>− 0.05</p></td><td char="." align="char"><p>0.20</p></td><td char="." align="char"><p>− 0.08</p></td><td char="." align="char"><p>− 0.14</p></td><td char="." align="char"><p>0.05</p></td><td char="." align="char"><p>0.37</p></td><td char="." align="char"><p>0.25</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Dark appearance (14)</p></td><td char="." align="char"><p>0.11</p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>− 0.08</p></td><td char="." align="char"><p>− 0.34</p></td><td char="." align="char"><p>− 0.09</p></td><td char="." align="char"><p>0</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>0.17</p></td><td char="." align="char"><p>− 0.01</p></td><td char="." align="char"><p>0.19</p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>0.12</p></td><td char="." align="char"><p>− 0.14</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Apparant social background (15)</p></td><td char="." align="char"><p>0.24</p></td><td char="." align="char"><p>0.13</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p>0.14</p></td><td char="." align="char"><p>− 0.02</p></td><td char="." align="char"><p>− 0.03</p></td><td char="." align="char"><p>− 0.17</p></td><td char="." align="char"><p>− 0.04</p></td><td char="." align="char"><p>− 0.25</p></td><td char="." align="char"><p>− 0.24</p></td><td char="." align="char"><p>− 0.22</p></td><td char="." align="char"><p>0.49</p></td><td char="." align="char"><p>− 0.03</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>Masculinity (16)</p></td><td char="." align="char"><p>− 0.14</p></td><td char="." align="char"><p>0.10</p></td><td char="." align="char"><p>− 0.10</p></td><td char="." align="char"><p>− 0.07</p></td><td char="." align="char"><p>0.18</p></td><td char="." align="char"><p>− 0.18</p></td><td char="." align="char"><p>0</p></td><td char="." align="char"><p>− 0.15</p></td><td char="." align="char"><p>− 0.28</p></td><td char="." align="char"><p>− 0.13</p></td><td char="." align="char"><p>0.16</p></td><td char="." align="char"><p>− 0.06</p></td><td char="." align="char"><p>0.23</p></td><td char="." align="char"><p>− 0.08</p></td><td char="." align="char"><p>− 0.20</p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr></tbody></table> </ephtml> </p> <p>NTarget = 45, NCues = 17. Given the z-standardization of all cues within the two sex categories before aggregation, intercorrelation of sex with all other cues remains 0.</p> <hd id="AN0184915346-43">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0184915346-44"> <title> References </title> <blist> <bibl id="bib1" idref="ref42" type="bt">1</bibl> <bibtext> Ambady N, Bernieri F, Richeson J. Toward a histology of social behavior: Judgmental accuracy from thin slices of the behavioral stream. Advances in Experimental Social Psychology. 2000; 32: 201-271. 10.1016/S0065-2601(00)80006-4</bibtext> </blist> <blist> <bibl id="bib2" idref="ref10" type="bt">2</bibl> <bibtext> Ambady N, Hallahan M, Rosenthal R. On judging and being judged accurately in zero-acquaintance situations. Journal of Personality and Social Psychology. 1995; 69; 3: 518-529. 10.1037/0022-3514.69.3.518</bibtext> </blist> <blist> <bibl id="bib3" idref="ref51" type="bt">3</bibl> <bibtext> Ambady N, Rosenthal R. Half a minute: Predicting teacher evaluations from thin slices of nonverbal behavior and physical attractiveness. Journal of Personality and Social Psychology. 1993; 64; 3: 431-441. 10.1037/0022-3514.64.3.431</bibtext> </blist> <blist> <bibl id="bib4" idref="ref9" type="bt">4</bibl> <bibtext> Ambady N, Rosenthal R. Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. Psychological Bulletin. 1992; 111; 2: 256-274. 10.1037/0033-2909.111.2.256</bibtext> </blist> <blist> <bibl id="bib5" idref="ref57" type="bt">5</bibl> <bibtext> Back MD, Nestler SHall JA, Schmid Mast M, West TV. Accuracy of judging personality. The social psychology of perceiving others accurately. 2016; Cambridge University Press: 98-125. 10.1017/CBO9781316181959.005</bibtext> </blist> <blist> <bibl id="bib6" idref="ref61" type="bt">6</bibl> <bibtext> Back MD, Schmukle SC, Egloff B. A closer look at first sight: Social relations lens model analysis of personality and interpersonal attraction at zero acquaintance. European Journal of Personality. 2011; 25; 3: 225-238. 10.1002/per.790</bibtext> </blist> <blist> <bibl id="bib7" idref="ref2" type="bt">7</bibl> <bibtext> Bergold S, Steinmayr R. Teacher judgments predict developments in adolescents' school performance, motivation, and life satisfaction. Journal of Educational Psychology. 2023; 115; 4: 642-664. 10.1037/edu0000786</bibtext> </blist> <blist> <bibl id="bib8" idref="ref117" type="bt">8</bibl> <bibtext> Borkenau P, Liebler A. Convergence of stranger ratings of personality and intelligence with self-ratings, partner ratings, and measured intelligence. Journal of Personality and Social Psychology. 1993; 65; 3: 546-553. 10.1037/0022-3514.65.3.546</bibtext> </blist> <blist> <bibl id="bib9" idref="ref41" type="bt">9</bibl> <bibtext> Bhowmik CV, Nestler S, Schrader F-W, Praetorius A-K, Biesanz JC, Back MD. Teacher judgments at zero-acquaintance: A social accuracy analysis. Contemporary Educational Psychology. 2021; 65. 10.1016/j.cedpsych.2021.101965101965</bibtext> </blist> <blist> <bibtext> Bonefeld M, Dickhäuser O, Karst K. Do preservice teachers' judgments and judgment accuracy depend on students' characteristics? The effect of gender and immigration background. Social Psychology of Education. 2020; 23; 1: 189-216. 10.1007/s11218-019-09533-2</bibtext> </blist> <blist> <bibtext> Borkenau P, Leising D, Fritz U. Effects of communication between judges on consensus and accuracy in judgments of people's intelligence. European Journal of Psychological Assessment. 2014; 30; 4: 274-282. 10.1027/1015-5759/a000188</bibtext> </blist> <blist> <bibtext> Breil SM, Osterholz S, Nestler S, Back MDLetzring TD, Spain JS. Contributions of nonverbal cues to the accurate judgment of personality traits. The Oxford handbook of accurate personality judgment. 2021; Oxford University Press: 195-218. 10.31234/osf.io/mn2je</bibtext> </blist> <blist> <bibtext> Brunswik E. Perception and the representative design of psychological experiments. 1956; University of California Press. 10.1525/9780520350519</bibtext> </blist> <blist> <bibtext> Carney DR, Colvin CR, Hall JA. A thin slice perspective on the accuracy of first impressions. Journal of Research in Personality. 2007; 41; 5: 1054-1072. 10.1016/j.jrp.2007.01.004</bibtext> </blist> <blist> <bibtext> Cooksey RW, Freebody P, Davidson GR. Teachers' predictions of children's early reading achievement: An application of social judgment theory. American Educational Research Journal. 1986; 23; 1: 41-64. 10.3102/00028312023001041</bibtext> </blist> <blist> <bibtext> Darley JM, Fazio RH. Expectancy confirmation processes arising in the social interaction sequence. American Psychologist. 1980; 35; 10: 867-881. 10.1037/0003-066X.35.10.867</bibtext> </blist> <blist> <bibtext> Doornkamp L, Van der Pol LD, Groeneveld S, Mesman J, Endendijk JJ, Groeneveld MG. Understanding gender bias in teachers' grading: The role of gender stereotypical beliefs. Teaching and Teacher Education. 2022; 118: 103826. 10.1016/j.tate.2022.103826</bibtext> </blist> <blist> <bibtext> Fletcher, T. D. (2022). QuantPsyc: Quantitative Psychology Tools (1.6) [Computer software]. https://cran.r-project.org/web/packages/QuantPsyc/index.html</bibtext> </blist> <blist> <bibtext> Förster, N, & Böhmer, I. (2017). Das Linsenmodell – Grundlagen und exemplarische Anwendungen in der pädagogisch-psychologischen Diagnostik [The lens model – fundamental aspects and exemplary applications in educational assessment]. In A. Südkamp & A.-K. Praetorius (Eds.), Diagnostische Kompetenz von Lehrkräften. Theoretische und methodische Weiterentwicklungen [Diagnostic competence of teachers. Theoretical and methodological advancements] (pp. 46–50). Waxmann.</bibtext> </blist> <blist> <bibtext> Frey, A, Taskinen, P. H, Schütte, K, Prenzel, M, Artelt, C, Baumert, J, Blum, W, Hammann, M, Klieme, E, & Pekrun, R. (Eds.). (2009). PISA 2006 Skalenhandbuch. Dokumentation der Erhebungsinstrumente [PISA 2006 scale documentation]. Waxmann.</bibtext> </blist> <blist> <bibtext> Friedrich A, Flunger B, Nagengast B, Jonkmann K, Trautwein U. Pygmalion effects in the classroom: Teacher expectancy effects on students' math achievement. Contemporary Educational Psychology. 2015; 41: 1-12. 10.1016/j.cedpsych.2014.10.006</bibtext> </blist> <blist> <bibtext> Funder DC, Ozer DJ. Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science. 2019; 2; 2: 156-168. 10.1177/2515245919847202</bibtext> </blist> <blist> <bibtext> Hardy I, Decristan J, Klieme E. Adaptive teaching in research on learning and instruction. Journal for Educational Research Online. 2019; 11; 2: 169-191. 10.25656/01:18004</bibtext> </blist> <blist> <bibtext> Harris MJ, Garris CPAmbady N, Skowronski JJ. You never get a second chance to make a first impression: Behavioral consequences of first impressions. First impressions. 2008; Guilford Publications: 147-168</bibtext> </blist> <blist> <bibtext> Helmke A, Schrader F-W. Interactional effects of instructional quality and teacher judgement accuracy on achievement. Teaching and Teacher Education. 1987; 3; 2: 91-98. 10.1016/0742-051X(87)90010-2</bibtext> </blist> <blist> <bibtext> Holder K, Kessels U. Gender and ethnic stereotypes in student teachers' judgments: A new look from a shifting standards perspective. Social Psychology of Education. 2017; 20; 3: 471-490. 10.1007/s11218-017-9384-z</bibtext> </blist> <blist> <bibtext> Jussim L, Harber KD. Teacher expectations and self-fulfilling prophecies: Knowns and unknowns, resolved and unresolved controversies. Personality and Social Psychology Review. 2005; 9; 2: 131-155. 10.1207/s15327957pspr0902_3</bibtext> </blist> <blist> <bibtext> Jussim L, Cain TR, Crawford JT, Harber K, Cohen FNelson TD. The unbearable accuracy of stereotypes. Handbook of prejudice, stereotyping, and discrimination. 2009; Psychology Press: 199-227</bibtext> </blist> <blist> <bibtext> Jussim L, Crawford JT, Rubinstein RS. Stereotype (In)accuracy in perceptions of groups and individuals. Current Directions in Psychological Science. 2015; 24; 6: 490-497. 10.1177/0963721415605257</bibtext> </blist> <blist> <bibtext> Karelaia N, Hogarth RM. Determinants of linear judgment: A meta-analysis of lens model studies. Psychological Bulletin. 2008; 134; 3: 404-426. 10.1037/0033-2909.134.3.404</bibtext> </blist> <blist> <bibtext> Kaufmann E. Lens model studies: Revealing teachers' judgements for teacher education. Journal of Education for Teaching. 2023; 49; 2: 236-251. 10.1080/02607476.2022.2061336</bibtext> </blist> <blist> <bibtext> Keller C. Effect of teachers' stereotyping on students' stereotyping of mathematics as a male domain. The Journal of Social Psychology. 2001; 141; 2: 165-173. 10.1037/amp0000263</bibtext> </blist> <blist> <bibtext> Kriegbaum K, Jansen M, Spinath B. Motivation: A predictor of PISA's mathematical competence beyond intelligence and prior test achievement. Learning and Individual Differences. 2015; 43: 140-148. 10.1016/j.lindif.2015.08.026</bibtext> </blist> <blist> <bibtext> Lansu TAM, van den Berg YHM. Thin-Slice judgments of children's social status and behavior. The Journal of Experimental Education. 2020; 90; 4: 1-14. 10.1080/00220973.2020.1808943</bibtext> </blist> <blist> <bibtext> Leaper C, Starr CR. Helping and hindering undergraduate women's STEM motivation: Experiences with STEM encouragement, STEM-related gender bias, and sexual harassment. Psychology of Women Quarterly. 2019; 43; 2: 165-183. 10.1177/0361684318806302</bibtext> </blist> <blist> <bibtext> Leder H, Forster M, Gerger G. The glasses stereotype revisited: Effects of eyeglasses on perception, recognition, and impression of faces. Swiss Journal of Psychology / Schweizerische Zeitschrift Für Psychologie / Revue Suisse De Psychologie. 2011; 70: 211-222. 10.1024/1421-0185/a000059</bibtext> </blist> <blist> <bibtext> Liepmann, D, Beauducel, B, Brocke, B, & Nettelstroh, W. (2012). IST-Screening. Intelligenz-Struktur-Test [IST-Screening. Intelligence-Structure-Test]. Hogrefe.</bibtext> </blist> <blist> <bibtext> Machts N, Kaiser J, Schmidt FTC, Möller J. Accuracy of teachers' judgments of students' cognitive abilities: A meta-analysis. Educational Research Review. 2016; 19: 85-103. 10.1016/j.edurev.2016.06.003</bibtext> </blist> <blist> <bibtext> Marksteiner T, Reinhard M-A, Dickhäuser O, Sporer SL. How do teachers perceive cheating students? Beliefs about cues to deception and detection accuracy in the educational field. European Journal of Psychology of Education. 2012; 27; 3: 329-350. 10.1007/s10212-011-0074-5</bibtext> </blist> <blist> <bibtext> Marsh HW, Craven RPhye GD. Chapter 6: Academic self-concept: Beyond the dustbowl. Handbook of classroom assessment. 1996; Academic Press: 131-198. 10.1016/B978-012554155-8/50008-9</bibtext> </blist> <blist> <bibtext> Marsh HW. Content specificity of relations between academic achievement and academic self-concept. Journal of Educational Psychology. 1992; 84; 1: 35-42. 10.1037/0022-0663.84.1.35</bibtext> </blist> <blist> <bibtext> Murphy NA. Appearing smart: The impression management of intelligence, person perception accuracy, and behavior in social interaction. Personality and Social Psychology Bulletin. 2007; 33; 3: 325-339. 10.1177/0146167206294871</bibtext> </blist> <blist> <bibtext> Murphy NA, Hall JA, Colvin CR. Accurate intelligence assessments in social interactions: Mediators and gender effects. Journal of Personality. 2003; 71; 3: 465-493. 10.1111/1467-6494.7103008</bibtext> </blist> <blist> <bibtext> Murphy NA, Hall JA, Ruben MA, Frauendorfer D, Schmid Mast M, Johnson KE, Nguyen L. Predictive validity of thin-slice nonverbal behavior from social interactions. Personality & Social Psychology Bulletin. 2019; 45; 7: 983-993. 10.1177/0146167218802834</bibtext> </blist> <blist> <bibtext> Murphy NA, Hall JA, Schmid Mast M, Ruben MA, Frauendorfer D, Blanch-Hartigan D, Roter DL, Nguyen L. Reliability and validity of nonverbal thin slices in social interactions. Personality & Social Psychology Bulletin. 2015; 41; 2: 199-213. 10.1177/0146167214559902</bibtext> </blist> <blist> <bibtext> Möller J, Pohlmann B, Köller O, Marsh HW. A meta-analytic path analysis of the internal/external frame of reference model of academic achievement and academic self-concept. Review of Educational Research. 2016; 79; 3: 1129-1167. 10.3102/0034654309337522</bibtext> </blist> <blist> <bibtext> Nestler S, Back MD. Applications and extensions of the lens model to understand interpersonal judgments at zero acquaintance. Current Directions in Psychological Science. 2013; 22; 5: 374-379. 10.1177/0963721413486148</bibtext> </blist> <blist> <bibtext> Nestler S, Egloff B, Küfner ACP, Back MD. An integrative lens model approach to bias and accuracy in human inferences: Hindsight effects and knowledge updating in personality judgments. Journal of Personality and Social Psychology. 2012; 103; 4: 689-717. 10.1037/a0029461</bibtext> </blist> <blist> <bibtext> Nickerson RS. Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology. 1998; 2; 2: 175-220. 10.1037/1089-2680.2.2.175</bibtext> </blist> <blist> <bibtext> Praetorius A-K, Drexler K, Rösch L, Christophel E, Heyne N, Scheunpflug A, Zeinz H, Dresel M. Judging students' self-concepts within 30s? Investigating judgement accuracy in a zero-acquaintance situation. Learning and Individual Differences. 2015; 37: 231-236. 10.1016/j.lindif.2014.11.015</bibtext> </blist> <blist> <bibtext> Praetorius, A.-K, Karst, K, Dickhäuser, O, & Lipowsky, F. (2011). Wie gut schätzen Lehrkräfte die Fähigkeitsselbstkonzepte ihrer Schüler ein? [How accurate are teachers in their judgments of pupils' self-concepts?] Psychologie in Erziehung Und Unterricht, 2, 81–91. https://doi.org/10.2378/peu2010.art30d</bibtext> </blist> <blist> <bibtext> Protivínský T, Münich D. Gender Bias in teachers' grading: What is in the grade. Studies in Educational Evaluation. 2018; 59: 141-149. 10.1016/j.stueduc.2018.07.006</bibtext> </blist> <blist> <bibtext> R Development Core Team. (2020). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing.</bibtext> </blist> <blist> <bibtext> Revelle, W. (2023). psych: Procedures for Psychological, Psychometric, and Personality Research (2.3.6) [Computer software]. https://cran.r-project.org/web/packages/psych/index.html</bibtext> </blist> <blist> <bibtext> Reynolds DJ, Gifford R. The sounds and sights of intelligence: A lens model channel analysis. Personality and Social Psychology Bulletin. 2001; 27; 2: 187-200. 10.1177/0146167201272005</bibtext> </blist> <blist> <bibtext> Rost, D. A, Sparfeldt, J. R, & Schilling, S. R. (2007). DISK-GITTER mit SKSLF-8. Differentielles Schulisches Selbstkonzept-Gitter mit Skala zur Erfassung des Selbstkonzepts schulischer Leistungen und Fähigkeiten [Differential self-concept scale to assess the academic self-concept]. Hogrefe.</bibtext> </blist> <blist> <bibtext> Schnitzler K, Holzberger D, Seidel T. Connecting judgment process and accuracy of student teachers: Differences in observation and student engagement cues to assess student characteristics. Frontiers in Education. 2020; 5: 602470. 10.3389/feduc.2020.602470</bibtext> </blist> <blist> <bibtext> Seidel, T, Prenzel, M, Duit, R, & Lehrke, M. (2003). Technischer Bericht zur Videostudie "Lehr-Lern-Prozesse im Physikunterricht" [Technical Report of the Video Study "Teaching and Learning Processes in the Physics Classroom"] (1, Aufl.). IPN Leibniz-Institut f. d. Pädagogik d. Naturwissenschaften an d. Universität Kiel.</bibtext> </blist> <blist> <bibtext> Shavelson RJ, Hubner JJ, Stanton GC. Self-concept: Validation of construct interpretations. Review of Educational Research. 1976; 46; 3: 407-441. 10.3102/00346543046003407</bibtext> </blist> <blist> <bibtext> Spinath B. Akkuratheit der Einschätzung von Schülermerkmalen durch Lehrer und das Konstrukt der diagnostischen Kompetenz [Accuracy of teachers' judgments regarding students' characteristics and the construct of diagnostic competence]. Zeitschrift Für Pädagogische Psychologie. 2005; 19; 1: 85-95. 10.1024/1010-0652.19.12.85</bibtext> </blist> <blist> <bibtext> Stopfer JM, Egloff B, Nestler S, Back MD. Personality expression and impression formation in online social networks: An integrative approach to understanding the processes of accuracy, impression management and meta-accuracy. European Journal of Personality. 2014; 28; 1: 73-94. 10.1002/per.1935</bibtext> </blist> <blist> <bibtext> Südkamp A, Kaiser J, Möller J. Accuracy of teachers' judgments of students' academic achievement: A meta-analysis. Journal of Educational Psychology. 2012; 104; 3: 743-762. 10.1037/a0027627</bibtext> </blist> <blist> <bibtext> Tucker LR. A suggested alternative formulation in the developments by Hursch, Hammond, and Hursch, and by Hammond, Hursch, and Todd. Psychological Review. 1964; 71; 6: 528-530. 10.1037/h0047061</bibtext> </blist> <blist> <bibtext> Urhahne D, Chao S-H, Florineth ML, Luttenberger S, Paechter M. Academic self-concept, learning motivation, and test anxiety of the underestimated student. The British Journal of Educational Psychology. 2011; 81; 1: 161-177. 10.1348/000709910X504500</bibtext> </blist> <blist> <bibtext> Urhahne D, Wijnia L. A review on the accuracy of teacher judgments. Educational Research Review. 2021; 32. 10.1016/j.edurev.2020.100374100374</bibtext> </blist> <blist> <bibtext> Wickham, H. (2022). plyr: Tools for Splitting, Applying and Combining Data (1.8.8) [Computer software]. https://cran.r-project.org/web/packages/plyr/index.html</bibtext> </blist> </ref> <ref id="AN0184915346-45"> <title> Footnotes </title> <blist> <bibtext> Here, <emph>r</emph><subs>m</subs> (matching index) is the correlation between the predicted values of both the judgment and the criterion model (i.e., cue sensitivity), <emph>R</emph><subs>e</subs> refers to predictability and <emph>R</emph><subs>s</subs> denotes consistency, which is measured by the correlation between the actual judgments and the judgments predicted by the cues (see Karelaia & Hogarth, [30] and Tucker, [63], for a detailed elaboration of the lens model equation).</bibtext> </blist> <blist> <bibtext> The 45 students that served as targets in the present study came from 10 different schools. Hence, it could be argued that the present data is nested in the sense that students are more similar in some respects within classes than across classes. However, we expect this nested data structure to play a negligible role for the present research because perceivers were not aware of which schools the targets were attending to, hence they were not aware that some students attend to the same school. Furthermore, we also believe that this nested data structure does not play a role in the statistical analyses because it is not likely that students from the same class were more similar in respect to the cues that they exhibited (also given how the target pool was generated). Finally, the significance test that we use in the lens model analyses is based on the single perceivers' parameters (e.g., the accuracies of the single perceivers). Thus, the nested target-structure does not play a role in these tests (i.e., in the calculation of the standard error, for example, of the average accuracy correlation). Therefore, we do not calculate cross-classified multilevel models.</bibtext> </blist> <blist> <bibtext> To evaluate effect sizes obtained in this study, we employed the benchmarks suggested by Funder & Ozer ([22]) as orientational framework.</bibtext> </blist> </ref> <aug> <p>By Caroline V. Bhowmik; Mitja D. Back; Steffen Nestler and Friedrich-Wilhelm Schrader</p> <p>Reported by Author; Author; Author; Author</p> <p></p> <p>Caroline V. Bhowmik Caroline V. Bhowmik is a postdoctoral researcher at the Department of Political, Historical, Religious and Cultural Studies at Karlstad University, Sweden; email: caroline.bhowmik@kau.se. Her research focuses on teacher perceptual processes and judgment accuracy in respect to students' emotional and motivational characteristics, as well as teacher perceptions of students' emotional reactions towards the climate crisis (e.g., eco-anxiety) and how to integrate emotional aspects in sustainability education.</p> <p>Mitja D. Back Mitja D. Back is professor for Personality Psychology and Psychological Assessment at the Department of Psychology, University of Münster, Germany; email: mitja.back@uni-muenster.de. His research centers around the interplay of personality and social relationships including work on first impressions, social interaction dynamics, the co-development of personality and social relationships, and narcissism. He is the scientific director of the Center for Social Skills and the Münster Center for Open Science.</p> <p>Steffen Nestler Steffen Nestler is professor for psychological methods and statistics at the University of Münster, Germany; e-mail: steffen.nestler@uni-muenster.de. His research interests include the analysis of social relations and network data, the analysis of intensive longitudinal data, the estimation of single-level and multilevel structural equation models, and regularization methods.</p> <p>Friedrich-Wilhelm Schrader Friedrich-Wilhelm Schrader formerly Academic Direktor of the Department of Psychology at the University of Kaiserslautern-Landau, Germany, has sparked the development of teacher judgment accuracy research in the German-speaking scientific context and fundamentally contributed to the methodological and theoretical development of teacher judgment accuracy research from the disciplinary perspective of educational psychology and empirical educational sciences.</p> </aug> <nolink nlid="nl1" bibid="bib23" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib21" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib27" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib62" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib65" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib38" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib60" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib16" firstref="ref11"></nolink> <nolink nlid="nl9" bibid="bib24" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib49" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib57" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib13" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib46" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib43" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib50" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib59" firstref="ref20"></nolink> <nolink nlid="nl17" bibid="bib41" firstref="ref21"></nolink> <nolink nlid="nl18" bibid="bib40" firstref="ref22"></nolink> <nolink nlid="nl19" bibid="bib33" firstref="ref24"></nolink> <nolink nlid="nl20" bibid="bib25" firstref="ref26"></nolink> <nolink nlid="nl21" bibid="bib51" firstref="ref31"></nolink> <nolink nlid="nl22" bibid="bib64" firstref="ref34"></nolink> <nolink nlid="nl23" bibid="bib35" firstref="ref38"></nolink> <nolink nlid="nl24" bibid="bib11" firstref="ref44"></nolink> <nolink nlid="nl25" bibid="bib42" firstref="ref45"></nolink> <nolink nlid="nl26" bibid="bib55" firstref="ref47"></nolink> <nolink nlid="nl27" bibid="bib45" firstref="ref49"></nolink> <nolink nlid="nl28" bibid="bib44" firstref="ref50"></nolink> <nolink nlid="nl29" bibid="bib34" firstref="ref53"></nolink> <nolink nlid="nl30" bibid="bib47" firstref="ref58"></nolink> <nolink nlid="nl31" bibid="bib12" firstref="ref62"></nolink> <nolink nlid="nl32" bibid="bib30" firstref="ref63"></nolink> <nolink nlid="nl33" bibid="bib19" firstref="ref65"></nolink> <nolink nlid="nl34" bibid="bib15" firstref="ref66"></nolink> <nolink nlid="nl35" bibid="bib39" firstref="ref67"></nolink> <nolink nlid="nl36" bibid="bib48" firstref="ref74"></nolink> <nolink nlid="nl37" bibid="bib14" firstref="ref79"></nolink> <nolink nlid="nl38" bibid="bib56" firstref="ref81"></nolink> <nolink nlid="nl39" bibid="bib58" firstref="ref82"></nolink> <nolink nlid="nl40" bibid="bib20" firstref="ref83"></nolink> <nolink nlid="nl41" bibid="bib37" firstref="ref84"></nolink> <nolink nlid="nl42" bibid="bib61" firstref="ref94"></nolink> <nolink nlid="nl43" bibid="bib31" firstref="ref100"></nolink> <nolink nlid="nl44" bibid="bib63" firstref="ref101"></nolink> <nolink nlid="nl45" bibid="bib53" firstref="ref103"></nolink> <nolink nlid="nl46" bibid="bib66" firstref="ref104"></nolink> <nolink nlid="nl47" bibid="bib54" firstref="ref105"></nolink> <nolink nlid="nl48" bibid="bib18" firstref="ref106"></nolink> <nolink nlid="nl49" bibid="bib36" firstref="ref123"></nolink> <nolink nlid="nl50" bibid="bib17" firstref="ref125"></nolink> <nolink nlid="nl51" bibid="bib52" firstref="ref126"></nolink> <nolink nlid="nl52" bibid="bib10" firstref="ref127"></nolink> <nolink nlid="nl53" bibid="bib26" firstref="ref128"></nolink> <nolink nlid="nl54" bibid="bib32" firstref="ref129"></nolink> <nolink nlid="nl55" bibid="bib28" firstref="ref130"></nolink> <nolink nlid="nl56" bibid="bib29" firstref="ref131"></nolink>
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  Data: Appearing Smart, Confident and Motivated: A Lens Model Approach to Judgment Accuracy in an Educational Setting
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  Data: <searchLink fieldCode="AR" term="%22Caroline+V%2E+Bhowmik%22">Caroline V. Bhowmik</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-3754-4135">0000-0003-3754-4135</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mitja+D%2E+Back%22">Mitja D. Back</searchLink><br /><searchLink fieldCode="AR" term="%22Steffen+Nestler%22">Steffen Nestler</searchLink><br /><searchLink fieldCode="AR" term="%22Friedrich-Wilhelm+Schrader%22">Friedrich-Wilhelm Schrader</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Social+Psychology+of+Education%3A+An+International+Journal%22"><i>Social Psychology of Education: An International Journal</i></searchLink>. 2025 28(1).
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 34
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Evaluative+Thinking%22">Evaluative Thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Attitudes%22">Teacher Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Preservice+Teachers%22">Preservice Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Nonverbal+Communication%22">Nonverbal Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Cues%22">Cues</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Motivation%22">Student Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligence%22">Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Assistive+Technology%22">Assistive Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Bias%22">Gender Bias</searchLink>
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  Data: 10.1007/s11218-025-10057-1
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  Data: 1381-2890<br />1573-1928
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  Data: Which behavioral and visual information do teachers rely on when judging relevant characteristics of their students and which cues should they rely on? Drawing on Brunswik's Lens Model (Perception and the representative design of psychological experiments, University of California Press, 1956. https://doi.org/10.1525/9780520350519), we investigated the role of students' expression of nonverbal behavioral cues (e.g., friendly facial expression) and physical appearance (e.g., wearing eyeglasses) and how this information is utilized during the judgment process by pre-service teachers and psychology students (N = 102). Perceivers provided ratings of students' (N = 45) academic self-concept, intelligence and motivation in brief nonverbal video clips showing one student each in a physics classroom. Numerous behavioral and physical cues (in total 165) were extracted from the stimulus material by two independent raters. Perceivers achieved highest accuracy for students' motivation, whereas intelligence was judged with the lowest accuracy. Lens model parameter analysis indicated that perceivers strongly relied on students' sex, an attentive and self-assured facial expression, and whether or not a student was wearing eyeglasses in their judgments. Cues that were actually related to students' characteristics, on the other hand, involved students' sex, a masculine and distinctive appearance, and a tensed as well as friendly facial expression. An overall favorable judgment for boys points into the direction of a gender bias. Implications for our understanding of teacher judgment processes and outcomes are discussed.
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        Value: 10.1007/s11218-025-10057-1
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      – SubjectFull: Evaluative Thinking
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Teacher Attitudes
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      – SubjectFull: College Students
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      – SubjectFull: Student Characteristics
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      – SubjectFull: Gender Bias
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    Titles:
      – TitleFull: Appearing Smart, Confident and Motivated: A Lens Model Approach to Judgment Accuracy in an Educational Setting
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            – D: 01
              M: 12
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1381-2890
            – Type: issn-electronic
              Value: 1573-1928
          Numbering:
            – Type: volume
              Value: 28
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Social Psychology of Education: An International Journal
              Type: main
ResultId 1