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Motivational and Self-Regulated Learning Profiles of Students Taking a Foundational Engineering Course

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Title: Motivational and Self-Regulated Learning Profiles of Students Taking a Foundational Engineering Course
Language: English
Authors: Nelson, Katherine G., Shell, Duane F., Husman, Jenefer, Fishman, Evan J., Soh, Leen-Kiat
Source: Journal of Engineering Education. Jan 2015 104(1):74-100.
Availability: Wiley Periodicals, Inc. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Peer Reviewed: Y
Page Count: 27
Publication Date: 2015
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Metacognition, Engineering Education, Profiles, Barriers, Majors (Students), Student Motivation, Correlation, Gender Differences, Computer Science Education, Student Attitudes, Undergraduate Students
DOI: 10.1002/jee.20066
ISSN: 1069-4730
Abstract: Background: Technical, nonengineering required courses taken at the onset of an engineering degree provide students a foundation for engineering coursework. Students who perform poorly in these foundational courses, even in those tailored to engineering, typically have limited success in engineering. A profile approach may explain why these courses are obstacles for engineering students. This approach examines the interaction among motivation and self-regulation constructs. Purpose (Hypothesis): This project sought to determine what motivational and self-regulated learning profiles engineering students adopt in foundational courses. We hypothesized that engineering students would adopt profiles associated with maladaptive motivational beliefs and self-regulated learning behaviors. The effects of profile adoption on learning and differences associated with student major, minor, and gender were analyzed. Design/Method: Five hundred and thirty-eight students, 332 of them engineering majors, were surveyed on motivation and self-regulation variables. Data were analyzed from a learner-centered profile approach using cluster analysis. Results: We obtained a five-cluster learning profile solution. Approximately 83% of engineering students enrolled in an engineering-tailored foundational computer science course adopted maladaptive profiles. These students learned less than those who adopted adaptive learning profiles. Profile adoption depended on whether a student was considering a major or minor in computer science or not. Conclusions: Findings indicate the motivational and self-regulated learning profiles that engineering students adopt in foundational courses, why they do so, and what profile adoption means for learning. Our findings can guide instructors in providing motivational beliefs and self-regulated learning scaffolds in the classroom.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1255329
Database: ERIC
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  Value: <anid>AN0100548915;6m401jan.15;2018Jun29.11:57;v2.2.500</anid> <title id="AN0100548915-1">Motivational and Self-Regulated Learning Profiles of Students Taking a Foundational Engineering Course. </title> <p>Background: Technical, nonengineering required courses taken at the onset of an engineering degree provide students a foundation for engineering coursework. Students who perform poorly in these foundational courses, even in those tailored to engineering, typically have limited success in engineering. A profile approach may explain why these courses are obstacles for engineering students. This approach examines the interaction among motivation and self‐regulation constructs. Purpose (Hypothesis): This project sought to determine what motivational and self‐regulated learning profiles engineering students adopt in foundational courses. We hypothesized that engineering students would adopt profiles associated with maladaptive motivational beliefs and self‐regulated learning behaviors. The effects of profile adoption on learning and differences associated with student major, minor, and gender were analyzed. Design/Method: Five hundred and thirty‐eight students, 332 of them engineering majors, were surveyed on motivation and self‐regulation variables. Data were analyzed from a learner‐centered profile approach using cluster analysis. Results: We obtained a five‐cluster learning profile solution. Approximately 83% of engineering students enrolled in an engineering‐tailored foundational computer science course adopted maladaptive profiles. These students learned less than those who adopted adaptive learning profiles. Profile adoption depended on whether a student was considering a major or minor in computer science or not. Conclusions: Findings indicate the motivational and self‐regulated learning profiles that engineering students adopt in foundational courses, why they do so, and what profile adoption means for learning. Our findings can guide instructors in providing motivational beliefs and self‐regulated learning scaffolds in the classroom.</p> <p>learning profiles; foundational courses; motivation; self‐regulation</p> <p>Many factors influence engineering students' success. Recent research has focused on several aspects of undergraduate students' motivation for persevering (e.g., Hutchison, Follman, Sumpter, & Bodner, [<reflink idref="bib32" id="ref1">32</reflink>] ; Jones, Paretti, Hein, & Knott, [<reflink idref="bib33" id="ref2">33</reflink>] ). The goal of this study was to consider how engineering students' approaches to learning in a foundational course foster or impede their success. Our examination of students' beliefs about themselves and the course, and how these beliefs motivate their self‐regulated learning can create a comprehensive picture of engineering students' successes and struggles in classroom learning. Our findings can help faculty and researchers see how students' motivational beliefs and self‐regulated learning behaviors work together to influence achievement, and aid educators as they encourage students toward academic success. The context of our research is a foundational course required of most engineering students: introductory computer science.</p> <hd id="AN0100548915-2">Foundational Courses</hd> <p>Most engineering programs require students to take foundational technology, science, and mathematics courses at the beginning of their undergraduate studies, often as early as their first semester. These foundational courses provide necessary scaffolding towards subsequent technical coursework in the students' major field. The content covered in these foundational courses is critical to subsequent understanding of engineering subjects. Each university or college approaches instruction in these foundational courses differently. Some programs offer them within the engineering program itself, whereas others work with science, mathematics, and computer science departments to specifically tailor their courses towards engineering (Sheppard, Macatangay, Colby, & Sullivan, [<reflink idref="bib62" id="ref3">62</reflink>] ).</p> <p>The first year is when students are more likely to drop out of engineering – with close to 35% leaving introductory, foundational courses (Gainen, [<reflink idref="bib21" id="ref4">21</reflink>] ). Budny, Bjedov, and LeBold ([<reflink idref="bib8" id="ref5">8</reflink>] ) used the term “high risk” to describe such courses in technical and scientific disciplines because they may inadvertently eliminate those first‐ and second‐year engineering students who perform poorly in them. These courses also have been referred to as gatekeeper courses (Gainen, [<reflink idref="bib21" id="ref6">21</reflink>] ) or barrier courses (Suresh, [<reflink idref="bib66" id="ref7">66</reflink>] ) because poor performance can be a barrier to continued pursuit of engineering. First‐year grade point average (GPA) is a strong indicator of persistence beyond the first year of engineering (French, Immekus, & Oakes, [<reflink idref="bib19" id="ref8">19</reflink>] ; Veenstra, Dey, & Herrin, [<reflink idref="bib71" id="ref9">71</reflink>] ) and of degree completion (Adelman, [<reflink idref="bib2" id="ref10">2</reflink>] ). Low grades in foundational courses can contribute to lower GPA and lead students to move to other degree programs, whereas successful completion of foundational courses promotes successful completion of an engineering program (Budny et al., [<reflink idref="bib8" id="ref11">8</reflink>] ).</p> <p>Due in large part to national concern over the small number of engineering students and their retention (National Research Council, [<reflink idref="bib39" id="ref12">39</reflink>] ), administrators and faculty have considered how to best support first‐ and second‐year engineering students' persistence and success in these courses, and research has begun to identify how instructors and programs can support success in courses and degree completion. Froyd and Ohland ([<reflink idref="bib20" id="ref13">20</reflink>] ) found that when engineering programs take an integrated approach and link engineering with other required, nonmajor courses, retention in engineering increases. Sathianathan et al. ([<reflink idref="bib48" id="ref14">48</reflink>] ) argued the need to contextualize courses by demonstrating that engineering students who are given engineering applied problems and projects in calculus performed better than students not given engineering applied problems. Miller et al. ([<reflink idref="bib38" id="ref15">38</reflink>] , [<reflink idref="bib37" id="ref16">37</reflink>] ) found that adding exercises in creative thinking skills to course content improved achievement and learning in introductory computer science courses, including a foundational computer science course for engineering students. In addition to pedagogical approaches, engineering educators have begun using motivational theories to advance understanding of what motivates engineering students to persist and achieve in the field and what curricular interventions would be the most beneficial in reducing engineering student attrition (Jones et al., [<reflink idref="bib33" id="ref17">33</reflink>] ) and increasing content understanding (Stump, Hilpert, Husman, Chung, & Kim, [<reflink idref="bib65" id="ref18">65</reflink>] ).</p> <p>This study examined the motivational and self‐regulated learning implications of a foundational introductory computer science (CS1‐level) course at a large Midwestern state university as part of a National Science Foundation‐funded initiative called “Renaissance Computing” (Soh et al., [<reflink idref="bib64" id="ref19">64</reflink>] ). A suite of parallel introductory CS1 courses was created, and each course was tailored for students from different engineering, science, business, and computer science majors. For the engineering course, students were taught programming using Matlab, and the course content in the course was tailored towards engineering applications. To understand the many ways that student motivational and self‐regulated learning might be influenced by these tailored foundational computer science courses, we used a profile approach to examine students' motivation and self‐regulation.</p> <hd id="AN0100548915-3">Profile Approach</hd> <p>Recent research has led to understanding students' motivational and self‐regulated learning and to exploring their implications for learning within various fields (Bandura, [<reflink idref="bib5" id="ref20">5</reflink>] ; Boekaerts & Cascallar, [<reflink idref="bib7" id="ref21">7</reflink>] ; Eccles & Wigfield, [<reflink idref="bib16" id="ref22">16</reflink>] ; Pekrun & Linnenbrink‐Garcia, [<reflink idref="bib41" id="ref23">41</reflink>] ; Zimmerman & Schunk, [<reflink idref="bib79" id="ref24">79</reflink>] ). Self‐regulated learning in educational research in engineering (Hilpert et al., [<reflink idref="bib26" id="ref25">26</reflink>] ), computer science (Shell, Hazley, Soh, Ingraham, & Ramsay, [<reflink idref="bib57" id="ref26">57</reflink>] ), science (Pugh, Linnenbrink‐Garcia, Koskey, Stewart, & Manzey, [<reflink idref="bib45" id="ref27">45</reflink>] ), middle and high school (Wigfield, Byrnes, & Eccles, [<reflink idref="bib75" id="ref28">75</reflink>] ), and post‐secondary education (Acee & Weinstein, [<reflink idref="bib1" id="ref29">1</reflink>] ) has consistently demonstrated relationships between motivation constructs and students' approach to learning both in and out of the classroom.</p> <p>Consideration of multiple aspects of students' motivation and their approach to learning is especially important for engineering education research. Engineering, like all disciplines, requires students to engage in self‐regulated learning inside (e.g., note taking, question asking) and outside of the classroom (e.g., studying), and requires students to persist, even in the face of failure. In these learning situations, students' motivational beliefs (Jones et al., [<reflink idref="bib33" id="ref30">33</reflink>] ) and self‐regulated learning behaviors (Hilpert et al., [<reflink idref="bib26" id="ref31">26</reflink>] ) influence their achievement and retention in the field. In general, engineering educators and researchers need to better understand how to enhance engineering students' motivation and self‐regulated learning.</p> <p>Theorists have discussed the complex and reciprocal relationships between motivation and self‐regulation constructs (Shell et al., [<reflink idref="bib56" id="ref32">56</reflink>] ). Prior work in these fields typically has examined individual motivation and self‐regulation constructs and has often examined them in isolation or, at most, considered the ways individual variables interact (McInerney & Van Etten, [<reflink idref="bib36" id="ref33">36</reflink>] ). Recently, researchers have begun to examine the complex reciprocity among motivation and self‐regulation variables (Schunk & Zimmerman, [<reflink idref="bib53" id="ref34">53</reflink>] ; Shell & Husman, [<reflink idref="bib59" id="ref35">59</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref36">61</reflink>] ) using the profile approach. The profile approach entails understanding the complex and reciprocal relationships between motivational beliefs and self‐regulated learning using an integrated, multivariate approach (Conley, [<reflink idref="bib12" id="ref37">12</reflink>] ; Guthrie, Coddington, & Wigfield, [<reflink idref="bib23" id="ref38">23</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref39">61</reflink>] ). Profiles of motivation and self‐regulation represent coordinated patterns of motivational beliefs and self‐regulated learning behaviors. Using the profile approach, researchers can consider interactions among many independent, well‐established psychological constructs; they build on over 30 years of psychological research on the individual constructs while considering the specific engineering education context.</p> <hd id="AN0100548915-4">Theoretical Framework</hd> <p>Motivation research has established that students' engagement, effort, persistence, and approaches to learning are influenced by many beliefs, needs, and perceptions (Schunk, Pintrich, & Meece, [<reflink idref="bib52" id="ref40">52</reflink>] ). Examples of key constructs that influence learning and engagement are goals, self‐efficacy, expectancies, and affect or emotion. Similarly, self‐regulated learning involves a group of behaviors and cognitions, or mental processes, where successful students employ many self‐regulated learning behaviors in their education (Husman & Corno, [<reflink idref="bib28" id="ref41">28</reflink>] ), like the good strategy user of Pressley, Borkowski, and Schneider ([<reflink idref="bib44" id="ref42">44</reflink>] ).</p> <p>A profile, as shown in Figure [NaN] , considers the influence of motivation and self‐regulation constructs. The boxes represent the different motivational beliefs and self‐regulated learning construct variables, and the circle represents the profile. A profile identifies coherent patterns of motivations, cognitions, and self‐regulatory behaviors that inform a learner‐centered approach to learning and instruction. Because a profile approach examines combinations of constructs, it requires methods that, as Ainley ([<reflink idref="bib3" id="ref43">3</reflink>] ) noted, “preserve the integrity of the combinations” (p. 396). These methods include factor analysis (Entwistle & McCune, [<reflink idref="bib18" id="ref44">18</reflink>] ), canonical correlation (Shell & Husman, [<reflink idref="bib59" id="ref45">59</reflink>] ), and cluster analysis (Conley, [<reflink idref="bib12" id="ref46">12</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref47">61</reflink>] ).</p> <p>Profile research has accelerated as researchers have seen the benefits of the profile approach (Chen, [<reflink idref="bib10" id="ref48">10</reflink>] ; Conley, [<reflink idref="bib12" id="ref49">12</reflink>] ; Daniels et al., [<reflink idref="bib13" id="ref50">13</reflink>] ; Guthrie et al., [<reflink idref="bib23" id="ref51">23</reflink>] ; Schwinger, Steinmayr, & Spinath, [<reflink idref="bib54" id="ref52">54</reflink>] ). A group of replicable profiles is emerging. Shell and Husman ([<reflink idref="bib59" id="ref53">59</reflink>] ), using canonical correlation, identified five profiles of college students' motivational and self‐regulated learning. These were a strategic profile that fits previous descriptions of highly motivated, strategic, self‐regulated students (Boekaerts & Cascallar, [<reflink idref="bib7" id="ref54">7</reflink>] ); a knowledge‐building profile that fits previous descriptions of an intrinsically motivated, knowledge‐building (Scardamalia & Bereiter, [<reflink idref="bib51" id="ref55">51</reflink>] ), autonomous (Reeve, Deci, & Ryan, [<reflink idref="bib47" id="ref56">47</reflink>] ), or mastery oriented (Pintrich, [<reflink idref="bib42" id="ref57">42</reflink>] ) student; an apathetic profile that fits previous descriptions of an amotivational (Reeve et al., [<reflink idref="bib47" id="ref58">47</reflink>] ) or apathetic (Tait & Entwistle, [<reflink idref="bib68" id="ref59">68</reflink>] ) student; a surface‐learning profile that fits previous descriptions of an extrinsically motivated rote or surface‐learning student (Entwistle & McCune, [<reflink idref="bib18" id="ref60">18</reflink>] ); and a learned helpless profile that fits previous descriptions of a learned helpless (consistently failing student who eventually gives up) student (Dweck, [<reflink idref="bib14" id="ref61">14</reflink>] ). Using cluster analysis, Shell and Soh ([<reflink idref="bib61" id="ref62">61</reflink>] ) replicated these five profiles with college students in computer science courses. Prior research in the student‐approaches‐to‐learning tradition using factor analysis has also identified a similar five‐profile solution (Entwistle & McCune, [<reflink idref="bib18" id="ref63">18</reflink>] ; Tait & Entwistle, [<reflink idref="bib68" id="ref64">68</reflink>] ; Vermunt & Vermetten, [<reflink idref="bib72" id="ref65">72</reflink>] ).</p> <p>Shell et al. ([<reflink idref="bib56" id="ref66">56</reflink>] ) and Entwistle and McCune ([<reflink idref="bib18" id="ref67">18</reflink>] ) argued that within specific classes at any point in time, students adopt a profile as a function of the subject matter and classroom context; and research has shown that there may be some students who adopt each of the five profiles (Shell & Soh, [<reflink idref="bib61" id="ref68">61</reflink>] ). Although students may adopt a preferred profile, adoption of profiles remains dynamic. Students may shift profiles in different courses and subjects and in response to contextual factors and personal reactions within a course. That is, each course provides a unique set of experiences that can influence profile adoption. Hayenga and Corpus ([<reflink idref="bib24" id="ref69">24</reflink>] ) and Tuominen‐Soini, Salmela‐Aro, and Niemivirta ([<reflink idref="bib69" id="ref70">69</reflink>] ) found that students' profile adoption was relatively stable throughout an academic school year and that about one‐third of the students changed a profile. Linnenbrink‐Garcia ([<reflink idref="bib35" id="ref71">35</reflink>] ) found that students' profile adoption was affected by classroom interventions researchers designed to establish different types of goal orientations. These studies examined students in K‐12 settings.</p> <p>The extent to which student profile adoption changes in college courses is unknown. Shell and Soh ([<reflink idref="bib61" id="ref72">61</reflink>] ) found that profile adoption differed between college students who were majors or nonmajors in the course's subject and that the distribution of profiles differed by course. These findings suggest that although students tend to adopt stable profiles within a course, students can adopt different profiles according to the classroom environment and their views of the classroom.</p> <hd id="AN0100548915-5">Profile Variables</hd> <p>The different self‐regulation and motivation variables being utilized in this study to conduct a profile approach of engineering student learning in a foundational course are defined below.</p> <hd id="AN0100548915-6">Self‐regulation variables</hd> <p>Self‐regulation is a self‐directed process through which learners actively participate in their own learning by using task‐related skills and supports (Zimmerman & Schunk, [<reflink idref="bib79" id="ref73">79</reflink>] ). Four variables of self‐regulation were used in this study. The first variable is general metacognitive self‐regulation. Students who are self‐regulating engage in active planning, monitoring, and evaluation of their learning and apply general learning strategies to accomplish these. These students have been called good strategy users (Pressley et al., [<reflink idref="bib44" id="ref74">44</reflink>] ). The second variable comes from the knowledge‐building approach to learning proposed by Scardamalia and Bereiter ([<reflink idref="bib50" id="ref75">50</reflink>] , [<reflink idref="bib51" id="ref76">51</reflink>] ). Central to this approach is the idea that meaningful learning involves the production of knowledge rather than the reproduction of knowledge. Knowledge building is characterized by going above and beyond surface‐level learning (e.g., memorization) by connecting new information to existing knowledge, integrating new knowledge across topics, and pursuing deep understanding of course material. The third variable of self‐regulation was drawn from research examining more maladaptive self‐regulated learning strategies (Vermunt & Vermetten, [<reflink idref="bib72" id="ref77">72</reflink>] ; Wolters, [<reflink idref="bib76" id="ref78">76</reflink>] ). Lack of regulation examines students' confusion and difficulty in effectively studying and self‐regulating along with their need for excessive support from others. The final aspect is student engagement with the class as reflected through active participation and effort, including active course involvement such as question asking (Scardamalia & Bereiter, [<reflink idref="bib49" id="ref79">49</reflink>] ) and studying (study time and perceived study effort; Shell & Husman, [<reflink idref="bib59" id="ref80">59</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref81">61</reflink>] ).</p> <hd id="AN0100548915-7">Motivation and affect variables</hd> <p>When a student is purposeful in applying effort towards personal goals, researchers would describe that student as being motivated (Schunk et al., [<reflink idref="bib52" id="ref82">52</reflink>] ). The motivation variables in this study were class goal orientation (Dweck & Leggett, [<reflink idref="bib15" id="ref83">15</reflink>] ; Elliot, Murayama, & Pekrun, [<reflink idref="bib17" id="ref84">17</reflink>] ), future time perspective (Husman & Lens, [<reflink idref="bib29" id="ref85">29</reflink>] ), and course emotion or affect (Linnenbrink, [<reflink idref="bib34" id="ref86">34</reflink>] ; Pekrun, Frenzel, Goetz, & Perry, [<reflink idref="bib40" id="ref87">40</reflink>] ; Pekrun & Linnenbrink‐Garcia, [<reflink idref="bib41" id="ref88">41</reflink>] ).</p> <p>Class goal orientation measures were based on a framework that follows a tradition in goal theory that focused on the goals students set for courses (Senko, Hulleman, & Harackiewicz, [<reflink idref="bib55" id="ref89">55</reflink>] ; Shell et al., [<reflink idref="bib56" id="ref90">56</reflink>] ). Goals are examined in three dimensions (learning, performance, and task); each dimension has an approach as well as an avoid component. Researchers' interest when studying learning approach goals is directed at students' learning new knowledge or gaining competence; learning approach goals are consistent with most past formulations of learning or mastery goals presented in the literature (Dweck & Leggett, [<reflink idref="bib15" id="ref91">15</reflink>] ; Senko et al., [<reflink idref="bib55" id="ref92">55</reflink>] ). Learning avoid goals reflect a student's active desire to not learn material or gain anything from the course (Shell & Soh, [<reflink idref="bib61" id="ref93">61</reflink>] ). A student who does not care about a course might set a goal to just complete course assignments without retaining any of the course content. Performance approach goals reflect a student's desire to obtain favorable judgments of his or her abilities by others or to perform better than others in the class. Performance avoid goals reflect a student's desire to avoid negative judgments of his or her ability or do worse relative to others in the class. Task or work approach goals, also called outcome goals, reflect a student's desire to perform a task well or achieve a high level (Grant & Dweck, [<reflink idref="bib22" id="ref94">22</reflink>] ). Task or work avoid goals reflect a student's desire to complete the class with as little time and effort as possible (Ames, [<reflink idref="bib4" id="ref95">4</reflink>] ; Wolters, [<reflink idref="bib76" id="ref96">76</reflink>] ).</p> <p>Future time perspective (FTP) is a set of psychological constructs that together explain some of the differences we see in students' tendency to plan for the future, delay gratification, and make responsible life choices (Zimbardo & Boyd, [<reflink idref="bib78" id="ref97">78</reflink>] ). Two components of FTP were examined in this study: perception of instrumentality (PI) and career connectedness. Endogenous perceived instrumentality reflects a student's perception that the task at hand is instrumental to achieving his or her future long‐term goals, where the task and the goal are tied to his or her emerging identity. For example, a student may see the task as instrumental to becoming a successful engineer. Exogenous PI reflects a student's perception that the present task is instrumental for his or her future long‐term goals, but not as it relates to his or her emerging identity. Thus students may see the task as instrumental to getting to their future goal (e.g., completing an English literature general studies course), but not because they think completing the task is essential to their emerging identity as a successful engineer (Husman & Lens, [<reflink idref="bib29" id="ref98">29</reflink>] ). Career connectedness refers to the student's general ability to make connections between present activities and some future goal. It is one of the most predictive components in engineering education contexts (Hilpert et al., [<reflink idref="bib26" id="ref99">26</reflink>] ).</p> <p>Course affect (or emotion) involves students' general feelings and reactions to the course. Positive affect increases students' engagement in academic work and supports more adaptive self‐regulation (Pekrun & Linnenbrink–Garcia, [<reflink idref="bib41" id="ref100">41</reflink>] ). Negative affect decreases motivation and leads to maladaptive self‐regulation (Shell & Soh, [<reflink idref="bib61" id="ref101">61</reflink>] ).</p> <hd id="AN0100548915-8">Research Aims</hd> <p>Our research sought to determine what motivational and self‐regulated learning profiles engineering students adopt in foundational courses. To identify learning profiles, we took the learner‐centered approach that has been utilized in most recent profile studies (Chen, [<reflink idref="bib10" id="ref102">10</reflink>] ; Conley, [<reflink idref="bib12" id="ref103">12</reflink>] ; Daniels et al., [<reflink idref="bib13" id="ref104">13</reflink>] ; Schwinger et al., [<reflink idref="bib54" id="ref105">54</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref106">61</reflink>] ; Tuominen‐Soini et al., [<reflink idref="bib69" id="ref107">69</reflink>] ). The learner‐centered approach determines if groups of students can be identified who share common motivational and self‐regulated learning characteristics. We used cluster analysis, an analytic method that groups people by patterns of variables, to identify learning profiles. We included both motivation and self‐regulation variables within the cluster analysis to determine learning profiles. The prior studies that have included both motivation and self‐regulation variables to determine learning profiles (Entwistle & McCune, [<reflink idref="bib18" id="ref108">18</reflink>] ; Linnenbrink‐Garcia, [<reflink idref="bib35" id="ref109">35</reflink>] ; Schwinger et al., [<reflink idref="bib54" id="ref110">54</reflink>] ; Shell & Husman, [<reflink idref="bib59" id="ref111">59</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref112">61</reflink>] ; Tait & Entwistle, [<reflink idref="bib68" id="ref113">68</reflink>] ; Vermunt & Vermetten, [<reflink idref="bib72" id="ref114">72</reflink>] ) have typically identified a five‐profile solution that corresponds to the Shell and Husman ([<reflink idref="bib59" id="ref115">59</reflink>] ) profiles. Because this five‐profile solution has been observed in other students within the student‐approaches‐to‐learning tradition, we hypothesized that a similar five‐profile solution would emerge in the cluster analysis.</p> <p>Relying on recent findings by Shell and Soh ([<reflink idref="bib61" id="ref116">61</reflink>] ), who examined students' learning profiles in multiple computer science courses during one semester, we hypothesized that engineering students who took the foundational course in this study would be more likely to adopt maladaptive profiles like the apathetic and learned helpless profiles identified by Shell and Husman ([<reflink idref="bib59" id="ref117">59</reflink>] ). Consistent with prior research of Shell and Soh ([<reflink idref="bib61" id="ref118">61</reflink>] ), we hypothesized that the learning profile adopted by students would significantly affect their learning such that students who adopt the strategic and knowledge‐building profiles should have higher retention of course content. Also, utilizing the findings by Shell and Soh ([<reflink idref="bib61" id="ref119">61</reflink>] ), we hypothesized that engineering students who were considering a major or minor in computer science as their engineering major or along with their engineering major would be more likely to adopt the adaptive strategic and knowledge‐building profiles.</p> <hd id="AN0100548915-9">Method</hd> <hd id="AN0100548915-10">Participants</hd> <p>Students volunteered as part of a larger evaluation of an NSF‐sponsored effort to revise the undergraduate computer science curriculum at a large Midwestern state university (Soh et al., [<reflink idref="bib64" id="ref120">64</reflink>] ). Instead of a single introductory computer science course (CS1‐level), this project developed a suite of parallel introductory CS1‐level courses. Core content was the same for all courses; but courses were tailored for students from different majors with different programming languages and lab exercises that were consistent with the students' major field. CS1‐Computer Science was for computer science majors who could be enrolled in computer science either through the College of Arts and Sciences or the College of Engineering. CS1‐Honors was for combined business and computer science honors program majors. CS1‐Mixed comprised half computer science majors who preferred the language (C++) used and half business and general science majors. CS1‐Engineering, which was the primary focus of this study, was for non‐computer science engineering majors (mechanical engineering, civil engineering, electrical engineering, etc.). The CS1‐Computer Science and CS1‐Honors courses were part of the major field for students majoring in computer science or the combined business and computer science honors program. The CS1‐Mixed course was part of the major field for computer science students and a foundational course that may or may not be required for students majoring in business or science disciplines. The CS1‐Engineering course was a required foundational course for the non‐computer science engineering majors. Students can receive credit for only one of the courses.</p> <p>The CS1‐Engineering course consisted of regular lectures (three hours per week), with five homework programming assignments and 13 weekly, 1.5‐hour, inquiry‐based, problem‐driven laboratory assignments that focused on engineering applications. The primary programming language was Matlab, a versatile language commonly used in engineering disciplines for various applications involving analysis of numerics. Matlab was chosen in consultation with engineering faculty. The topics covered included typical introductory computer science CS1‐level topics, such as repetition, selection, functions, character manipulations, arrays, I/O, file I/O, search, sorting, recursion, debugging, and problem solving, as well as more Matlab‐facilitated topics, such as vectors matrices, 2‐D plotting, and cell structures. Course lectures illustrated these topics with examples in engineering applications. See Soh et al. ([<reflink idref="bib64" id="ref121">64</reflink>] ) for more information on the tailoring of the courses.</p> <p>Participants were 538 students from this suite of introductory computer science courses over four semesters (439 men, 93 women, 6 unknown; 262 freshmen [first year], 143 sophomores [second year], 86 juniors [third year], 32 seniors [fourth year], 15 other or unknown). This participant sample was used for cluster analysis to determine the composition of student learning profiles. From this sample, there were 332 students in the CS1‐Engineering course (279 men, 50 women, 3 unknown; 118 freshmen, 109 sophomores, 70 juniors, 20 seniors, 6 other or unknown). This sample subset was used for subsequent analyses that focused on engineering majors.</p> <p>Race‐ethnicity information was not collected directly due to Institutional Review Board concerns about indirect identification because of low numbers of certain ethnicities. The CS1 courses, because they are required, reflect general demographics of the colleges and majors involved. The approximate demographic breakdowns for the semesters studied based on university enrollment records were: engineering majors: 92% White, 2% African American, 3% Asian, 3% Hispanic, 4% foreign; computer science majors: 87% White, 2% African American, 6% Asian, 5% Hispanic, 7% foreign; combined business and computer science honors program: 89% White, 0% African American, 9% Asian, 2% Hispanic, 0% foreign; and general business and other science majors: 77% White, 2% African American, 2% Asian, 3% Hispanic, 17% foreign.</p> <p>Student participation was voluntary, but participation throughout the study was generally high. Student participation in a pre‐survey given the first week of the course in the spring 2010, fall 2010, and fall 2011 semesters was almost 100% in all courses. The pre‐ to post‐survey retention for these three semesters was CS1‐Engineering, 79%; CS1‐Honors, 98%; CS1‐Computer Science, 60%; and CS1‐Mixed, 70%. Although no pre‐survey data were available for fall 2009, participation and retention were consistent with other semesters. There were no differences in demographic make‐up (gender or year in school) between those who completed the post‐survey and those who did not.</p> <hd id="AN0100548915-11">Data Sources</hd> <p>The motivation and self‐regulation measures used in this study are described below and correspond to the motivation and self‐regulation variables defined previously.</p> <hd id="AN0100548915-12">Self‐Regulation Measures</hd> <p>Self‐regulation was assessed with the Student Perceptions of Classroom Knowledge Building (SPOCK) scale. Additional validation information is available in Shell et al. ([<reflink idref="bib60" id="ref122">60</reflink>] ), Shell and Husman ([<reflink idref="bib59" id="ref123">59</reflink>] ), and Shell and Soh ([<reflink idref="bib61" id="ref124">61</reflink>] ). The instrument asks students about self‐regulated learning behavior within a specific course. The SPOCK measures four aspects of students' perceptions of their own self‐regulated learning.</p> <hd id="AN0100548915-13">Self‐regulated strategy use</hd> <p>Eight items of SPOCK assess the extent of student planning, goal setting, monitoring, and evaluation of studying and learning (e.g., “In this class, I try to determine the best approach for studying each assignment”; “In this class, I try to monitor my progress when I study”). These items assess strategic behaviors and study strategies typically associated with models of strategic self‐regulated learning (e.g., Pintrich, [<reflink idref="bib43" id="ref125">43</reflink>] ; Pressley et al., [<reflink idref="bib44" id="ref126">44</reflink>] ; Weinstein & Mayer, [<reflink idref="bib74" id="ref127">74</reflink>] )</p> <hd id="AN0100548915-14">Knowledge building</hd> <p>Nine items assess the extent of student exploration and interconnection of knowledge (e.g., “Whenever I learn something new in this class, I try to tie it to other facts and ideas that I already know”; “In this class, I focused on those topics that were personally meaningful to me”). Questions in this scale are based on the knowledge‐building and intentional learning models of Scardamalia and Bereiter ([<reflink idref="bib50" id="ref128">50</reflink>] , [<reflink idref="bib51" id="ref129">51</reflink>] ) and focus on going beyond the given material and on tying the information being learned to other courses and existing knowledge.</p> <hd id="AN0100548915-15">Lack of regulation</hd> <p>Ten items assess students' lack of understanding of how to study and their need for assistance and guidance in studying (e.g., “In this class, I couldn't figure out how I should study the material”; “In this class, I relied on someone else to tell me what to do”). This scale assessed behaviors similar to those in the lack of regulation orientation identified by Vermunt and Vermetten ([<reflink idref="bib72" id="ref130">72</reflink>] ).</p> <hd id="AN0100548915-16">Engagement</hd> <p>Two scales of SPOCK measure the extent of question asking in class (see Scardamalia & Bereiter, [<reflink idref="bib49" id="ref131">49</reflink>] ). The high‐level question asking scale (five items) assesses the extent to which students ask questions that extend or expand on the basic information being provided in the class (e.g., “In this class, I ask questions about things I am curious about”; “In this class, I ask questions to help me know more about the topics we are covering in class”). The low‐level question asking scale (four items) assesses the extent to which students ask questions to obtain or clarify basic course information (e.g., “In this class, I ask questions so that I can be sure I know the right answers for tests”; “In this class, I ask questions to be clear about what the instructor wants me to learn”).</p> <p>Study time was assessed using two scales measuring student self‐reported studying (Shell & Husman, [<reflink idref="bib58" id="ref132">58</reflink>] ; Shell & Husman, [<reflink idref="bib59" id="ref133">59</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref134">61</reflink>] ). Study time (one item) was assessed by asking students to indicate the average number of hours per week they spent studying for their computer science course on a 1‐to‐7 scale representing two‐hour units from 1 (less than two hours per week) to 7 (over 12 hours per week). In the United States, the phrase “studying for class” typically means activities done outside of the actual class time. Although unlikely, some students may have included in‐class time in their response. Perceived study effort (one item) was assessed by asking students to indicate their perception of the effort they put forth studying for their computer science course relative to most students on a five‐point Likert scale as follows: 1 (I put forth much less effort studying), 2 (I put forth somewhat less effort studying), 3 (I put forth about the same effort studying), 4 (I put forth somewhat more effort studying), 5 (I put forth much more effort studying).</p> <p>Students were asked to respond only for the course from which they were recruited and not for other courses or school in general. Students rated their frequency of the behaviors on a five‐point Likert scale as follows: 1 (almost never), 2 (seldom), 3 (sometimes), 4 (often), 5 (almost always). Scale scores were computed as the mean score of the scale items. Cronbach's alpha reliability estimates for the self‐regulated strategy use, knowledge building, lack of regulation, engagement high‐level question asking, and engagement low‐level question asking scales were.89,.90,.85,.90, and.85, respectively.</p> <hd id="AN0100548915-17">Motivation and Affect Measures</hd> <p>Three motivation and affect constructs were utilized: class goal orientation, future time perspective, and course affect.</p> <p>Students' class goal orientation was measured using the instrument in Shell and Soh ([<reflink idref="bib61" id="ref135">61</reflink>] ). The instrument is an extension of the Shell and Husman ([<reflink idref="bib59" id="ref136">59</reflink>] ) instrument that was based on the goal framework described in Shell et al. ([<reflink idref="bib56" id="ref137">56</reflink>] ). Three different dimensions were assessed.</p> <hd id="AN0100548915-18">Learning</hd> <p>The learning approach scale assesses (five items) goals for developing long‐term, deep understanding of course content and skills (e.g., “Learning new knowledge or skills in the class just for the sake of learning them”; “Really understanding the course material”). Learning avoid (four items) assesses deliberate avoidance of long‐term learning or retention of course content (e.g., “Getting a grade whether I remember anything beyond that or not”; “Getting this course done even though I don't care about the content”).</p> <hd id="AN0100548915-19">Performance</hd> <p>The performance approach scale assesses (six items) normative performance relative to other students and favorable assessments of ability by others for ego protection (e.g., “Doing better than the other students in the class on tests and assignments”; “Impressing the instructor with your performance”). Performance avoid (three items) assesses avoiding negative performance evaluations and unfavorable assessments of ability by others (e.g., “Keeping others from thinking you are dumb”; “Avoiding looking like you don't understand the class material”).</p> <hd id="AN0100548915-20">Task</hd> <p>The task approach scale assesses efforts to accomplish high achievement and do well on class assignments and activities without reference to normative comparisons (e.g., “Getting a good grade in the class”; “Getting high grades on tests and other graded assignments”). Task avoid (three items; also called work avoidance goals; see Ames, [<reflink idref="bib4" id="ref138">4</reflink>] , and Wolters, [<reflink idref="bib76" id="ref139">76</reflink>] ) assesses deliberate intention to put forth minimal effort in the course (e.g., “Getting a passing grade with as little studying as possible”; “Getting through the course with the least amount of time and effort”).</p> <p>Students rated goals on a five‐point Likert scale: 1 (very unimportant), 2 (unimportant), 3 (neither important nor unimportant), 4 (important), 5 (very important). Scores were computed as the mean score of the items in each scale. Cronbach's alpha reliability estimates for the learning approach, learning avoid, performance approach, performance avoid, task approach, and task avoid scales were.87,.86,.79,.85,.90, and.81, respectively. Additional instrument validation information is available in Shell and Husman ([<reflink idref="bib59" id="ref140">59</reflink>] ) and Shell and Soh ([<reflink idref="bib61" id="ref141">61</reflink>] ).</p> <p>Future time perspective was measured by two instruments, perceptions of instrumentality and career connectedness.</p> <hd id="AN0100548915-21">Perceptions of instrumentality (PI)</hd> <p>This instrument measures student perceptions of the instrumental relationship between their specific course work and attaining STEM academic and career goals. Additional validation information is available in Husman and Lens ([<reflink idref="bib29" id="ref142">29</reflink>] ), Husman et al. ([<reflink idref="bib30" id="ref143">30</reflink>] ), and Shell and Soh ([<reflink idref="bib61" id="ref144">61</reflink>] ). The four items of the endogenous PI scale assess the instrumentality for learning the course material (e.g., “I will use the information I learn in this CS1 class in the future”; “What I learn in this CS1 class will be important for my future occupational success”). The four items of the exogenous PI scale assess the instrumentality for course grades and achievement (e.g., “The grade I get in this CS1 class will not be important for my future academic success” (reverse scored); “The grade I get in this CS1 class will affect my future”). Students indicated their agreement with each question using a five‐point Likert scale as follows: 1 (strongly disagree), 2 (disagree), 3 (neutral), 4 (agree), 5 (strongly agree). Endogenous and exogenous PI scale scores were computed as the mean of the items in each scale, with negative items reverse scored. Cronbach's alpha reliability estimates for the endogenous and exogenous PI scales were.93 and.64, respectively.</p> <hd id="AN0100548915-22">Career connectedness</hd> <p>The 11 items of this instrument assess connections between a student's present and their future career goals (e.g., “One should be taking steps today to help realize future career goals”; “What will happen in the future in my career is an important consideration in deciding what action to take now”). The instrument was an adaptation of the Future Time Perspective Scale connectedness subscale from Husman and Shell ([<reflink idref="bib31" id="ref145">31</reflink>] ). Additional instrument validation information is available in Shell and Husman ([<reflink idref="bib58" id="ref146">58</reflink>] ) and Shell and Soh ([<reflink idref="bib61" id="ref147">61</reflink>] ). Item wording was changed from asking about the general future to specifically asking about the future in the context of careers. Students indicated their agreement with each question using a five‐point Likert scale as follows: 1 (strongly disagree), 2 (disagree), 3 (neutral), 4 (agree), 5 (strongly agree). The career connectedness score was computed as the mean of the items in the scale, with negative items reverse scored. Cronbach's alpha reliability estimate for the scale was.88.</p> <p>Course affect assessed the positive and negative emotions students have for a particular class.</p> <hd id="AN0100548915-23">Positive/Negative affect</hd> <p>This construct is measured by a modified version of the Positive and Negative Affect Scale (PANAS; as used in Shell & Husman, [<reflink idref="bib59" id="ref148">59</reflink>] , and Shell & Soh, [<reflink idref="bib61" id="ref149">61</reflink>] ). Instrument validation information is available in Watson, Clark, and Tellegen ([<reflink idref="bib73" id="ref150">73</reflink>] ). Positive affect (10 items) assesses the frequency of experiencing positive emotions and feelings in the course (e.g., excited, proud). Negative affect (10 items) assesses the frequency of experiencing negative emotions and feelings in the course (e.g., frustrated, afraid, distressed). Students rated the frequency of experiencing each emotion or feeling on a five‐point scale as follows: 1 (a few times or not at all), 2 (occasionally, 25% of the time), 3 (quite often, 50% of the time), 4 (very often, 75% of the time), 5 (most of the time, 80% to 100% of the time). Positive and negative scale scores were computed as the mean of the items in each scale. Cronbach's alpha reliability estimates for the positive and negative affect scales were.91 and.90, respectively.</p> <hd id="AN0100548915-24">Knowledge Test</hd> <p>The suite of introductory computer science (CS1) courses from which participants were recruited included the same basic core computer science topics. To create a common measure of retention of knowledge of these core topics that could be used for all the courses, a web‐based 13‐item test of computational thinking and computer science knowledge that blends conceptual and problem‐solving questions was developed by computer science and engineering faculty members. The test measures content such as selection, looping, arrays, functions, algorithms, search, and sorting. The test was refined over the fall 2009, spring 2010, and fall 2010 semesters from an initial set of 26 items. The original 26 items were reduced to 18 on the basis of item redundancy because of concerns by the Computer Science and Engineering faculty about the time needed to administer the test. The 18‐item version was examined using item discrimination (percentage passing) and item‐total (point bi‐serial) correlations. Four items with poor discrimination in fall 2009 and spring 2010 were eliminated. A fifth item was eliminated due to low item‐total correlation of less than.30 and relatively poor discrimination. The final 13‐item version had reasonably strong psychometric properties. In fall 2010 and fall 2011, the Cronbach alpha reliability estimates were.78 with point bi‐serial correlations of.36 to.58 (2010) and.30 to.62 (2011); there were two exceptions below.26. The two items with poor point bi‐serial correlations are among the most difficult and cover important course topics not covered by other items. The knowledge test was separate from the regular course examinations and did not count toward course grades.</p> <hd id="AN0100548915-25">Procedures</hd> <p>Participants completed the questionnaire battery using the Survey Monkey web‐based survey program. Data were collected during four semesters (fall 2009, spring 2010, fall 2010, and fall 2011). Participants completed surveys and the knowledge test during proctored course laboratory periods in the final week of the semester or outside of the course (fall 2009 only). Missing data were handled with list‐wise deletion. Therefore, participants who had not completed all of the surveys tests, had their record deleted from the data. Missing data varied by analysis and ranged from 2% to 3% (eight or nine participants) for all analyses, except for knowledge test scores which had 17 (5%) participants missing.</p> <hd id="AN0100548915-26">Results</hd> <hd id="AN0100548915-27">Profile Determination</hd> <p>Cluster analysis was used to determine what learning profiles students exhibited in the CS‐1 courses. Cluster analysis was conducted with SPSS V.20, using the two‐step cluster procedure (Chiu, Fang, Chen, Wang, & Jeris, [<reflink idref="bib11" id="ref151">11</reflink>] ; Zhang, Ramakrishnon, & Livny, [<reflink idref="bib77" id="ref152">77</reflink>] ). This cluster procedure has two steps: a pre‐clustering to derive a set of small subclusters, and clustering of the resulting subclusters into final clusters. The pre‐clustering step uses a sequential clustering approach. Data records are scanned one by one, and the current case is either merged with a previously formed cluster or used to start a new cluster based on the distance criterion. The pre‐clustering procedure constructs a modified cluster feature (CF) tree; the CF tree contains levels of nodes, with each node containing a number of records. The clustering step takes these subclusters and input, and groups them into the desired number of clusters using an agglomerative hierarchical clustering method. Log‐likelihood was specified as the measure for cluster distance for pre‐clustering, and the Bayesian information criterion was specified as the clustering criterion for the clustering step. All variables were standardized prior to clustering, and 10% noise handling was used to eliminate the effect of extreme outliers.</p> <p>Cluster analysis is interpretive. Regardless of the extent to which statistical indicators are available to suggest the number of clusters present, the ultimate decision about what patterns are indicative of clusters relies on theoretical coherence as determined by the authors. The patterns of variables within a cluster must be explainable in the context of the theories and prior research on the constituent constructs. As we had hypothesized, we identified five clusters (learning profiles), a solution that corresponded to the five clusters identified by Shell and Husman ([<reflink idref="bib59" id="ref153">59</reflink>] ) and Shell and Soh ([<reflink idref="bib61" id="ref154">61</reflink>] ). To determine if a better solution could be identified, three‐, four‐, and six‐cluster solutions were tested. Goodness‐of‐fit indicators for the three‐, four‐, five‐, and six‐cluster solutions were.150,.138,.130, and.129, respectively, for sums of squares within group (SSE), which measures cohesions within clusters, with lower scores indicating better fit of the data;.042,.060,.067, and.070 for sums of squares between groups (SSB), which measures separation, with higher scores indicating better fit of the data; and.192,.202,.199, and.173 for average silhouette, which measures how tightly clusters group, with higher scores indicating better fit. Few differences between the indexes for the four‐, five‐, and six‐cluster solutions were found. The best overall balance among all indexes was achieved with the five‐cluster solution that had the second best fit indicator scores for all measures. Although the six‐cluster solution had the best SSE and SSB indicator scores, it had the worst silhouette indicator score. As discussed in the Introduction, the five‐cluster solution also has the best theoretical anchoring and prior research support; thus, it was preferred on theoretical grounds unless there was any strong statistical evidence for a better solution.</p> <p>A multivariate analysis of variance (MANOVA) was conducted to test whether motivation and self‐regulation variables significantly differed across the five clusters. The clusters were significantly different: Wilks's lambda =.059, F (<reflink idref="bib72" id="ref155">72</reflink>, 1996.01) = 29.29, p <.0001, partial eta‐squared =.508. Also, in univariate follow‐up tests, each individual variable was significantly different across the clusters (all p <.0001). Although all variables contribute to distinguishing the clusters, the most important variables for determining clusters were, in order from most important, learning avoid, positive affect, learning approach, knowledge building, strategy use, endogenous PI, high‐level question asking, lack of regulation, and task avoid.</p> <p>The clusters, which will subsequently be referred to as motivational and self‐regulated learning profiles, learning profiles, or profiles, are given in Table [NaN] and Table [NaN] . Table [NaN] lists the variable means for each profile, whereas the conceptual‐level differences of the variables between the profiles can be seen in Table [NaN] . As discussed in Shell and Husman ([<reflink idref="bib59" id="ref156">59</reflink>] ), Shell et al. ([<reflink idref="bib56" id="ref157">56</reflink>] ), and Shell and Soh ([<reflink idref="bib61" id="ref158">61</reflink>] ), and as shown in Tables [NaN] and [NaN] , learning profiles are distinguished by, at times, subtle differences. Two of the profiles are adaptive (Strategic and Knowledge‐Building profiles) and three are maladaptive (Apathetic, Surface Learning, and Learned Helpless).</p> <p>Variables in Learning Profiles</p> <p> <ephtml> <table><tr valign="bottom"><th align="left" /><th align="center">Learning profiles</th></tr><tr valign="bottom"><th align="left" /><th align="center">Adaptive</th><th align="center">Maladaptive</th></tr><tr valign="bottom"><th align="left">Variables</th><th align="center"><p>Strategic (n = 104)</p></th><th align="center"><p>Knowledge Building (n = 63)</p></th><th align="center"><p>Apathetic (n = 121)</p></th><th align="center"><p>Surface Learning (n = 102)</p></th><th align="center"><p>Learned Helpless (n = 139)</p></th></tr><tr valign="top"><td align="left">Self‐regulation</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Self‐regulated strategy use</td><td align="char" char=".">3.80</td><td align="char" char=".">2.56</td><td align="char" char=".">2.37</td><td align="char" char=".">3.08</td><td align="char" char=".">3.44</td></tr><tr valign="top"><td align="left">Knowledge building</td><td align="char" char=".">3.72</td><td align="char" char=".">3.20</td><td align="char" char=".">2.13</td><td align="char" char=".">3.03</td><td align="char" char=".">3.04</td></tr><tr valign="top"><td align="left">Lack of regulation</td><td align="char" char=".">2.37</td><td align="char" char=".">2.15</td><td align="char" char=".">3.28</td><td align="char" char=".">2.90</td><td align="char" char=".">3.28</td></tr><tr valign="top"><td align="left">Engagement</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">High‐level question asking</td><td align="char" char=".">3.59</td><td align="char" char=".">2.06</td><td align="char" char=".">2.06</td><td align="char" char=".">2.85</td><td align="char" char=".">2.97</td></tr><tr valign="top"><td align="left">Low‐level question asking</td><td align="char" char=".">3.50</td><td align="char" char=".">1.97</td><td align="char" char=".">2.25</td><td align="char" char=".">2.93</td><td align="char" char=".">3.07</td></tr><tr valign="top"><td align="left">Study time</td><td align="char" char=".">3.93</td><td align="char" char=".">1.89</td><td align="char" char=".">2.30</td><td align="char" char=".">3.17</td><td align="char" char=".">3.44</td></tr><tr valign="top"><td align="left">Perceived study effort</td><td align="char" char=".">3.83</td><td align="char" char=".">2.10</td><td align="char" char=".">2.55</td><td align="char" char=".">3.20</td><td align="char" char=".">3.35</td></tr><tr valign="top"><td align="left">Motivation and affect</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Class goal orientation</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Learning approach</td><td align="char" char=".">4.66</td><td align="char" char=".">4.41</td><td align="char" char=".">3.23</td><td align="char" char=".">3.55</td><td align="char" char=".">4.16</td></tr><tr valign="top"><td align="left">Learning avoid</td><td align="char" char=".">1.83</td><td align="char" char=".">1.92</td><td align="char" char=".">3.68</td><td align="char" char=".">2.81</td><td align="char" char=".">3.31</td></tr><tr valign="top"><td align="left">Performance approach</td><td align="char" char=".">3.09</td><td align="char" char=".">2.86</td><td align="char" char=".">2.91</td><td align="char" char=".">2.65</td><td align="char" char=".">3.74</td></tr><tr valign="top"><td align="left">Performance avoid</td><td align="char" char=".">2.48</td><td align="char" char=".">2.80</td><td align="char" char=".">3.13</td><td align="char" char=".">2.46</td><td align="char" char=".">3.70</td></tr><tr valign="top"><td align="left">Task approach</td><td align="char" char=".">4.44</td><td align="char" char=".">4.26</td><td align="char" char=".">3.86</td><td align="char" char=".">3.56</td><td align="char" char=".">4.53</td></tr><tr valign="top"><td align="left">Task avoid</td><td align="char" char=".">1.80</td><td align="char" char=".">2.51</td><td align="char" char=".">3.29</td><td align="char" char=".">2.57</td><td align="char" char=".">2.99</td></tr><tr valign="top"><td align="left">Future time perspective</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Perceptions of instrumentality</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Endogenous PI</td><td align="char" char=".">4.36</td><td align="char" char=".">4.32</td><td align="char" char=".">2.29</td><td align="char" char=".">3.33</td><td align="char" char=".">3.54</td></tr><tr valign="top"><td align="left">Exogenous PI</td><td align="char" char=".">3.92</td><td align="char" char=".">3.90</td><td align="char" char=".">3.16</td><td align="char" char=".">3.25</td><td align="char" char=".">3.75</td></tr><tr valign="top"><td align="left">Career connectedness</td><td align="char" char=".">4.49</td><td align="char" char=".">4.13</td><td align="char" char=".">3.94</td><td align="char" char=".">3.99</td><td align="char" char=".">4.34</td></tr><tr valign="top"><td align="left">Course affect</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Positive affect</td><td align="char" char=".">3.77</td><td align="char" char=".">3.13</td><td align="char" char=".">2.03</td><td align="char" char=".">2.73</td><td align="char" char=".">3.04</td></tr><tr valign="top"><td align="left">Negative affect</td><td align="char" char=".">1.68</td><td align="char" char=".">1.49</td><td align="char" char=".">2.76</td><td align="char" char=".">2.13</td><td align="char" char=".">2.51</td></tr></table> </ephtml> </p> <p>Variable Levels for Learning Profiles</p> <p> <ephtml> <table><tr valign="bottom"><th align="left" /><th align="center">Learning profiles</th></tr><tr valign="bottom"><th align="left" /><th align="center">Adaptive</th><th align="center">Maladaptive</th></tr><tr valign="bottom"><th align="left">Variables</th><th align="center">Strategic</th><th align="center">Knowledge Building</th><th align="center">Apathetic</th><th align="center">Surface Learning</th><th align="center">Learned Helpless</th></tr><tr valign="top"><td align="left">Self‐regulation</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Self‐regulated strategy use</td><td align="left">High</td><td align="left">Low</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">High</td></tr><tr valign="top"><td align="left">Knowledge building</td><td align="left">High</td><td align="left">High</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">Moderate</td></tr><tr valign="top"><td align="left">Lack of regulation</td><td align="left">Low</td><td align="left">Low</td><td align="left">High</td><td align="left">Moderate</td><td align="left">High</td></tr><tr valign="top"><td align="left">Engagement</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">High‐level question asking</td><td align="left">High</td><td align="left">Low</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">Moderate</td></tr><tr valign="top"><td align="left">Low‐level question asking</td><td align="left">High</td><td align="left">Low</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">Moderate</td></tr><tr valign="top"><td align="left">Study time</td><td align="left">High</td><td align="left">Low</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">High</td></tr><tr valign="top"><td align="left">Perceived study effort</td><td align="left">High</td><td align="left">Low</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">Moderate</td></tr><tr valign="top"><td align="left">Motivation and affect</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Goal orientation</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Learning approach</td><td align="left">High</td><td align="left">High</td><td align="left">Low</td><td align="left">Low</td><td align="left">Moderate</td></tr><tr valign="top"><td align="left">Learning avoid</td><td align="left">Low</td><td align="left">Low</td><td align="left">High</td><td align="left">Moderate</td><td align="left">High</td></tr><tr valign="top"><td align="left">Performance approach</td><td align="left">Moderate</td><td align="left">Moderate</td><td align="left">Moderate</td><td align="left">Low</td><td align="left">High</td></tr><tr valign="top"><td align="left">Performance avoid</td><td align="left">Low</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">Low</td><td align="left">High</td></tr><tr valign="top"><td align="left">Task approach</td><td align="left">High</td><td align="left">High</td><td align="left">Moderate</td><td align="left">Low</td><td align="left">High</td></tr><tr valign="top"><td align="left">Task avoid</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">High</td><td align="left">Moderate</td><td align="left">High</td></tr><tr valign="top"><td align="left">Future time perspective</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Perceptions of instrumentality</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Endogenous PI</td><td align="left">High</td><td align="left">High</td><td align="left">Low</td><td align="left">Moderate</td><td align="left">Moderate</td></tr><tr valign="top"><td align="left">Exogenous PI</td><td align="left">High</td><td align="left">High</td><td align="left">Low</td><td align="left">Low</td><td align="left">High</td></tr><tr valign="top"><td align="left">Career connectedness</td><td align="left">High</td><td align="left">Moderate</td><td align="left">Moderate</td><td align="left">Moderate</td><td align="left">High</td></tr><tr valign="top"><td align="left">Course affect</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr valign="top"><td align="left">Positive affect</td><td align="left">High</td><td align="left">Moderate</td><td align="left">Low</td><td align="left">Low</td><td align="left">Moderate</td></tr><tr valign="top"><td align="left">Negative affect</td><td align="left">Low</td><td align="left">Low</td><td align="left">High</td><td align="left">Moderate</td><td align="left">High</td></tr></table> </ephtml> </p> <p>1 Note: Descriptors reflect relative levels of a variable (row) across profiles (columns). They do not indicate absolute levels of variables or the level of a variable compared with other variables.</p> <p>The Strategic profile corresponds closely to traditional views of a strategic, self‐regulated student (Pressley et al., [<reflink idref="bib44" id="ref159">44</reflink>] ; Weinstein & Mayer, [<reflink idref="bib74" id="ref160">74</reflink>] ). These students had high levels of both self‐regulated strategy use and knowledge‐building strategies along with active engagement, as indicated by high levels of question asking, study time, and perceived study effort. Students in the Learned Helpless profile had similar characteristics; they had high levels of self‐regulated strategy use and study time, and moderate levels of knowledge‐building strategies, question asking, and perceived study effort. Students adopting the Learned Helpless profile, however, reported high lack of self‐regulation, which suggests that they were distinguished from students in the Strategic profile largely by not being successful in their self‐regulated strategy use, knowledge‐building, and engagement efforts. Students in these two profiles were similar in many aspects of their motivation and affect. Students in both profiles were high or moderate in learning, performance, and task approach goal orientation; in career connectedness; in endogenous and exogenous PI; and in positive affect. These patterns suggested that their positive motivation was similar. Students who adopted the Learned Helpless profile, however, contrasted dramatically with those who adopted the Strategic profile in learning, performance, and task avoid goal orientation; the students in the Learned Helpless profile reported high levels of these goal orientations, whereas students in the Strategic profile reported low levels of these goal orientations. A similar contrast was apparent for negative affect. These contrasts suggest that the positive motivation of students who adopted the Learned Helpless profile was being offset by high levels of negative motivation in a way similar to how their positive attempts at self‐regulated learning were being undermined by high lack of regulation. The combination of high performance goal orientation and failure, as expressed in the high lack of regulation scores, is consistent with Dweck and Leggett's ([<reflink idref="bib15" id="ref161">15</reflink>] ) description of the precursors to learned helplessness.</p> <p>Students in the Strategic and Knowledge Building profiles were very similar motivationally. Students in both of these profiles had high learning approach and low learning avoid goal orientation, moderate performance approach and low performance avoid goal orientation, and high task approach goal orientation. Also, students in both of these learning profiles had high endogenous PI and exogenous PI and low negative affect. But they differed slightly because students who adopted the Knowledge Building profile had only moderate rather than high levels of career connectedness and positive affect, and moderate rather than low levels of task avoid goal orientation. Interestingly, despite their motivational similarities, these two groups of students' self‐regulated behaviors were very different. Students adopting the Knowledge Building profile reported high levels of knowledge‐building behaviors but low levels of all other self‐regulation and engagement variables.</p> <p>Students in the Surface Learning and Apathetic profiles shared many motivational and affective characteristics. They had the lowest levels of all profiles for learning approach and task approach goal orientation, both endogenous and exogenous PI, career connectedness, and positive affect. Students in both of these profiles saw little value in the course, had little personal investment or desire to learn course content, and experienced negative emotions. They primarily just wanted the course to finish and to do the minimum amount of work possible. Despite these similarities, students in the Apathetic profile had higher levels of learning avoid, performance avoid, and task avoid goal orientation and negative affect; this pattern suggests not just a lack of positive motivation but heightened negative emotions and motivation for the course.</p> <p>Students in the Apathetic and Surface Learning profiles differed in their self‐regulation. Students in the Apathetic profile essentially reported no active strategic self‐regulation or engagement in the course. They reported the lowest levels of self‐regulated learning strategy use, knowledge building, and engagement. They did, however, report the highest level of lack of regulation. These students apparently are not motivated enough to try to learn, and they do not feel that they would be successful if they did try. So, rather than continuing to try and fail in their self‐regulated strategy use, like the students in the Learned Helpless profile, the students in the Apathetic profile do not even try. The students in the Surface Learning profile, on the other hand, were somewhat engaged and self‐regulating. They were in the middle of all profiles in engagement measures of question asking, study time, and perceived study effort and in self‐regulated strategy use. Relative to students in the Strategic and Knowledge Building profiles, however, they reported less knowledge building; this low level suggests that they did not engage in as much deep, personally meaningful learning.</p> <p>What appears to motivate this higher level of self‐regulated learning and engagement relative to the students in the Apathetic profile was higher endogenous PI. Although low relative to students in the Strategic or Knowledge Building profiles, students in the Surface Learning profile were more likely to see learning the material in the course as at least somewhat instrumental to their future academic and career goals. This relationship apparently was enough for them to at least minimally engage; this relationship was also reflected in lower task avoid goal orientation than for students in the Apathetic profile. The students in the Surface Learning profile do not seem to care about demonstrating performance (as shown in the lowest performance approach and avoid goal orientation of all learning profiles) or doing well in general (as shown by the lowest task approach goal orientation of all learning profiles). Perhaps the students in the Surface Learning profile perceive enough instrumentality in the course to try at least to get an average grade.</p> <hd id="AN0100548915-28">Learning Profiles of Engineering Students</hd> <p>Once we determined the learning profiles using cluster analysis, we assessed the profiles that engineering students adopted and the trends seen for this group compared to general CS1 courses related to profile adoption and learning of content.</p> <hd id="AN0100548915-29">Differences between students in CS1‐Engineering and in other CS1 courses</hd> <p>We hypothesized that students in the foundational CS1‐Engineering course would adopt maladaptive profiles (Apathetic, Surface Learning, or Learned Helpless) compared with students in CS1 courses, generally. As expected, students in the foundational CS1‐Engineering course were more likely to adopt the Apathetic or Learned Helpless profiles, with about 61% of CS1‐Engineering students adopting one of these profiles (Table [NaN] ). Compared with the primarily computer science majors in the other introductory computer science courses (CS1‐Honors, CS1‐Computer Science, CS1‐Mixed), engineering majors in the foundational CS1‐Engineering course adopted the more maladaptive Surface Learning, Apathetic, and Learned Helpless profiles at higher rates (83% vs. 45%). The differences were most striking for adoption of the Apathetic profile; 32% of students in CS1‐Engineering adopted this profile, while only 7% of students adopted the profile in the other three CS1 courses. The inverse pattern occurred for the more adaptive profiles: the Strategic or Knowledge Building learning profiles were adopted by 56% of students in the other three CS1 courses, whereas they were only adopted by 17% of the students in CS1‐Engineering.</p> <p>Comparison of Students in CS Courses</p> <p> <ephtml> <table><tr valign="bottom"><th align="left" /><th align="center">Learning profiles</th></tr><tr valign="bottom"><th align="left" /><th align="center">Adaptive</th><th align="center">Maladaptive</th></tr><tr valign="bottom"><th align="left" /><th align="center">Strategic</th><th align="center">Knowledge Building</th><th align="center">Apathetic</th><th align="center">Surface Learning</th><th align="center">Learned Helpless</th></tr><tr valign="bottom"><th align="left" /><th align="center">n</th><th align="left">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th></tr><tr valign="top"><td align="left">CS1‐Engineering</td><td align="char" char=".">38</td><td align="char" char=".">12</td><td align="char" char=".">17</td><td align="char" char=".">5</td><td align="char" char=".">106</td><td align="char" char=".">32</td><td align="char" char=".">72</td><td align="char" char=".">22</td><td align="char" char=".">94</td><td align="char" char=".">29</td></tr><tr valign="top"><td align="left">Other CS1 courses</td><td align="char" char=".">66</td><td align="char" char=".">33</td><td align="char" char=".">46</td><td align="char" char=".">23</td><td align="char" char=".">15</td><td align="char" char=".">7</td><td align="char" char=".">30</td><td align="char" char=".">15</td><td align="char" char=".">45</td><td align="char" char=".">22</td></tr></table> </ephtml> </p> <p>2 Note. χ2(<reflink idref="bib4" id="ref162">4</reflink>) = 99.94, p <.0001.</p> <hd id="AN0100548915-30">Effect of learning profile adoption on learning</hd> <p>To examine how the adoption of different learning profiles affected engineering students' learning in CS1‐Engineering, we conducted a one‐way ANOVA. Knowledge test scores were significantly different across profiles: F (<reflink idref="bib4" id="ref163">4</reflink>, 310) = 6.78, p <.0001. Because variances were unequal, Dunnett T3 pair‐wise, post hoc comparisons were conducted. Students in the Strategic (M = 6.82, SD = 2.47) and Knowledge Building (M = 8.24, SD = 2.82) profiles did not significantly differ from each other, and students in the Apathetic (M = 5.02, SD = 2.60), Surface Learning (M = 5.38, SD = 3.29), and Learned Helpless (M = 5.79, SD = 2.69) profiles did not significantly differ from each other. Students in the Knowledge Building profile scored significantly higher than those in the Apathetic (Cohen's d = 1.15), Surface Learning (Cohen's d = 1.03), and Learned Helpless (Cohen's d =.88) profiles. Students in the Strategic profile scored significantly higher than those in the Apathetic profile (Cohen's d =.65), but not students in the Surface Learning (Cohen's d =.52) or Learned Helpless (Cohen's d =.37) profiles. Engineering students who adopted the Knowledge Building profile scored around one standard deviation higher on the knowledge test than students who adopted the Apathetic, Surface Learning, or Learned Helpless profiles. Engineering students who adopted the Strategic profile scored around one‐half standard deviation higher on the knowledge test than did students who adopted the Apathetic, Surface Learning, or Learned Helpless profiles. These large effect sizes suggest nontrivial differences, even though only the difference between the Strategic and the Apathetic profile was statistically significant.</p> <hd id="AN0100548915-31">Association of learning profiles with computer science major</hd> <p>Consideration to major or minor in computer science was assessed in the fall 2010 and fall 2011 semesters. Among engineering students in CS1‐Engineering, learning profile adoption was significantly associated with the students' considerations to major or minor in computer science in addition to their engineering major (Table [NaN] ). The low number of engineering students considering or already majoring or minoring in computer science (13 students out of 165) and the many cells in Table [NaN] with values less than 5 makes the statistical tests unreliable. Despite this limitation, the differences in the patterns of profile adoption are striking. A far greater percentage of engineering students considering computer science as a major or minor adopted the Knowledge Building and Strategic profiles than those not considering a computer science major or minor (61% vs. 17 %). Almost one‐third of engineering students not considering to major or minor in computer science were in the Apathetic profile, whereas no students who were considering or already majoring or minoring in computer science adopted the Apathetic profile. The large percentage of engineering students who adopted the Apathetic profile suggests that there is a notable difference between how students approach courses in their chosen field and how they approach foundational required or elective courses outside their intended major. This pattern appears to be true even when the course is directly tailored to the students' major (e.g., tailored to engineering). Recall that the computer science courses here were developed for engineering students.</p> <p>Student Consideration for CS Major or Minor</p> <p> <ephtml> <table><tr valign="bottom"><th align="left" /><th align="center">Learning profiles</th></tr><tr valign="bottom"><th align="left" /><th align="center">Adaptive</th><th align="center">Maladaptive</th></tr><tr valign="bottom"><th align="left" /><th align="center">Strategic</th><th align="center">Knowledge Building</th><th align="center">Apathetic</th><th align="center">Surface Learning</th><th align="center">Learned Helpless</th></tr><tr valign="bottom"><th align="left" /><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th></tr><tr valign="top"><td align="left">Major or minor</td><td align="char" char=".">6</td><td align="char" char=".">46</td><td align="char" char=".">2</td><td align="char" char=".">15</td><td align="char" char=".">0</td><td align="char" char=".">0.0</td><td align="char" char=".">3</td><td align="char" char=".">23</td><td align="char" char=".">2</td><td align="char" char=".">15</td></tr><tr valign="top"><td align="left">Not considering</td><td align="char" char=".">18</td><td align="char" char=".">12</td><td align="char" char=".">7</td><td align="char" char=".">5</td><td align="char" char=".">49</td><td align="char" char=".">32.2</td><td align="char" char=".">30</td><td align="char" char=".">20</td><td align="char" char=".">48</td><td align="char" char=".">32</td></tr></table> </ephtml> </p> <p>3 Note. χ2(<reflink idref="bib4" id="ref164">4</reflink>) = 17.54, p =.002. Expected values less than five in four cells makes χ2 values unreliable.</p> <hd id="AN0100548915-32">Gender differences within learning profiles</hd> <p>Table [NaN] gives the distribution of learning profiles for men and women in the CS1‐Engineering course. The percentage of men and women engineering majors did not differ in profile adoption. However, the small number of women among the engineering majors makes it difficult to draw any strong conclusions about gender differences.</p> <p>Gender Differences in CS1‐Engineering by Learning Profile</p> <p> <ephtml> <table><tr valign="bottom"><th align="left" /><th align="center">Learning profiles</th></tr><tr valign="bottom"><th align="left" /><th align="center">Adaptive</th><th align="center">Maladaptive</th></tr><tr valign="bottom"><th align="left" /><th align="center">Strategic</th><th align="center">Knowledge Building</th><th align="center">Apathetic</th><th align="center">Surface Learning</th><th align="center">Learned Helpless</th></tr><tr valign="bottom"><th align="left" /><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th><th align="center">n</th><th align="center">%</th></tr><tr valign="top"><td align="left">Men</td><td align="char" char=".">30</td><td align="char" char=".">11</td><td align="char" char=".">13</td><td align="char" char=".">5</td><td align="char" char=".">90</td><td align="char" char=".">33</td><td align="char" char=".">63</td><td align="char" char=".">23</td><td align="char" char=".">79</td><td align="char" char=".">29</td></tr><tr valign="top"><td align="left">Women</td><td align="char" char=".">8</td><td align="char" char=".">16</td><td align="char" char=".">3</td><td align="char" char=".">6</td><td align="char" char=".">15</td><td align="char" char=".">31</td><td align="char" char=".">8</td><td align="char" char=".">16</td><td align="char" char=".">15</td><td align="char" char=".">31</td></tr></table> </ephtml> </p> <p>4 Note. χ2(<reflink idref="bib4" id="ref165">4</reflink>) = 2.14, p =.711.</p> <hd id="AN0100548915-33">Discussion</hd> <p>Faculty members who teach foundational courses already know that students exhibit attitudes, beliefs, and behaviors in these courses that are counterproductive to their motivation and learning. This study sought to better understand how students approach foundational courses by identifying learning profiles of students who share common motivational and self‐regulated learning characteristics; the students were taking courses in a suite of foundational computer science courses designed for both computer science majors and non‐computer science majors, some of whom were engineering students. Our identification of five profiles replicates and extends findings from prior studies of college students in nonengineering (Chen, [<reflink idref="bib10" id="ref166">10</reflink>] ; Daniels et al., [<reflink idref="bib13" id="ref167">13</reflink>] ; Schwinger et al., [<reflink idref="bib54" id="ref168">54</reflink>] ; Shell & Husman, [<reflink idref="bib59" id="ref169">59</reflink>] ) and engineering or technical fields (Entwistle & McCune, [<reflink idref="bib18" id="ref170">18</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref171">61</reflink>] ). The profiles also replicate profiles found in K‐12 settings (Chen, [<reflink idref="bib10" id="ref172">10</reflink>] ; Hayenga & Corpus, [<reflink idref="bib24" id="ref173">24</reflink>] ; Tuominen‐Soini et al., [<reflink idref="bib69" id="ref174">69</reflink>] ; Vansteenkiste, Sierens, Soenens, Luyckx, & Lens, [<reflink idref="bib70" id="ref175">70</reflink>] ). Identification of these profiles in multiple studies across diverse grade levels and content areas supports the argument by Shell et al. ([<reflink idref="bib56" id="ref176">56</reflink>] ) that these five profiles capture the motivational beliefs and self‐regulated learning behaviors typical of most students in formal educational settings.</p> <p>Are these the only five possible profiles that students can adopt when approaching their coursework? Perhaps not. Conley ([<reflink idref="bib12" id="ref177">12</reflink>] ) has identified more profiles of motivation, but her additional profiles have not been replicated in other studies. The profile literature, however, is still in its infancy. The applicability of these profiles needs to be examined across more post‐secondary courses and content areas. Also, a broader array of engineering courses needs to be studied, especially those at more advanced levels, as well as other foundational courses commonly taken by engineering students.</p> <p>The profiles described here shed light on the motivational beliefs and self‐regulated learning behaviors engineering students adopt in foundational courses and the effect of profile adoption on learning. Unfortunately, but not unexpectedly, approximately 83% of engineering students taking the CS1‐Engineering course in this study adopted one of the maladaptive Apathetic, Surface Learning, or Learned Helplessness profiles. Students who adopted these maladaptive profiles learned less content than students who adopted the adaptive Strategic or Knowledge Building profiles. Adoption of these maladaptive profiles by engineering students in foundational required courses may be contributing to their lower first‐year GPAs and ultimately to the lower retention rates of engineering students (Burtner, [<reflink idref="bib9" id="ref178">9</reflink>] ; French, et al., [<reflink idref="bib19" id="ref179">19</reflink>] ; Veenstra, Dey, & Herrin, [<reflink idref="bib71" id="ref180">71</reflink>] ).</p> <p>These data provide additional detail concerning the struggles with motivation and self‐regulation that engineering students may experience when they take required foundational courses outside of their major. The findings do not provide any evidence that engineering students themselves are inherently maladaptive learners.</p> <p>Many researchers (Entwistle & McCune, [<reflink idref="bib18" id="ref181">18</reflink>] ; Shell et al., [<reflink idref="bib56" id="ref182">56</reflink>] ; Shell & Husman, [<reflink idref="bib59" id="ref183">59</reflink>] ) note that student adoption of profiles is dynamic, responsive to context. A student's profile may shift across different courses and subject matter domains as well as in response to contextual factors within a course. Our findings indicate that participating in a foundational nonmajor course may create a particular context and produce a perfect storm of conditions that affect how engineering students approach coursework in that course. In another course (a course within their major, for example), they may act entirely differently.</p> <p>Our findings provide further evidence that students' goals (class goal orientation) and beliefs about the course (PI) may be components that together influence students' approaches regarding how or if they self‐regulate their learning in these courses (Simonsk, Dewitte, & Lens [<reflink idref="bib63" id="ref184">63</reflink>] ; Tabachnick, Miller, & Relyea, [<reflink idref="bib67" id="ref185">67</reflink>] ). The tendency of engineering students to see foundational courses as less instrumental than courses within their major field has also been consistently reported (Husman & Corno, [<reflink idref="bib28" id="ref186">28</reflink>] ; Puruhito, Husman, Hilpert, Ganesh, & Stump, [<reflink idref="bib46" id="ref187">46</reflink>] ). But, is looking at one motivation and self‐regulation variable in the profile enough to understand student approaches to learning in a course? In addition to the high perceived instrumentality in the Strategic and Knowledge Building profiles, our results indicate that high perceived instrumentality was also linked to the maladaptive Learned Helpless profile. Students in the Learned Helpless profile had contradictory motivational beliefs and self‐regulated learning behaviors. They had high learning approach, but also high learning avoid goal orientation. They report high self‐regulation, knowledge building, and engagement, but also high lack of regulation. Their high perceived instrumentality for the courses does not appear to be enough to overcome the lack of success and negative emotions that lead to their contradictory motivation and self‐regulation. Apparently, students' perception that a course is useful can only produce positive motivation and self‐regulated learning when students feel that they are in control of their learning and are confident that their efforts for self‐regulated learning and engagement are leading to success. Belief that a course is important for the future may increase students' feelings of helplessness if they do not feel they control their success in that course. Overall, the results of this study indicate that student engagement in a course cannot be understood through the lens of just one motivational construct. Students' beliefs, perceptions, behaviors, and strategic approaches interact and affect each other. Although it is clear that goal orientations and perceived instrumentality play an important role in orienting a students' approach to learning, they do not motivate success on their own.</p> <hd id="AN0100548915-34">Implications</hd> <p>Our findings further validate the profile approach as a framework for educators. The use of learning profiles created by researchers can help educators better understand the motivations and behaviors they see students exhibit − an understanding that can guide instructional and curricular strategies to encourage their students' optimal motivational beliefs and self‐regulated learning in particular courses. Efforts can be made by instructors to change learning profiles once they have been identified. As shown by Hulleman and Harackiewicz ([<reflink idref="bib27" id="ref188">27</reflink>] ), instructors can manipulate motivational beliefs and self‐regulated learning behaviors into adaptive motivational beliefs and self‐regulated learning behaviors. Although neither researchers nor educators currently have a reliable method for classifying students' classroom learning beliefs, we argue that the replication of these profiles in many studies (Chen, [<reflink idref="bib10" id="ref189">10</reflink>] ; Daniels et al., [<reflink idref="bib13" id="ref190">13</reflink>] ; Entwistle & McCune, [<reflink idref="bib18" id="ref191">18</reflink>] ; Schwinger et al., [<reflink idref="bib54" id="ref192">54</reflink>] ; Shell & Husman, [<reflink idref="bib59" id="ref193">59</reflink>] ; Shell & Soh, [<reflink idref="bib61" id="ref194">61</reflink>] ) demonstrates a common set of approaches or postures undergraduate students take when learning in traditional college settings. Educators can think about how their instructional practice may encourage one of the more maladaptive profiles (the instructional content does not appear to directly apply to students futures in engineering) or how it could support an adaptive profile (instructional content makes clear how the topics being covered are directly applicable to students' futures as engineers). Instructors may also look for signs of profiles that could be supporting, or indicating trouble, for students in their course. Evidence suggests changes to instruction can cause a student to adopt a new profile. For example, in a study of elementary school students, Linnenbrink‐Garcia ([<reflink idref="bib35" id="ref195">35</reflink>] ) found that maladaptive profiles similar to those identified here were positively influenced to adopt profiles by both a classroom intervention and students' perceptions of the classroom environment.</p> <p>Heyman, Martyna, and Bhatia ([<reflink idref="bib25" id="ref196">25</reflink>] ) showed that engineering students have “fixed” mindsets about their intelligence. Students who have fixed mindsets are more likely to have performance goals, whereas students with the opposite “growth” mindset are more likely to have mastery goals (learning goals). Performance goals can be detrimental in that they can decrease students' effort and allocation (Senko et al., [<reflink idref="bib55" id="ref197">55</reflink>] ). Interventions can be used by instructors to change their students' fixed mindsets to growth mindsets. Blackwell, Trzesniewski, & Dweck ([<reflink idref="bib6" id="ref198">6</reflink>] ) showed how malleable mindsets were through an intervention with middle school students. Specifically, they provided students content whose key message was that learning is the process of creating connections in the brain, that learning is in the students' control. Through this process, the students learned that they have control for their own learning – intelligence is not something that they are born with; it is not fixed (Blackwell et al., [<reflink idref="bib6" id="ref199">6</reflink>] ). Researchers observed that students changed their mindset from fixed to growth oriented (not fixed). This work on mindsets has not been extended to engineering at present; however, mindset is not a domain‐specific phenomenon. Foundational courses could be tailored to include mini‐interventions to teach students that their intelligence is not fixed. If these courses can be tailored to encourage students to adopt a more adaptive class goal orientation or learning approach, there is a greater likelihood that students will shift towards more adaptive profiles and enhance their performance and overall learning in foundational courses. All of these findings suggest that despite the general conceptual stability present in the five‐cluster learning profile solution, the profiles students adopt could be influenced by the classroom environment, students' perception of the course, and how it is taught. Overall, we encourage interventions that promote student adoption of adaptive learning profiles instead of just student adoption of specific adaptive motivational beliefs and self‐regulated learning behaviors, such as those interventions described by Linnenbrink‐Garcia ([<reflink idref="bib35" id="ref200">35</reflink>] ).</p> <hd id="AN0100548915-35">Limitations</hd> <p>The profile approach is not an inferential statistical technique; it is a descriptive methodology that is highly interpretive. In identifying the five learning profiles in this study through cluster analysis, there were only minor statistical differences among the four‐, five‐, and six‐cluster solutions. Any of these could have been legitimately justified on purely statistical grounds. As discussed, differences in profiles are sometimes subtle. Interpretation of cluster solutions relies as much on theoretical grounds as it does on the available statistics. We argue, as have other researchers (Entwistle & McCune, [<reflink idref="bib18" id="ref201">18</reflink>] ; Shell et al., [<reflink idref="bib56" id="ref202">56</reflink>] ), that the profiles that students adopt will be found in all courses; however, there is no way yet to validly infer any particular distribution of profiles in a course. We can suggest that the results here are typical of how engineering students may approach required foundational courses but are probably not so typical for their approach to other foundational required courses. We would, however, expect that engineering students are more likely to adopt maladaptive profiles within these courses than they would in courses within their major. The types of profiles students adopt can only be determined by additional research. Such research must consider differences in students' individual profiles as they enroll in different engineering courses and use a sample population with more extensive minorities and females. Consideration of differences will shed light on how students perceive their courses, their perceived capability to learn the content, and how they self‐regulate their own learning.</p> <p>This study examined only students at a single university as part of a specific, funded exploratory project. Use of a single university limits its generalization. The suite of introductory computer science courses is similar in content to introductory computer science courses at most colleges and universities, but not all of these courses are tailored for different student populations or taught using the same pedagogy. We cannot say whether similar introductory computer science courses at other schools would show the same profile distributions.</p> <p>There also may have been effects due to different instructors within the courses studied. The different courses in the study were all taught by rotating computer science faculty. With the exception of the CS1‐Honors course, which was taught by one faculty member, all of the other courses were taught by different faculty in different semesters, with all faculty teaching each course at some point. Although aspects of the courses were tailored by the instructors, all courses had the same basic content and instructional format of lecture and lab. The common curriculum and rotating faculty would suggest that there were not large instructor or pedagogical differences that could account for the study findings related to engineering student adoption of maladaptive profiles. Different instructors or pedagogy would likely affect the distribution of profiles within a specific offering of any one of the courses studied.</p> <p>Finally, although our findings replicated those of Shell and Soh ([<reflink idref="bib61" id="ref203">61</reflink>] ), their study used the same fall 2010 students as did this study. Overall, 233 of the 538 students in the total sample and 94 of the 332 engineering students in CS1‐Engineering overlapped. Although this study examined three additional semesters of data, the findings cannot be considered independent from those of Shell and Soh.</p> <hd id="AN0100548915-36">Conclusions</hd> <p>Our findings provide an important first step toward using a profile approach in engineering education to determine what learning profiles students adopt in foundational courses. Many of the students taking a foundational computer science course that was tailored for engineering adopted maladaptive motivational and self‐regulated learning profiles – that is, they adopted profiles that indicated limited perceptions of instrumentality for learning the course content, maladaptive goal orientation variables to avoid learning the course material, and a lack of effective self‐regulated learning behaviors. Additional research is needed that looks at all types of engineering courses to consider the profiles students adopt in these courses, and why these profiles are adopted. Once profile adoption is better understood by researchers, educators can help students shift to more adaptive learning profile adoption (see Linnenbrink‐Garcia, [<reflink idref="bib35" id="ref204">35</reflink>] ). Overall, our findings indicate that a profile approach may help engineering educators better tailor their courses, and especially their foundational courses, and to create interventions or instructional changes that encourage more motivated and self‐regulated learning in the classroom.</p> <hd id="AN0100548915-37">Acknowledgments</hd> <p>This research was partially supported by a grant from the National Science Foundation Grant CNS‐0829647 to Leen‐Kiat Soh and Duane F. Shell and is based upon work primarily supported by the National Science Foundation (NSF) and the Department of Energy (DOE) under NSF CA No. EEC‐1041895. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of NSF and DOE.</p> <p>Data from the fall 2010 semester were previously published in Shell and Soh ([<reflink idref="bib61" id="ref205">61</reflink>] ).</p> <ref id="AN0100548915-38"> <title>References</title> <blist> <bibl id="bib1" idref="ref29" type="bt">1</bibl> <bibtext>Acee, T. W., & Weinstein, C. E. ( 2010 ). Effects of a value‐reappraisal intervention on statistics students' motivation and performance. Journal of Experimental Education, 78 ( 4 ), 487 – 512. doi: 10.1080/00220970903352753 </bibtext> </blist> <blist> <bibl id="bib2" idref="ref10" type="bt">2</bibl> <bibtext>Adelman, C. ( 1999 ). 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New York, NY : Routledge/Taylor & Francis. doi: 10.3102/0002831207312909 </bibtext> </blist> </ref> <p>Graph: Learner‐centered approach to assessing the pattern of motivation and self‐regulation constructs.</p> <aug> <p>By Katherine G. Nelson; Duane F. Shell; Jenefer Husman; Evan J. Fishman and Leen‐Kiat Soh</p> <p></p> <p>Katherine G. Nelson is a post‐doctoral scholar in the Ira A. Fulton School of Engineering at Arizona State University, P.O. Box 9309, Tempe, AZ 85287‐9309;.</p> <p>Duane F. Shell is a research professor in the Department of Educational Psychology, 114 TEAC, University of Nebraska‐Lincoln, Lincoln, NE 68588‐0345;.</p> <p>Jenefer Husman is an associate professor in the T. Denny School of Social and Family Dynamics at Arizona State University, P.O. Box 3701, Tempe, AZ 85287‐5706;.</p> <p>Evan Fishman is a doctoral student in educational psychology in the Mary Lou Fulton Teacher's College at Arizona State University, P.O. Box 3701, Tempe, AZ 85287‐5706;.</p> <p>Leen‐Kiat Soh, is an associate professor in the Department of Computer Science and Engineering, 122E AVH, University of Nebraska‐Lincoln, Lincoln, NE 68588‐0115;</p> </aug>
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  Data: Motivational and Self-Regulated Learning Profiles of Students Taking a Foundational Engineering Course
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  Data: <searchLink fieldCode="AR" term="%22Nelson%2C+Katherine+G%2E%22">Nelson, Katherine G.</searchLink><br /><searchLink fieldCode="AR" term="%22Shell%2C+Duane+F%2E%22">Shell, Duane F.</searchLink><br /><searchLink fieldCode="AR" term="%22Husman%2C+Jenefer%22">Husman, Jenefer</searchLink><br /><searchLink fieldCode="AR" term="%22Fishman%2C+Evan+J%2E%22">Fishman, Evan J.</searchLink><br /><searchLink fieldCode="AR" term="%22Soh%2C+Leen-Kiat%22">Soh, Leen-Kiat</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Engineering+Education%22"><i>Journal of Engineering Education</i></searchLink>. Jan 2015 104(1):74-100.
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  Data: Wiley Periodicals, Inc. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
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  Data: 27
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  Data: 2015
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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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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+Education%22">Engineering Education</searchLink><br /><searchLink fieldCode="DE" term="%22Profiles%22">Profiles</searchLink><br /><searchLink fieldCode="DE" term="%22Barriers%22">Barriers</searchLink><br /><searchLink fieldCode="DE" term="%22Majors+%28Students%29%22">Majors (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Motivation%22">Student Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+Education%22">Computer Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/jee.20066
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  Label: ISSN
  Group: ISSN
  Data: 1069-4730
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  Label: Abstract
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  Data: Background: Technical, nonengineering required courses taken at the onset of an engineering degree provide students a foundation for engineering coursework. Students who perform poorly in these foundational courses, even in those tailored to engineering, typically have limited success in engineering. A profile approach may explain why these courses are obstacles for engineering students. This approach examines the interaction among motivation and self-regulation constructs. Purpose (Hypothesis): This project sought to determine what motivational and self-regulated learning profiles engineering students adopt in foundational courses. We hypothesized that engineering students would adopt profiles associated with maladaptive motivational beliefs and self-regulated learning behaviors. The effects of profile adoption on learning and differences associated with student major, minor, and gender were analyzed. Design/Method: Five hundred and thirty-eight students, 332 of them engineering majors, were surveyed on motivation and self-regulation variables. Data were analyzed from a learner-centered profile approach using cluster analysis. Results: We obtained a five-cluster learning profile solution. Approximately 83% of engineering students enrolled in an engineering-tailored foundational computer science course adopted maladaptive profiles. These students learned less than those who adopted adaptive learning profiles. Profile adoption depended on whether a student was considering a major or minor in computer science or not. Conclusions: Findings indicate the motivational and self-regulated learning profiles that engineering students adopt in foundational courses, why they do so, and what profile adoption means for learning. Our findings can guide instructors in providing motivational beliefs and self-regulated learning scaffolds in the classroom.
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  Data: 2020
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  Data: EJ1255329
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      – Text: English
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      Pagination:
        PageCount: 27
        StartPage: 74
    Subjects:
      – SubjectFull: Metacognition
        Type: general
      – SubjectFull: Engineering Education
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      – SubjectFull: Profiles
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      – SubjectFull: Barriers
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      – SubjectFull: Computer Science Education
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      – SubjectFull: Student Attitudes
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      – SubjectFull: Undergraduate Students
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      – TitleFull: Motivational and Self-Regulated Learning Profiles of Students Taking a Foundational Engineering Course
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