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Predicting Performance in Exams and Deep Approach to Learning in First Year University Students: A New Look at Academic Success

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Title: Predicting Performance in Exams and Deep Approach to Learning in First Year University Students: A New Look at Academic Success
Language: English
Authors: Manuel Bächtold (ORCID 0000-0002-8659-5177), Jacqueline Papet (ORCID 0000-0002-4946-4522), Dominique Barbe Asensio (ORCID 0000-0001-9124-1966), André Mas (ORCID 0000-0001-6086-1417), Sandra Borne (ORCID 0000-0002-3209-7826), Appolinaire Ngoua Ondo (ORCID 0000-0002-7106-1447)
Source: Studies in Higher Education. 2025 50(2):333-348.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 16
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Grade Prediction, Academic Achievement, Testing, Learning Strategies, College Freshmen, Undergraduate Study, Foreign Countries, Individual Differences, Student Characteristics, Socioeconomic Background, Educational Background, Parent Background, Learner Engagement, Cognitive Processes, Intellectual Disciplines, Social Environment
Geographic Terms: France
DOI: 10.1080/03075079.2024.2338262
ISSN: 0307-5079
1470-174X
Abstract: This study calls for a broadening of the perspective on academic success. While passing exams is an essential objective of higher education, it should not overshadow another important objective which is the development of students' skills, such as becoming curious, autonomous and reflective in the learning process. This study used Academic Performance in Exams (APE) and Deep Approach to Learning (DAL) as measures related to these two objectives. The aim was to identify and compare the factors that may influence APE and DAL. The study was conducted on first-year students (2011) at a French university. It was based on a random forest algorithm and took into account a wide range of factors belonging to different dimensions: demographics, social background, educational background, context of the educational programme, behavioural engagement, social environment, psychological and cognitive characteristics. The results show that the most important factors in predicting APE are the educational programme undertaken, student's educational background and parents' occupation. DAL was not found to be an important factor in APE. Regarding the prediction of DAL, the results point to the predominant weight of intrinsic motivation and the important weight of elaborated epistemic beliefs. In contrast, demographics and behavioural engagement were found to have negligible weight in predicting both APE and DAL. These findings raise questions about the type of success that is valued in the first year of university and call for reflection on assessment methods. They also allow the identification of levers that teachers can activate to support first year students.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1459176
Database: ERIC
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  Value: <anid>AN0182505730;she01feb.25;2025Jan30.04:22;v2.2.500</anid> <title id="AN0182505730-1">Predicting performance in exams and deep approach to learning in first year university students: a new look at academic success </title> <p>This study calls for a broadening of the perspective on academic success. While passing exams is an essential objective of higher education, it should not overshadow another important objective which is the development of students' skills, such as becoming curious, autonomous and reflective in the learning process. This study used Academic Performance in Exams (APE) and Deep Approach to Learning (DAL) as measures related to these two objectives. The aim was to identify and compare the factors that may influence APE and DAL. The study was conducted on first-year students (2011) at a French university. It was based on a random forest algorithm and took into account a wide range of factors belonging to different dimensions: demographics, social background, educational background, context of the educational programme, behavioural engagement, social environment, psychological and cognitive characteristics. The results show that the most important factors in predicting APE are the educational programme undertaken, student's educational background and parents' occupation. DAL was not found to be an important factor in APE. Regarding the prediction of DAL, the results point to the predominant weight of intrinsic motivation and the important weight of elaborated epistemic beliefs. In contrast, demographics and behavioural engagement were found to have negligible weight in predicting both APE and DAL. These findings raise questions about the type of success that is valued in the first year of university and call for reflection on assessment methods. They also allow the identification of levers that teachers can activate to support first year students.</p> <p>Keywords: Approach to learning; motivation; epistemic beliefs; difficulties; social support</p> <hd id="AN0182505730-2">1. Introduction</hd> <p>Academic success in first year of university has become a major issue for higher education institutions and policies (Clerici, Girald, and Meggiolaro [<reflink idref="bib16" id="ref1">16</reflink>]). First-year students run a greater risk of dropping out or failing to achieve the grades required to reach the next year (Fokkens-Bruinsma et al. [<reflink idref="bib28" id="ref2">28</reflink>]). In addition to choosing the right educational programme for them, first-year students face a challenging transition from high school to higher education. This transition involves changes in their educational environment, such as new learning tasks, new relations with the teachers, new social networks, new time management, and new engagement in studies (De Clercq, Galand, and Frenay [<reflink idref="bib21" id="ref3">21</reflink>]). This transition also implies a new way of thinking (Entwistle [<reflink idref="bib26" id="ref4">26</reflink>]), of understanding, experiencing, and conceptualizing the world (Ramsden [<reflink idref="bib48" id="ref5">48</reflink>]). Students must undergo an intellectual, methodological and sociological metamorphosis in order to succeed in their first year (Paivandi [<reflink idref="bib46" id="ref6">46</reflink>]).</p> <p>Recently, some researchers (Van der Zanden et al. [<reflink idref="bib61" id="ref7">61</reflink>]) have taken a new look at this issue of academic success, by emphasizing that it should not be limited to Academic Performances in Exam (APE). University education has a dual challenge: to help students achieve an academic degree, but also develop the psychological and cognitive skills that are essential for their future life as citizens and for their professional development. This second challenge implies students becoming curious, autonomous and reflective in their learning. In this respect, encouraging students to adopt a Deep Approach to Learning (DAL) (Biggs and Tang [<reflink idref="bib7" id="ref8">7</reflink>]), that contributes to the development of these skills, can be seen as an essential aim of university education. According to this shift in perspective, DAL should not only be considered as a possible factor of academic success, measured in terms of exam grades, but as a complementary outcome of higher education that deserves to be examined for its own sake.</p> <p>Numerous studies have been carried out to determine the influence of a range of variables on APE. In a systematic literature review, Hellas et al. ([<reflink idref="bib32" id="ref9">32</reflink>]) distinguished various kinds of factors in APE: demographics, family background, educational background, course data, working conditions, student motivation, and psychological, affective and learning scales. Several studies have compared the relative weight of some of these variables, most of them based on linear regressions (e.g. Clerici, Girald, and Meggiolaro [<reflink idref="bib16" id="ref10">16</reflink>]; Pinxten et al. [<reflink idref="bib47" id="ref11">47</reflink>]; Sothan [<reflink idref="bib57" id="ref12">57</reflink>]). However, all the variables have not yet been taken into account together in a same study for a systematic comparison. Multiple studies have also been conducted to determine the factors that favour DAL, taking into account both student factors (such as age, gender, motivation or self-esteem) and contextual factors related to the teaching environment (Baeten et al. [<reflink idref="bib3" id="ref13">3</reflink>]). Concerning this output variable, fewer studies have compared the relative weights of the different factors.</p> <p>The aim of this study is to determine in a more systematic way the relative weight of the factors that may influence APE, by integrating a wide range of potential factors, identified in the literature, into a single predictive model. This model is based on a Random Forest algorithm (RF), which has a twofold advantage: it does not presuppose a linear relationship between the input variables and the output variable, and it can include both quantitative and nominal variables. According to the change in perspective mentioned above, this study also aims to determine the relative weights of the variables that may influence DAL, with an equivalent predictive model. The two models can thus be compared and contribute to a discussion of the links between APE and DAL.</p> <hd id="AN0182505730-3">2. Factors identified in previous research</hd> <p>Different types of factors have been identified in the literature that may favour APE and/or DAL. They can be classified into the following categories: demographics, social background, educational background, context of the educational programme, social environment, behavioural engagement, psychological and cognitive characteristics.</p> <hd id="AN0182505730-4">2.1 Demographics</hd> <p>Either positive or negative correlations were found between age and APE (Cassidy [<reflink idref="bib13" id="ref14">13</reflink>]; Craft [<reflink idref="bib17" id="ref15">17</reflink>]), gender and APE (Bruinsma [<reflink idref="bib10" id="ref16">10</reflink>]; Clerici, Girald, and Meggiolaro [<reflink idref="bib16" id="ref17">16</reflink>]), and gender and DAL (Berberoglu and Hei [<reflink idref="bib5" id="ref18">5</reflink>]; Mattick, Dennis, and Bligh [<reflink idref="bib41" id="ref19">41</reflink>]). The correlation found between age and DAL are often positive (Gijbels et al. [<reflink idref="bib29" id="ref20">29</reflink>]), but sometimes not significant (Duff et al. [<reflink idref="bib24" id="ref21">24</reflink>]). A relation was found between the country of origin and APE, with native students having an advantage over foreign students (Clerici, Girald, and Meggiolaro [<reflink idref="bib16" id="ref22">16</reflink>]; Craft [<reflink idref="bib17" id="ref23">17</reflink>]).</p> <hd id="AN0182505730-5">2.2 Social background</hd> <p>The socio-economic status (SES) of students' families has been found to be positively correlated with APE in several studies (Brinbaum, Hugrée, and Poullaouec [<reflink idref="bib9" id="ref24">9</reflink>]; Pinxten et al. [<reflink idref="bib47" id="ref25">47</reflink>]; Sothan [<reflink idref="bib57" id="ref26">57</reflink>]), with one counter-example of a non-significant relationship (Craft [<reflink idref="bib17" id="ref27">17</reflink>]). Regarding the relationship between SES and DAL, one study found a negative correlation (Suphi and Yaratan [<reflink idref="bib58" id="ref28">58</reflink>]), while another found no significant correlation (Schrempft et al. [<reflink idref="bib56" id="ref29">56</reflink>]).</p> <hd id="AN0182505730-6">2.3 Educational background</hd> <p>Several studies have found that high school grades positively predict APE at university (Clerici, Girald, and Meggiolaro [<reflink idref="bib16" id="ref30">16</reflink>]; Sothan [<reflink idref="bib57" id="ref31">57</reflink>]). Little research has looked at the relationship between high school grades and DAL at university, but there is one study that found a negative relationship (Duff et al. [<reflink idref="bib24" id="ref32">24</reflink>]).</p> <hd id="AN0182505730-7">2.4 Context of the educational programme</hd> <p>The field of study has been found to have an influence both on APE (Clerici, Girald, and Meggiolaro [<reflink idref="bib16" id="ref33">16</reflink>]; Fokkens-Bruinsma et al. [<reflink idref="bib28" id="ref34">28</reflink>]) and DAL (Nelson Laird et al. [<reflink idref="bib45" id="ref35">45</reflink>]). DAL is more prevalent in soft fields than in hard fields. The relationship between the field and APE is more complex.</p> <hd id="AN0182505730-8">2.5 Behavioural engagement</hd> <p>There are mixed results regarding the relationship between effort (i.e. time spent studying) and APE, with several studies finding a positive relationship (Diseth et al. [<reflink idref="bib23" id="ref36">23</reflink>]; Dupont, Galand, and Nils [<reflink idref="bib25" id="ref37">25</reflink>]; Sothan [<reflink idref="bib57" id="ref38">57</reflink>]) and others finding a negative relationship (Fokkens-Bruinsma et al. [<reflink idref="bib28" id="ref39">28</reflink>]). Positive correlations were found between effort and DAL (Diseth et al. [<reflink idref="bib23" id="ref40">23</reflink>]; Román, Cuestas, and Fenollar [<reflink idref="bib50" id="ref41">50</reflink>]).</p> <hd id="AN0182505730-9">2.6 Social environment</hd> <p>The influence of the social environment can be measured in terms of how students perceive it. Perceived social support was found to have a significant effect on APE (DeBerard, Spielmans, and Julka [<reflink idref="bib20" id="ref42">20</reflink>]). More specifically, supervisors, family and institutional social support, but not peer social support, were found to be positively correlated with APE (Dupont, Galand, and Nils [<reflink idref="bib25" id="ref43">25</reflink>]). Another study found that family support was positively correlated with DAL, but not with APE (Román, Cuestas, and Fenollar [<reflink idref="bib50" id="ref44">50</reflink>]). Perceived social climate, which encompasses atmosphere, culture, values, organizational, instructional and interpersonal dimensions, was found to correlate with APE but only in some classroom contexts (Rania et al. [<reflink idref="bib49" id="ref45">49</reflink>]).</p> <hd id="AN0182505730-10">2.7 Psychological and cognitive characteristics</hd> <p>Intrinsic and more broadly self-determined motivation (SDT) (Ryan and Deci [<reflink idref="bib52" id="ref46">52</reflink>]; [<reflink idref="bib53" id="ref47">53</reflink>]) were found to be positive predictors in APE (Fokkens-Bruinsma et al. [<reflink idref="bib28" id="ref48">28</reflink>]). Many studies have found a positive correlation between intrinsic motivation and DAL (Chue and Nie [<reflink idref="bib15" id="ref49">15</reflink>]; Liu, Ye, and Yeung [<reflink idref="bib38" id="ref50">38</reflink>]; Minbashian, Huon, and Bird [<reflink idref="bib42" id="ref51">42</reflink>]), which is why both variables have often been combined into a single construct (which will be avoided below for conceptual clarity).</p> <p>Self-efficacy has also been found to correlate positively with both APE (Greco et al. [<reflink idref="bib30" id="ref52">30</reflink>]; Guo et al. [<reflink idref="bib31" id="ref53">31</reflink>]; Román, Cuestas, and Fenollar [<reflink idref="bib50" id="ref54">50</reflink>]) and DAL (Lin and Tsai [<reflink idref="bib37" id="ref55">37</reflink>]; Liu, Ye, and Yeung [<reflink idref="bib38" id="ref56">38</reflink>]; Román, Cuestas, and Fenollar [<reflink idref="bib50" id="ref57">50</reflink>]). An alternative way of looking at how students perceive their abilities when studying is to consider their perceived difficulties (Ainscough et al. [<reflink idref="bib2" id="ref58">2</reflink>]; Cameron and Rideout [<reflink idref="bib11" id="ref59">11</reflink>]; Trautwein and Bosse [<reflink idref="bib59" id="ref60">59</reflink>]). However, to our knowledge, the relationship between these perceived difficulties and APE or DAL has not yet been investigated.</p> <p>Evaluativist epistemic beliefs (evaluativism), which are the most elaborated beliefs on the nature of knowledge and the processes of knowing (Hofer and Pintrich [<reflink idref="bib33" id="ref61">33</reflink>]), have been found to correlate positively with APE for some kinds of beliefs (e.g. uncertainty of knowledge), but not for others (e.g. subjectivity of knowledge) (Aditomo [<reflink idref="bib1" id="ref62">1</reflink>]; Guo et al. [<reflink idref="bib31" id="ref63">31</reflink>]; Lonka, Ketonen, and Vermunt [<reflink idref="bib39" id="ref64">39</reflink>]). A more straightforward positive relationship has been found between evaluativism and DAL (Chiu et al. [<reflink idref="bib14" id="ref65">14</reflink>]; Lehmann [<reflink idref="bib35" id="ref66">35</reflink>]; Lin, Liang, and Tsai [<reflink idref="bib36" id="ref67">36</reflink>]).</p> <p>Finally, DAL can also be considered as a factor in APE. Several studies found a positive correlation between the two variables (Diseth et al. [<reflink idref="bib23" id="ref68">23</reflink>]; Liu, Ye, and Yeung [<reflink idref="bib38" id="ref69">38</reflink>]; Schrempft et al. [<reflink idref="bib56" id="ref70">56</reflink>]), while some studies found a negative correlation (Bruinsma [<reflink idref="bib10" id="ref71">10</reflink>]) or no significant correlation (Gijbels et al. [<reflink idref="bib29" id="ref72">29</reflink>]; Minbashian, Huon, and Bird [<reflink idref="bib42" id="ref73">42</reflink>]).</p> <hd id="AN0182505730-11">3 How to compare the relative weight of the factors?</hd> <p>Given the large number of factors influencing APE and DAL identified in the research, the question arises as to their respective weight. In the case of APE, but not DAL, several studies have been carried out to compare these factors. However, each of these studies is restricted to a limited number of the above factors. Moreover, most of them are based on linear regressions (Cassidy [<reflink idref="bib13" id="ref74">13</reflink>]; Duff et al. [<reflink idref="bib24" id="ref75">24</reflink>]; Sothan [<reflink idref="bib57" id="ref76">57</reflink>]), which in some studies are used to develop structural equation models (Diseth et al. [<reflink idref="bib23" id="ref77">23</reflink>]; Dupont, Galand, and Nils [<reflink idref="bib25" id="ref78">25</reflink>]; Román, Cuestas, and Fenollar [<reflink idref="bib50" id="ref79">50</reflink>]). A problem is that the relationships between factors and APE are not necessarily linear (Diseth [<reflink idref="bib22" id="ref80">22</reflink>]; Mouratidis et al. [<reflink idref="bib43" id="ref81">43</reflink>]; Musso, Hernández, and Cascallar [<reflink idref="bib44" id="ref82">44</reflink>]), which may also be the case for DAL.</p> <p>More recently, researchers have used machine learning algorithms, such as RF, Classification Tree, or Neural Network, to predict APE and address these two limitations (Beaulac and Rosenthal [<reflink idref="bib4" id="ref83">4</reflink>]; Cannistra et al. [<reflink idref="bib12" id="ref84">12</reflink>]; Musso, Hernández, and Cascallar [<reflink idref="bib44" id="ref85">44</reflink>]). These algorithms allow to include a large number of factors, quantitative or nominal, in the same model and without assuming linear relations with the target variable. Moreover, they enable to compare the predictive weight of the factors in the model, and to reach a high predictive power.</p> <hd id="AN0182505730-12">4. The present study</hd> <p>Focusing on the first year of university, the present study considers two objectives of university education: helping students succeed in their exams, and developing psychological and cognitive skills necessary for their future life as citizens and their professional development. These two objectives can be related to two different outcome variables: APE and DAL. In order to predict these two variables, we propose to use the RF algorithm and to consider a large set of factors that fall under the different categories identified in the literature: demographics, social background, educational background, context of the educational programme, behavioural engagement, social environment, psychological and cognitive characteristics. Considering these sets of factors jointly in the same models provides an opportunity to compare their respective influence on APE and DAL in a more systematic way than in previous research. Accordingly, this study aims to address the following two research questions:</p> <p></p> <ulist> <item> RQ1: What is the relative weight of the different types of factors in predicting APE for first year university students?</item> <p></p> <item> RQ2: What is the relative weight of the different types of factors in predicting DAL for first year university students?</item> </ulist> <p>A more specific question related to RQ1 is whether or not DAL itself is an important factor in APE. By investing RQ1 and RQ2 in the same study, we will also be able to compare the weight of the factor predicting APE and DAL, and determine whether there are some common important factors and, accordingly, common possible levers to activate.</p> <hd id="AN0182505730-13">5. Method</hd> <p></p> <hd id="AN0182505730-14">5.1 Participants and procedure</hd> <p>A total of 7301 first-year students at a French university were invited to complete a questionnaire and 2011 students responded to all questions (27.5% of respondents). This sample was composed of students enrolled in ten different educational programmes, in science and/or humanities. The participants had a mean age of 18.35 years (<emph>SD</emph> = 1.46), and 58.8% were female.</p> <p>The questionnaire was administered electronically two months after the start of their first year of study. This delay had two purposes: to avoid including in the sample students who change programmes during the period that authorizes it administratively, and to include students who drop out during the year (the later in the year the questionnaire being administered, the fewer students who drop out being taken into account). Prior to completing the questionnaire, all participants provided informed consent. Students with missing data and problematic outliers were removed from the sample.</p> <hd id="AN0182505730-15">5.2. Data collection</hd> <p>The study is based on data provided by the university administration and data collected through the questionnaire. The latter was composed of 61 closed-ended questions. Some of them were adapted from the literature, while others were created (for the complete questionnaire, see Supplemental material A). The question wording was adjusted after a qualitative pre-test based on interviews with five students and three successive tests with 980 students in total.</p> <hd id="AN0182505730-16">5.3 Outcome variables</hd> <p>To run the RF algorithm, the outcome variables have been defined in binary form. The first outcome variable was APE, defined as passing or not passing midterm exams. According to university regulations, a student passes his or her exams if the average of his or her grades is equal to or higher than 10 on a scale of 0–20. The data regarding students' grades were supplied by the university administration. It should be noted that the midterm exams are the first exams that students take and that the exams they take at the end of the first year are generally similar in format.</p> <p>The second outcome variable was DAL, defined as above or below the mean of the students on a scale based on thirteen items (McDonald's <emph>ω</emph> =.87). The DAL scale consisted of four subscales: integrative approach expressing a structured thinking and composed of five items (<emph>ω</emph> =.80), reflective approach composed of four items (<emph>ω</emph> =.69), interactions-with-peers approach composed of two items (<emph>ω</emph> =.86), and interactions-with-teachers approach composed of two items (<emph>ω</emph> =.80). Integrative and reflective approaches included items adapted from the Biggs, Kember, and Leung ([<reflink idref="bib6" id="ref86">6</reflink>]) and Entwistle and Mac Cune ([<reflink idref="bib27" id="ref87">27</reflink>]) questionnaires. The other two subscales are new and describe students' intent to better understand what is being taught through social interactions. For these items related to DAL, students were asked to respond on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree).</p> <hd id="AN0182505730-17">5.4 Factor variables</hd> <p></p> <hd id="AN0182505730-18">5.4.1 Demographics, social background, educational background, and context of the educational...</hd> <p>The university administration supplied demographics on the students (age, gender, native or foreigner), on their social background (parents' occupation, whether they had a scholarship, or a job), on their educational background (high school graduation mark, type of high school diploma, whether they were just graduated from high school, or repeated their first year of university), and on the context of the educational programme (educational programme undertaken, type of degree, and whether their educational programme corresponded to their first wish).</p> <hd id="AN0182505730-19">5.4.2 Behavioural engagement</hd> <p>Two items of the questionnaire provided data on student behavioural engagement: course attendance (7-point Likert scale measuring frequency), and the work at home for studies (with five time intervals).</p> <hd id="AN0182505730-20">5.4.3 Social environment</hd> <p>The social environment consisted of the perceived social support and perceived social climate. The perceived social support scale was constructed as the mean size of the network (i.e. number of different sources) for five types of support (financial, learning, course guidance, confidence, and project) (<emph>ω</emph> =.80). Regarding the perceived social climate of the educational programme, students had to position a cursor on a scale ranging from 1 meaning stressful to 7 meaning reassuring, and on another scale ranging from 1 meaning free to 7 meaning supervised.</p> <hd id="AN0182505730-21">5.4.4 Psychological and cognitive characteristics</hd> <p>Motivation was measured by means of the Vallerand et al. ([<reflink idref="bib60" id="ref88">60</reflink>]) scale, in line with SDT and adapted to the university context. It was based on seven subscales, each composed of four items: intrinsic motivation to know (<emph>ω</emph> =.88), to stimulation (<emph>ω</emph> =.83), to accomplishment (<emph>ω</emph> =.87), identified extrinsic motivation (<emph>ω</emph> =.80), introjected extrinsic motivation (<emph>ω</emph> =.83), external extrinsic motivation (<emph>ω</emph> =.80), and amotivation (<emph>ω</emph> =.86).</p> <p>Evaluativism consisted of four items (<emph>ω</emph> =.62) corresponding to the four kinds of epistemic beliefs distinguished by Hofer and Pintrich ([<reflink idref="bib33" id="ref89">33</reflink>]): beliefs concerning the uncertainty, complexity, source, and justification of knowledge. Note that such a relatively low value for the reliability coefficient is not unusual (DeBacker et al. [<reflink idref="bib19" id="ref90">19</reflink>]). In several studies, the values found and used for measures of epistemic beliefs are around 0.6 or even below 0.6 (e.g. supplementary materials in Schiefer et al. [<reflink idref="bib54" id="ref91">54</reflink>]). This can be explained by the fact that, for some individuals, epistemic beliefs may not form a fully coherent system (Schommer [<reflink idref="bib55" id="ref92">55</reflink>]).</p> <p>Perceived difficulties in studying consisted of seven items (<emph>ω</emph> =.83) that reflect the main difficulties identified in the literature (Ainscough et al. [<reflink idref="bib2" id="ref93">2</reflink>]; Cameron and Rideout [<reflink idref="bib11" id="ref94">11</reflink>]; Trautwein and Bosse [<reflink idref="bib59" id="ref95">59</reflink>]): difficulties with course guidance, in understanding courses, in understanding course issues, in understanding course instructions, due to content complexity, with learning methods, and in organizing work out of class.</p> <p>For all the items related to motivation, amotivation, evaluativism, and perceived difficulties, students were asked to respond on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree).</p> <p>For APE as the outcome variable, the four subscales of DAL mentioned above were also considered.</p> <hd id="AN0182505730-22">5.5 The two models</hd> <p>As two outcome variables were investigated in the study, two different models were developed: a model with 31 factors for APE (Figure 1) and another with 27 factors for DAL (Figure 2).</p> <p>Graph: Figure 1. The factors considered to predict APE.</p> <p>Graph: Figure 2. The factors considered to predict DAL.</p> <hd id="AN0182505730-23">5.6 Statistical analysis</hd> <p>To investigate the relationships between the factor variables and the outcomes variables, we used the RF algorithm, that involves a training phase with a randomly selected part of the observations, the bag sample, and a prediction phase with the remaining observations, the out-of-bag sample (Breiman [<reflink idref="bib8" id="ref96">8</reflink>]; Rosenbusch et al. [<reflink idref="bib51" id="ref97">51</reflink>]). It is based on the method of binary decision trees, which consists of dividing the set of observations thanks to successive binary questions. Each question relates to a factor variable. The order of the questions is determined by their discriminatory power relative to the output variable, with the first question being the most discriminatory. The successive questions allow the construction of subsets of observations that are increasingly homogeneous with respect to the output variable (for example, subsets of students who, for the most part, passed their midterm exams and subsets of students who, for the most part, did not passed them). RF combines a large number of such decision trees, each of which covers a sample of randomly selected observations of the bag. For each split in a given tree, a subset of input variables is randomly selected. To test the RF model, observations from the out-of-bag sample are used. For each of these observations, the model makes a prediction about the output variable by taking into account the predictions of all decision trees and retaining the most frequent prediction. A level of prediction accuracy can then be associated to the model. The contribution of each variable to the prediction can be determined using the mean decrease of the Gini Index, which indicates the purity of a dataset's partition. This value can be translated into a percentage contribution to the prediction of the outcome variable in the model. RF allows for the inclusion of a large number of variables, which may be quantitative (continuous or discrete) or nominal, in a single model and does not make the assumption of linear relationships with the outcome variable.</p> <p>The RF analyses were performed with R software and the randomForest package. To optimize the prediction accuracy, the following three parameters were tuned for each RF model: number of randomly selected input variables for each split, number of decision trees, and number of splits. The reliability, descriptive and correlation analyses were performed by means of JASP software (Version 0.16.2).</p> <hd id="AN0182505730-24">6. Results</hd> <p>Descriptive statistics for all variables in both models, as well as correlations between the two output variables, can be found in Supplemental material B.</p> <hd id="AN0182505730-25">6.1. Relative weight of the factors predicting APE</hd> <p>In the case of APE, the RF analysis resulted in a model with a prediction accuracy of 74.9%. In this model, the percentage of contribution of each variable to the prediction of APE is given in Figure 3 (for the percentages and corresponding means of the Gini index, see Supplemental material C).</p> <p>Graph: Figure 3. Contribution of each variable to the prediction of APE (passing midterm exams).</p> <p>According to this model, the most important factor is the educational programme undertaken (contributing 13.4% to the prediction in the model). Comparing the passing rate between the educational programmes considered in the study, the rate is highest in the case of a preparatory degree for teaching and a Technology University Diploma, and lowest in the case of the Bachelors of economics and science. The second most important factor is the high school graduation mark (9.3%). The relationship between this factor and APE is positive, as indicated by the value of their correlation (Spearman's <emph>ρ</emph> = 0.361, <emph>p</emph> <.001). A third important factor is the occupation of the parents (8.6%). The importance of this factor can be illustrated by the following examples: the percentage of students who succeed in midterm exams is 71.0% when both parents are executives or intermediaries, 60.6% when both parents are employees or workers, and 46.2% when both parents are unemployed. Furthermore, according to this model, DAL is a secondary factor in APE (2.75% to 3.5% depending on the subscale considered). This means that the exams reward students who adopt a DAL only to a limited extent. Demographic data (age, gender and country of origin) are among the least decisive factors. The same applies to behavioural engagement.</p> <hd id="AN0182505730-26">6.2. Relative weight of the factors predicting DAL</hd> <p>In the case of DAL, the RF analysis resulted in a model with a prediction accuracy of 77.0%. In this model, the percentage of contribution of each variable to the prediction of DAL is given in Figure 4 (for the detailed percentages and corresponding means of the Gini index, see Supplemental material C).</p> <p>Graph: Figure 4. Contribution of each variable to the prediction of DAL.</p> <p>In this second model, the most important factors are the three forms of intrinsic motivation: to know (16.6%), to stimulation (15.6%), and to accomplishment (12.2%). Evaluativism is also an important factor, although somewhat weaker (9.7%). The relationship between this factor and DAL is positive, as indicated by the value of their correlation (<emph>ρ</emph> = 0.375, <emph>p</emph> <.001). Another factor with roughly the same weight as evaluativism is the parents' occupation (9.4%). The relationship between this factor and DAL appears to be inverse compared to its relationship with APE, as shown by the following examples: the percentage of students with a DAL score above the mean is 69.2% when both parents are unemployed, 52.7% when both parents are employees or workers, and 52.3% when both parents are executives or intermediaries. Finally, as with APE, demographics and behavioural engagement are among the least decisive factors.</p> <hd id="AN0182505730-27">7. Discussion</hd> <p>The aim of this study was to identify and compare the factors that may influence APE and DAL in the first year of university, using the RF algorithm and taking into account a wide range of factors belonging to a variety of dimensions: demographics, social background, educational background, context of the educational programme, behavioural engagement, social environment, psychological and cognitive characteristics.</p> <p>Regarding APE, the results show the predominant importance of the educational programme undertaken in passing the exam. Behind this variable lie several possible factors, such as discipline, admission conditions, student-teacher ratio, promotion size and teaching methods. A controlled study would be needed to determine which of these factors are the most decisive. It should be noted, however, that the two educational programmes considered in the study with the highest exam success rates are characterized by a selection process based on students' academic records on entry, a high student-teacher ratio (i.e. number of teachers per student) and a small class size, which is not the case for the two educational programmes with the lowest success rates.</p> <p>The findings also show the important weight of students' educational background and confirm previous studies (Clerici, Girald, and Meggiolaro [<reflink idref="bib16" id="ref98">16</reflink>]; Sothan [<reflink idref="bib57" id="ref99">57</reflink>]). Students who perform better in high school grades tend also to perform better in exams at university. This means that high performance in high school exams may be a factor of stability for students in coping with the difficult transition to the university system.</p> <p>Parental occupation is a third important factor in predicting APE. The higher the socio-economic level of the parents' occupations, the more likely students are to succeed in their exams. This result is in line with those of several studies and in particular with that of a study conducted in the same country, France (Brinbaum, Hugrée, and Poullaouec [<reflink idref="bib9" id="ref100">9</reflink>]). This confirms that, in this country, educational inequalities, which are strongly linked to students' socio-economic backgrounds, extend into the first year of university.</p> <p>According to our study, DAL is not a major factor in APE. This finding raises questions about the type of success that is valued in the first year of university. It calls for reflection on assessment methods. Indeed, according to several studies, assessments which require the memorization of knowledge rather than conceptual mastery or detailed answers rather than relating content are not beneficial to students who adopt DAL and, consequently, do not encourage students to adopt such an approach to learning during their studies (Bruinsma [<reflink idref="bib10" id="ref101">10</reflink>]; Dahlgren et al. [<reflink idref="bib18" id="ref102">18</reflink>]; Minbashian, Huon, and Bird [<reflink idref="bib42" id="ref103">42</reflink>]).</p> <p>With respect to DAL, the results point to the preponderant weight of the three forms of intrinsic motivation (i.e. to knowledge, stimulation and achievement), in line with the findings of previous studies (Chue and Nie [<reflink idref="bib15" id="ref104">15</reflink>]; Liu, Ye, and Yeung [<reflink idref="bib38" id="ref105">38</reflink>]; Minbashian, Huon, and Bird [<reflink idref="bib42" id="ref106">42</reflink>]). These results point to possible levers that teachers can activate to lead their students to adopt a more in-depth approach to learning: these levers would consist not only in arousing their interest in the content taught, but also in offering them activities that are stimulating and that give them a sense of achievement. Furthermore, we can see that the decreasing weight of the three forms of extrinsic motivation (i.e. identified, introjected and external) echoes their decreasing value on the SDT self-determination scale (Ryan and Deci [<reflink idref="bib52" id="ref107">52</reflink>]). In other words, the less students' motivation expresses a form of autonomy in the regulation of their activities, the less likely it is to favour DAL.</p> <p>The findings also highlight the important weight of evaluativism, i.e. elaborated epistemic beliefs. The relatively strong link between evaluativism and DAL has been established by several previous studies (Chiu et al. [<reflink idref="bib14" id="ref108">14</reflink>]; Lehmann [<reflink idref="bib35" id="ref109">35</reflink>]; Lin, Liang, and Tsai [<reflink idref="bib36" id="ref110">36</reflink>]). This result points to a second lever for teachers: this would consist in offering students opportunities to discuss the nature of the knowledge taught and the process of constructing and validating this knowledge. Indeed, a number of studies have shown that epistemic beliefs in science remain poor or naïve if the various aspects of the nature of scientific knowledge and its construction and validation are not explicitly addressed (Khishfe [<reflink idref="bib34" id="ref111">34</reflink>]).</p> <p>The results show the relatively high predictive weight of parents' occupation for DAL. However, this factor plays a different role for DAL than for APE. While the relationship between the family's socio-economic level is positive with APE, it is negative with DAL. This result may seem surprising, but it is consistent with the results found in two studies (Schrempft et al. [<reflink idref="bib56" id="ref112">56</reflink>]; Suphi and Yaratan [<reflink idref="bib58" id="ref113">58</reflink>]). One possible explanation could be that socio-economically disadvantaged students are more inclined to learn in depth in order to compensate, consciously or not, for their lower chances of academic success. This result could be related to a study (Macaulay, Webber, and Fraunholz [<reflink idref="bib40" id="ref114">40</reflink>]) showing that students from low socioeconomic backgrounds express strong motivation to develop strategies for success.</p> <p>The study also shows that some factors identified in the literature are weaker than others. This is the case in particular for perceived difficulties and perceived social support, which are relatively less important, although not negligible, in predicting APE and DAL. These results point to other possible levers for teachers, but whose potential influence in favouring APE and DAL is weaker. The first lever is to take greater account of the different types of difficulties experienced by students in their studies, such as difficulties in understanding course issues or course instructions, difficulties due to the complexity of the content, difficulties with learning methods or organizing work out of class (Ainscough et al. [<reflink idref="bib2" id="ref115">2</reflink>]; Cameron and Rideout [<reflink idref="bib11" id="ref116">11</reflink>]; Trautwein and Bosse [<reflink idref="bib59" id="ref117">59</reflink>]). The second lever is to provide more support to students, be it for learning, course guidance or self-confidence (Dupont, Galand, and Nils [<reflink idref="bib25" id="ref118">25</reflink>]).</p> <p>The study found that behavioural engagement, measured by course attendance and work at home for studies, has a relatively weak predictive value for both APE and DAL. In terms of predicting APE, the weight of behavioural engagement is lower than that of the different subscales of DAL. This finding suggests that the quality of a student's work is more important than its quantity. It should be noted that this finding contrasts with other studies in which behavioural engagement is a more important factor in exam success (Diseth et al. [<reflink idref="bib23" id="ref119">23</reflink>]; Sothan [<reflink idref="bib57" id="ref120">57</reflink>]). A possible explanation for this discrepancy may lie in the dependence of the strength of this factor on the educational programme or discipline.</p> <p>Finally, the findings indicate that demographics (age, gender, and country of origin) have negligible weight in predicting both APE and DAL. With regard to country of origin, this result should be treated with caution as foreign students represented only 5.62% of the total sample. As for age and gender, their weak influence is consistent with the fact that contradictory results have been found in the literature, whether in relation to APE (Bruinsma [<reflink idref="bib10" id="ref121">10</reflink>]; Cassidy [<reflink idref="bib13" id="ref122">13</reflink>]; Clerici, Girald, and Meggiolaro [<reflink idref="bib16" id="ref123">16</reflink>]; Craft [<reflink idref="bib17" id="ref124">17</reflink>]) or DAL (Berberoglu and Hei [<reflink idref="bib5" id="ref125">5</reflink>]; Duff et al. [<reflink idref="bib24" id="ref126">24</reflink>]; Gijbels et al. [<reflink idref="bib29" id="ref127">29</reflink>]; Mattick, Dennis, and Bligh [<reflink idref="bib41" id="ref128">41</reflink>]). If the weight of these factors is low, the nature of their relationship with APE or DAL, whether positive or negative, could be more dependent on the context.</p> <hd id="AN0182505730-28">8. Educational implications</hd> <p>This study calls for broadening the perspective on the issue of academic success, in line with previous authors (Van der Zanden et al. [<reflink idref="bib61" id="ref129">61</reflink>]). While passing exams and graduation of students is a major objective of higher education, it should not overshadow another objective that is linked to an important development of students: to become curious, autonomous and reflective in the learning process. In this study, which focused on the first year of university, APE and DAL were used as measures related to these two objectives. According to the findings, these two objectives do not coincide, as DAL is not the most important factor in APE. This result might lead teachers to question their assessment procedures and possibly revise them, so that they reward students adopting DAL more and thereby encourage them to adopt DAL in their learning. In addition, this study allowed us to identify several possible levers that teachers can use to encourage their students to adopt DAL: levers consisting of increasing their intrinsic motivation, in its three forms (i.e. to know, to stimulation and to accomplishment), and the lever consisting of enriching their epistemic beliefs.</p> <hd id="AN0182505730-29">9. Limitations and future directions</hd> <p>This study has several limitations. The RF analyses were conducted by distinguishing students in a binary manner with respect to each outcome variable. It is possible to refine this analysis by partitioning the students into more groups and looking for the most predictive factors that discriminate between these groups. Furthermore, the RF algorithm offers a simple predictive model, linking all the factors directly to the output variable. Some relationships could be indirect, meaning that one factor could influence the output variable through the mediation of another variable. In this respect, structural equation modelling can provide complementary insights. Finally, the results obtained concern only first-year students in a French university. This last limitation points to future research directions. In particular, the present study would deserve to be replicated in first-year students at other universities to determine the extent to which the results can be generalized. It could also be replicated in the same university in the second and third years of study, in order to examine the evolution of the relative predictive weight of the different factors. In particular, we can wonder whether, in the subsequent years of university studies, the high school graduation mark remains an important factor in APE, whether the nature of assessments is changing and tending to give greater importance to DAL, whether the link between evaluativism and DAL remains relatively strong, or whether behavioural engagement is becoming a more decisive factor in APE and DAL.</p> <hd id="AN0182505730-30">Disclosure statement</hd> <p>No potential conflict of interest was reported by the authors.</p> <hd id="AN0182505730-31">Ethical considerations</hd> <p>Prior to completing the questionnaire of the study, all participants provided informed consent in compliance with the European General Data Protection Regulation (EU) 2016/679 and under the supervision of the Data Protector Officer of the university. The study received formal approval of the Research Ethics Committee of the University of Montpellier.</p> <hd id="AN0182505730-32">Data availability</hd> <p>In line with the commitment made to the students surveyed, the complete raw data file is not provided online. They are available on request, as are the codes used.</p> <ref id="AN0182505730-33"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref62" type="bt">1</bibl> <bibtext> Supplemental data for this article can be accessed online at https://doi.org/10.1080/03075079.2024.2338262.</bibtext> </blist> </ref> <ref id="AN0182505730-34"> <title> References </title> <blist> <bibtext> Aditomo, A. 2018. " Epistemic Beliefs and Academic Performance Across Soft and Hard Disciplines in the First Year of College." Journal of Further and Higher Education 42 (4): 482 – 96. https://doi.org/10.1080/0309877X.2017.1281892</bibtext> </blist> <blist> <bibl id="bib2" idref="ref58" type="bt">2</bibl> <bibtext> Ainscough, L., E. Stewart, K. 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  Data: Predicting Performance in Exams and Deep Approach to Learning in First Year University Students: A New Look at Academic Success
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  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Manuel+Bächtold%22">Manuel Bächtold</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8659-5177">0000-0002-8659-5177</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jacqueline+Papet%22">Jacqueline Papet</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4946-4522">0000-0002-4946-4522</externalLink>)<br /><searchLink fieldCode="AR" term="%22Dominique+Barbe+Asensio%22">Dominique Barbe Asensio</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9124-1966">0000-0001-9124-1966</externalLink>)<br /><searchLink fieldCode="AR" term="%22André+Mas%22">André Mas</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6086-1417">0000-0001-6086-1417</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sandra+Borne%22">Sandra Borne</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3209-7826">0000-0002-3209-7826</externalLink>)<br /><searchLink fieldCode="AR" term="%22Appolinaire+Ngoua+Ondo%22">Appolinaire Ngoua Ondo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7106-1447">0000-0002-7106-1447</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Studies+in+Higher+Education%22"><i>Studies in Higher Education</i></searchLink>. 2025 50(2):333-348.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  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="%22Grade+Prediction%22">Grade Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Testing%22">Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22College+Freshmen%22">College Freshmen</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Study%22">Undergraduate Study</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Differences%22">Individual Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Socioeconomic+Background%22">Socioeconomic Background</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Background%22">Educational Background</searchLink><br /><searchLink fieldCode="DE" term="%22Parent+Background%22">Parent Background</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Intellectual+Disciplines%22">Intellectual Disciplines</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Environment%22">Social Environment</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22France%22">France</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/03075079.2024.2338262
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0307-5079<br />1470-174X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study calls for a broadening of the perspective on academic success. While passing exams is an essential objective of higher education, it should not overshadow another important objective which is the development of students' skills, such as becoming curious, autonomous and reflective in the learning process. This study used Academic Performance in Exams (APE) and Deep Approach to Learning (DAL) as measures related to these two objectives. The aim was to identify and compare the factors that may influence APE and DAL. The study was conducted on first-year students (2011) at a French university. It was based on a random forest algorithm and took into account a wide range of factors belonging to different dimensions: demographics, social background, educational background, context of the educational programme, behavioural engagement, social environment, psychological and cognitive characteristics. The results show that the most important factors in predicting APE are the educational programme undertaken, student's educational background and parents' occupation. DAL was not found to be an important factor in APE. Regarding the prediction of DAL, the results point to the predominant weight of intrinsic motivation and the important weight of elaborated epistemic beliefs. In contrast, demographics and behavioural engagement were found to have negligible weight in predicting both APE and DAL. These findings raise questions about the type of success that is valued in the first year of university and call for reflection on assessment methods. They also allow the identification of levers that teachers can activate to support first year students.
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  Data: 2025
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  Data: EJ1459176
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        Value: 10.1080/03075079.2024.2338262
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      – Text: English
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      Pagination:
        PageCount: 16
        StartPage: 333
    Subjects:
      – SubjectFull: Grade Prediction
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Testing
        Type: general
      – SubjectFull: Learning Strategies
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      – SubjectFull: College Freshmen
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      – SubjectFull: Individual Differences
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      – SubjectFull: Student Characteristics
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      – SubjectFull: Socioeconomic Background
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      – SubjectFull: Educational Background
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      – SubjectFull: Parent Background
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      – SubjectFull: Learner Engagement
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      – SubjectFull: Social Environment
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      – SubjectFull: France
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      – TitleFull: Predicting Performance in Exams and Deep Approach to Learning in First Year University Students: A New Look at Academic Success
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