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Non-Cognitive Factors of Learning as Early Indicators of Students at-Risk of Failing in Tertiary Education

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Abstract

It is increasingly evident that significant numbers of college students do not complete the courses on which they enrol, particularly for courses with lower entry requirements (ACT, 2012). Enrolment numbers to tertiary education are increasing, as is diversity in student populations (OECD, 2013). This adds to the challenge of both identifying students at risk of failing, and provisioning appropriate supports to enable all students perform optimally (Mooney et al., 2010).

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... Table I summarizes the algorithms that have been used to predict students' academic performance. [35], [38], [39], [40], [41], [42], [43], [44], [19], [45], [46], [21], [22], [17], [47], [26], [27], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [13], [31], [58], [59], [10], [29] 2 Decision Tree [43], [60], [23], [26], [51], [53], [55], [61] 3 SVM [45], [24], [17], [26], [53], [13] 4 NB [23], [26], [51], [53], [55], [61] 5 K-NN [26], [62], [51], [53], [61] 6 Ensemble [53], [61], [63] However, current research trends focus on educational data mining (EDM) methods to study the same issues. EDM methods have matured over the years and can now achieve high accuracy and be robust against missing data. ...
... Table I summarizes the algorithms that have been used to predict students' academic performance. [35], [38], [39], [40], [41], [42], [43], [44], [19], [45], [46], [21], [22], [17], [47], [26], [27], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [13], [31], [58], [59], [10], [29] 2 Decision Tree [43], [60], [23], [26], [51], [53], [55], [61] 3 SVM [45], [24], [17], [26], [53], [13] 4 NB [23], [26], [51], [53], [55], [61] 5 K-NN [26], [62], [51], [53], [61] 6 Ensemble [53], [61], [63] However, current research trends focus on educational data mining (EDM) methods to study the same issues. EDM methods have matured over the years and can now achieve high accuracy and be robust against missing data. ...
... Table I summarizes the algorithms that have been used to predict students' academic performance. [35], [38], [39], [40], [41], [42], [43], [44], [19], [45], [46], [21], [22], [17], [47], [26], [27], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [13], [31], [58], [59], [10], [29] 2 Decision Tree [43], [60], [23], [26], [51], [53], [55], [61] 3 SVM [45], [24], [17], [26], [53], [13] 4 NB [23], [26], [51], [53], [55], [61] 5 K-NN [26], [62], [51], [53], [61] 6 Ensemble [53], [61], [63] However, current research trends focus on educational data mining (EDM) methods to study the same issues. EDM methods have matured over the years and can now achieve high accuracy and be robust against missing data. ...
... In different studies, [21], [22], [23] used survey questionnaire techniques to collect student intrinsic and personality data that are not readily available in the database for predicting student performance They measured the effects of personality traits, learning styles, personality, learning strategies and motivation factors and psychological well-being on the academic performance of students. ...
... The result shows a strong correlation between student mental condition and their performance. Also, [23] used a short questionnaire made up of five different personality factors along with learning style of the student, their psychological well-being as well as educational achievement on academic performance. Moreover, [21] used personality, motivation and learning strategies variables gathered between the year 2010-2012 alongside six different classification algorithms to predict student learning progression and achievement. ...
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... In different studies, [21], [22], [23] used survey questionnaire techniques to collect student intrinsic and personality data that are not readily available in the database for predicting student performance They measured the effects of personality traits, learning styles, personality, learning strategies and motivation factors and psychological well-being on the academic performance of students. ...
... The result shows a strong correlation between student mental condition and their performance. Also, [23] used a short questionnaire made up of five different personality factors along with learning style of the student, their psychological well-being as well as educational achievement on academic performance. Moreover, [21] used personality, motivation and learning strategies variables gathered between the year 2010-2012 alongside six different classification algorithms to predict student learning progression and achievement. ...
... Likewise, Gray, Mcguinness, and Owende studied these skills and concluded that these are related to motivation, personality, learning approaches and self-regulation which have significant association with students' academic attainments. 21 These skills are developed slowly and gradually as the child grows up. The teacher first finds out the type of non-cognitive skills in a child and then develop the same through practicing from time to time. ...
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... The way we react to stress varies greatly between individuals, and the students need develop personal resources to successfully progress through higher education, despite its constant challenges (19). Recent years have seen growing interest in these resources what are called non-cognitive variables-also known as personal skills, personal qualities, character traits, psychosocial skills, and soft skills (20-23)-for their important impact on educational achievement, success in the job market, career and life success, and well-being (24,25). Among the non-cognitive skills that may increase vulnerability to stress, or, constitute protective resources for coping, two important constructs have been emphasized, and will be addressed in this study: personality traits (BF model) and resilience (19). ...
... The way we react to stress varies greatly between individuals, and the students need develop personal resources to successfully progress through higher education, despite its constant challenges (19). Recent years have seen growing interest in these resources what are called non-cognitive variables-also known as personal skills, personal qualities, character traits, psychosocial skills, and soft skills (20-23)-for their important impact on educational achievement, success in the job market, career and life success, and well-being (24,25). Among the non-cognitive skills that may increase vulnerability to stress, or, constitute protective resources for coping, two important constructs have been emphasized, and will be addressed in this study: personality traits (BF model) and resilience (19). ...
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The aim of this cross-sectional study was to establish predictive relationships of the Big Five personality factors (according to their self-regulatory level), together with resilience (proactive and reactive factors), for factors and symptoms of academic stress related to teaching and learning in the University context. A total of 405 female undergraduate students were selected, and completed questionnaires that had been previously validated in Spanish University students (Big Five personality factors, resilience, and academic stress symptoms and factors). A linear, ex-post facto design was used, including linear regression, Structural Equation Modeling (SEM), and mediational analyses. Specific linear regression showed the expected gradation: that self-regulatory personality factors (conscientiousness, extraversion) were positive linear predictors of proactive resilience, as well as significant negative predictors of stress factors and symptoms of academic stress; while the non-regulatory personality factors (openness to experience, agreeableness) showed little relationship. By contrast, the dysregulatory personality factor (neuroticism) was a negative predictor of proactive resilience, a positive predictor of reactive resilience, and positively predicted academic stress factors in the teaching and learning process, as well as stress symptoms. SEM general analysis showed that personality factors positively predicted resilience, and resilience negatively predicted factors and symptoms of academic stress. Specific mediational model analysis, with each personality factor, confirmed the different mediating relationships that appeared in the linear regression analyses. These results are discussed from the perspective of promoting resilience and healthy personalities in the University context. Implications for addressing academic stress at University are discussed.
... In a different way, the works of [13], [26] and [11] utilized survey questionnaire techniques for collecting the student's personal and intrinsic data that are not clearly accessible over the database for the prediction of student's performance. Furthermore, the evaluation of the factors such as learning styles, personality traits, strategies of learning and motivational factors have also been analyzed. ...
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... While research in Ireland has identified a significant relationship between prior educational attainment within the Leaving Certificate (points achieved) and undergraduate programme completion levels (Morgan et al., 2001). Also within the Irish context a study of first year students over a three year period, 2010 -2012 found that age, prior academic performance (particularly overall performance i.e. total Leaving Certificate points) and mathematics were statistically significant in predicting academic performance (Gray et al., 2016), while a study by the HEA published in 2010 identified prior educational attainment and in particular mathematics, and to a lesser degree English, as predictors of student academic performance within the Irish higher education sector (Mooney et al., 2010). ...
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... An important study is the meta-analysis by Richardson et al. (2012), which found that performance self-efficacy had the largest correlation with GPA followed by high school GPA, ACT and grade goal. Gray et al. (2016) examined factors of learning as early indicators of students at-risk. Their study found that factors such as age, prior academic achievement, study efforts, self-efficacy and deep learning approach significantly predicted failing students. ...
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Intrinsic and extrinsic types of motivation have been widely studied, and the distinction between them has shed important light on both developmental and educational practices. In this review we revisit the classic definitions of intrinsic and extrinsic motivation in light of contemporary research and theory. Intrinsic motivation remains an important construct, reflecting the natural human propensity to learn and assimilate. However, extrinsic motivation is argued to vary considerably in its relative autonomy and thus can either reflect external control or true self-regulation. The relations of both classes of motives to basic human needs for autonomy, competence and relatedness are discussed.
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This personal historical article traces the development of the Big-Five factor structure, whose growing acceptance by personality researchers has profoundly influenced the scientific study of individual differences. The roots of this taxonomy lie in the lexical hypothesis and the insights of Sir Francis Galton, the prescience of L. L. Thurstone, the legacy of Raymond B. Cattell, and the seminal analyses of Tupes and Christal. Paradoxically, the present popularity of this model owes much to its many critics, each of whom tried to replace it, but failed. In reaction, there have been a number of attempts to assimilate other models into the five-factor structure. Lately, some practical implications of the emerging consensus can be seen in such contexts as personnel selection and classification.
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This book provides a comprehensive state-of-the-art review of personality and intelligence, as well as covering other variables underlying academic and occupational performance. Personality and Intellectual Competence is a unique attempt to develop a comprehensive model to understand individual difference by relating major personality dimensions to cognitive ability measures, academic and job performance, and self-assessed abilities, as well as other traditional constructs such as leadership and creativity. It will be essential reading for anyone interested in personality, intelligence, and the prediction of future achievement in general. Personality and Intellectual Competence is an outstanding account of the relationship between major individual differences constructs. With its informative summary of the last century of research in the field, this book provides a robust and systematic theoretical background for understanding the psychological determinants of future achievement. The authors have sought to combine technical expertise with applied interests, making this a groundbreaking theoretical tool for anyone concerned with the scientific prediction of human performance. © 2005 by Lawrence Erlbaum Associates, Inc. All rights reserved.
Conference Paper
This paper presents evidence based outcomes for learners in third level education from the Continue IT: Leaning Styles Theme. The project is innovative in that it is a mainstream project that originated from previous projects and research into including students with disabilities and learning difficulties into higher education. This project, like the past projects it is built on, took interdisciplinary and multidisciplinary approaches to the development of inclusive solutions for learners with disabilities. The models of identification and support designed as a result of these previous projects have now been used to enhance learner outcomes overall in terms of retention, attendance, performance and achievement.
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This paper examines the concept of learner dispositions empirically and theoretically based on two related studies: one undertaken in the United Kingdom exploring students learning power, identity and their engagement in learning; and one undertaken in Australia, which explored the relationship between learning power and Dweckian self-theories. Three different measures of dispositions are used. Two of these – learning power and self-theories – approach dispositions as malleable but relatively slow to change attributes, while the third considers dispositions as potentially more contextually responsive. The two studies had the measure of learning power in common, enabling a statistical as well as a theoretical comparison between the two studies’ models of learning dispositions and their contribution to the notion of engagement. The implications of these related studies are that, in order to foster deep engagement in learning, pedagogical attention needs to be paid to the formation of learning identity and the development of learning dispositions in the process of knowledge construction. While the different approaches to conceptualising dispositions were broadly compatible, each provided a different insight into this complex concept and suggests different but related pedagogical strategies for building engagement. The paper concludes with an exploration of the implications for dispositional research of autopoetic theory as an integrating conceptual framework.
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The current study investigates the influence of manipulatives used in combination with traditional approaches to mathematics education and how varying amounts of time spent on manipulative use influence student achievement across different learning styles. Three learning environments were created that incorporated varying proportions of traditional teaching approaches and manipulative methods. In one of the learning environments, the teacher used strictly lecture- and exercise-based teaching activities, which are more conducive to abstract learning. Abstract learners showed higher academic performance compared with concrete learners in the environment where only traditional methods were used. For the other two environments, which utilised varying combinations of manipulative tools and traditional methods, the differences in the mathematics achievement levels among students of varying learning styles were not statistically significant. The study also showed that concrete learners demonstrated higher performance in mathematics when manipulatives were used than did their counterparts in the environment where only abstract activities were used; however, in the third learning environment, increasing the amount of manipulative use did not provide an extra benefit to concrete learners.
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The sixteen personality factor questionnaire (16PF) The Sixteen Personality Factor Questionnaire (16PF) is a comprehensive measure of normal-range personality found to be effective in a variety of settings where an in-depth assessment of the whole person is needed. The 16PF traits, presented in Table 7.1 , are the result of years of factor-analytic research focused on discovering the basic structural elements of personality (Cattell, R.B., 1957, 1973). In addition to discovering the sixteen normal-range personality traits for which the instrument is named, these researchers identified the five broad dimensions — a variant of the ‘Big Five’ factors (Cattell, R.B., 1957, 1970). From the beginning, Cattell proposed a multi-level, hierarchical structure of personality: the second-order global measures describe personality at a broader, conceptual level, while the more precise primary factors reveal the fine details and nuances that make each person unique, and are more powerful in predicting actual behavior. In addition, ...
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This study examined the association between self‐efficacy and self‐rated abilities in conjunction with adjustment and academic performance with a diverse sample of 271 undergraduate college students with majors in the liberal arts. Significant positive associations between and among the constructs in different combinations were found. The discussion focuses on the theoretical and practice implications of the results and offers suggestions for further research.
Conference Paper
There is a well-established literature examining the relationships between epistemology (the nature of knowledge), pedagogy (the nature of learning and teaching), and assessment. Learning Analytics (LA) is a new assessment technology and should engage with this literature since it has implications for when and why different LA tools might be deployed. This paper discusses these issues, relating them to an example construct, epistemic beliefs -- beliefs about the nature of knowledge -- for which analytics grounded in pragmatic, sociocultural theory might be well placed to explore. This example is particularly interesting given the role of epistemic beliefs in the everyday knowledge judgements students make in their information processing. Traditional psychological approaches to measuring epistemic beliefs have parallels with high stakes testing regimes; this paper outlines an alternative LA for epistemic beliefs which might be readily applied to other areas of interest. Such sociocultural approaches afford opportunity for engaging LA directly in high quality pedagogy.
Conference Paper
This paper describes the results on research work performed by the Open Academic Analytics Initiative, an on-going research project aimed at developing an early detection system of college students at academic risk, using data mining models trained using student personal and demographic data, as well as course management data. We report initial findings on the predictive performance of those models, their portability across pilot programs in different institutions and the results of interventions applied on those pilots.
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The main purpose of this study is to unravel the impact of the Big Five personality factors on academic performance. We propose a theoretical model with conditional indirect effects of the Big Five personality factors on academic performance through their impact upon academic motivation. To clarify the mixed results of previous studies concerning the impact of neuroticism, we suggest a moderating role of self-efficacy. Hierarchical, moderated mediation and mediated moderation regression analyses were performed on longitudinal data collected from 375 students of a University college in Belgium. The findings revealed a positive indirect effect of neuroticism on academic performance at higher levels of self-efficacy, complemented by a positive direct effect of neuroticism at lower levels of self-efficacy. Finally, this study showed that conscientiousness positively affected academic performance indirectly through academic motivation, but also that it is a condition for the indirect impact of extraversion, neuroticism, and conscientiousness.
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One of the hallmarks of adolescent and adult development of expert performance is its self regulation. This paper reviews different approaches to assessing the use of self-regulated learning (SRL) strategies in high-school and college students and their ability to predict academic performance. The current study assesses the use of SRL strategies with interviews and diaries and their relation to grade point average (GPA) in sixty upper-level college students majoring in science. Their diaries revealed that students with high, average, and low GPAs (assessed before the start of the semester) differed in overall use of SRL strategies and in the use of particular strategies during specific weeks. Methods of assessing and understanding differences in adult self-regulation and subsequent academic performance are evaluated and discussed.
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We examined motivational orientations, cognitive–metacognitive strategies, and resource management in predicting academic achievement. Undergraduates (407) completed the Motivated Strategies Learning Questionnaire, Implicit Theories of Intelligence Scale, Achievement Goal Inventory, and self-reported grade point average. A MANCOVA (controlling for sex and age) indicated that low self-efficacy students tended to believe intelligence is innate and unchangeable and high self-efficacy students pursued mastery goals involving challenge and gaining new knowledge as well as performance goals involving good grades and outperforming others. Further, hierarchical multiple regression analysis indicated that self-efficacy, effort regulation, and help-seeking predicted 18% of the variance in GPA. Interestingly, effort regulation partially mediated the relationship between self-efficacy and GPA. Overall, self-efficacious students are able to achieve academically because they monitor and self-regulate their impulses and persist in the face of difficulties. We discuss implications of these findings for educators seeking to strengthen both self-efficacy and effort regulation towards increasing academic achievement.
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Personality and learning styles are both likely to play significant roles in influencing academic achievement. College students (308 undergraduates) completed the Five Factor Inventory and the Inventory of Learning Processes and reported their grade point average. Two of the Big Five traits, conscientiousness and agreeableness, were positively related with all four learning styles (synthesis analysis, methodical study, fact retention, and elaborative processing), whereas neuroticism was negatively related with all four learning styles. In addition, extraversion and openness were positively related with elaborative processing. The Big Five together explained 14% of the variance in grade point average (GPA), and learning styles explained an additional 3%, suggesting that both personality traits and learning styles contribute to academic performance. Further, the relationship between openness and GPA was mediated by reflective learning styles (synthesis-analysis and elaborative processing). These latter results suggest that being intellectually curious fully enhances academic performance when students combine this scholarly interest with thoughtful information processing. Implications of these results are discussed in the context of teaching techniques and curriculum design.
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We performed a meta-analytic path analysis of an abbreviated version of social cognitive career theory's (SCCT) model of work performance (Lent, Brown, & Hackett, 1994). The model we tested included the central cognitive predictors of performance (ability, self-efficacy, performance goals), with the exception of outcome expectations. Results suggested that a slightly modified version of the model, incorporating a path between ability and goals, provided adequate fit to the data. In addition, we examined alternative pathways through which conscientiousness, a Big 5 personality variable, might operate in concert with the social cognitive variables in predicting work performance. Good fit was found for a model in which conscientiousness is linked to performance both directly and indirectly via self-efficacy and goals. The implications of these results for SCCT, future research, and practical efforts to facilitate work performance are discussed.
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A substantial body of evidence verifies that perceived self-efficacy operates as a common mechanism through which changes are achieved by diverse modes of influence, across markedly diverse spheres of functioning, with heterogeneous populations, and under differing life conditions. The scope of the organizational applications of perceived self-efficacy summarizes briefly before presenting the principles for altering efficacy belief systems. The brief review of its scope addresses the challenge of constructing a parsimonious theory of broad generalizability. To begin with, perceived self-efficacy is an influential determinant of career choice and development. The higher the people's perceived efficacy to fulfill educational requirements and occupational roles the wider the career options they seriously consider pursuing, the greater the interest they have in them, the better they prepare themselves educationally for different occupational careers, and the greater their staying power in challenging career pursuits.
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Results of 562 studies were integrated by meta-analysis to show the nature, effects, and treatment of academic test anxiety. Effect sizes were computed through the method invented by Glass (Glass, McGaw, & Smith, 1981). Correlations and effect-size groups were tested for consistency and significance with inferential statistics by Hedges and Olkin (1985). Test anxiety (TA) causes poor performance. It relates inversely to students’ self-esteem and directly to their fears of negative evaluation, defensiveness, and other forms of anxiety. Conditions (causes) giving rise to differential TA levels include ability, gender, and school grade level. A variety of treatments are effective in reducing test anxiety. Contrary to prior perceptions, improved test performance and grade point average (GPA) consistently accompany TA reduction.
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The computerised records of a large university were analysed in an attempt to determine which variables served as predictors of degree performance. Age was a powerful predictor: mature students gained better degrees on average than younger students; and mature students with non‐traditional qualifications obtained the best degrees of all. Gender, year of graduation, and type of qualification were weak predictors of performance, but degree classifications were found to differ significantly across disciplines. The results are broadly consistent with previous studies, and suggest that opening access to mature students and to those with non‐traditional qualifications has not led to any diminution of standards. However, variations between disciplines and, in national statistics, between different years, suggest that steps may need to be taken to standardise degree classifications.
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This paper examines the conceptual usefulness of distinguishing between two aspects of learning goals, namely direction and effort. This research builds upon and integrates three bodies of research related to self-regulation of learning, Boekaerts' work on the significance of cognitive, affective and motivational appraisals of study, Kuhl's notion of action control, and our previous work on qualitative differences in students' learning goals.An independent effect for direction and effort in predicting academic performance provided support for the assumption that these two aspects of goals are complementary dimensions of self-regulation of learning. The investigation of relationships between action control, motivation control, perceptions of course directions and students' goals revealed different patterns for direction and effort, as well as changing patterns from task on-set to task off-set. Overall higher levels of effort and performance appeared to require both positive appraisals of the task and volitional efficiency, but action control had a different impact on students' high or low on motivation control, and on their performance of different academic tasks.