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Non-Cognitive Factors of Learning as Early Indicators of Students at-Risk of Failing in Tertiary Education
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).
... 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. ...
Accurate prediction and early identification of student at-risk of attrition are of high concern for higher
educational institutions (HEIs). It is of a great importance not only to the students but also to the
educational administrators and the institutions in the areas of improving academic quality and
efficient utilisation of the available resources for effective intervention. However, despite the different
frameworks and various models that researchers have used across institutions for predicting performance,
only negligible success has been recorded in terms of accuracy, efficiency and reduction of student
attrition. This has been attributed to the inadequate and selective use of variables for the predictive models.
This paper presents a multi-dimensional and an integrated system framework that involves considerable
learners’ input and engagement in predicting their academic performance and intervention in HEIs. The
purpose and functionality of the framework are to produce a comprehensive, unbiased and efficient way of
predicting student performance that its implementation is based upon multi-sources data and database
system. It makes use of student demographic and learning management system (LMS) data from the
institutional databases as well as the student psychosocial-personality (SPP) data from the survey collected
from the student to predict performance. The proposed approach will be robust, generalizable, and
possibly give a prediction at a higher level of accuracy that educational administrators can rely on for
providing timely intervention to students.
... 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. ...
Introduction: Universities prepare students to play their role in the society and equip students with cognitive and non-cognitive skills. Having the significance of non-cognitive skills in mind, the study was designed to investigate the non-cognitive skills of undergraduate students at university level. Therefore, the objectives of the study were to measure non-cognitive skills of university students and to measure the relationship of non-cognitive skills and academic performance among them. Material & Methods: The population of the study was 7189 undergraduate students and a sample of 368 students was selected through simple random sampling for data collection. A questionnaire was developed and validated for gathering the data. The collected data were analysed through mean scores, standard deviation, chi-square test and Pearson correlation. Results: The mean score of the students on self-control was 4.71 having significance indicating that the participants had self-control over themselves. The mean score of the students on academic self-efficacy was 3.96 which again indicates that majority of them were academically self-effective. The mean score on goal setting scale 3.63 which was significant. Conclusion: The study found that the non-cognitive skills, i.e., self-control, self-efficacy, goal-setting and self-regulated learning has positive relationship with academic achievement of students.
... 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). ...
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. ...
Classification techniques have sensed substantial attention in Information Engineering and Technology for the performance prediction and optimisation since few decades. The discovered accuracy of the Classification Model helps the institutional practices and student’s performances. In this paper, a novel Ensemble-based Hybrid Classification Approach (EHCA) has been proposed to be managed to produce improved performance prediction. The mining process with new attributes based on student behaviours has also been incorporated since it creates a great impact on their academic performances. Moreover, the performance of the students is analysed with a set of classifiers in Educational Data Mining (EDM) namely, Naive Bayesian, Support-Vector-Machine(SVM) and J48. Additionally, Ensemble approach is employed for enhancing the classifier performances. Here, the basic Ensemble methods such as Bagging, Classification Boosting and Stacking are used for optimising the results with more precision. Further, the process of Ensemble-based Hybrid Classification is analysed and tested with the dataset collected from Kerala Technological University-SNG College of Engineering(KTU_SNG). The results obtained are compared with the results obtained for utilized single classifiers and the EHCA on the basis of performance efficiency and classification accuracy. The work evidences the efficiency of the proposed approach and proves its reliability in Profound Performance Prediction and Optimisation.
... 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). ...
First year progression rates are a key performance indicator within the higher education sector. Business intelligence can inform initiatives, interventions and supports aimed at specific student cohorts in attempts to improve progression rates. This study investigates prior educational performance, particularly in the Science Technology Engineering and Mathematics (STEM) subject categories, English and foreign languages to identify significant factors predictive of academic performance of computing and engineering first year students within the Institute of Technology Blanchardstown (ITB).
The methodology was quantitative with correlation and multiple regression employed for data analysis. First year computing (n=197) and engineering (n=247) samples were analysed for the academic terms 2013/14, 2014/15 and 2015/16. The attribute accounting for the most variance in the end of first year Grade Point Average (GPA) for the computing sample was found to be the total Leaving Certificate points attained per student. For the engineering sample, the most significant factors predictive of end of first year GPA were Mathematics points achieved in the Leaving Certificate, age and to a lesser degree total Leaving Certificate points.
The results of this analysis support the hypotheses that prior educational attainment in the Leaving Certificate is an important predictor of tertiary academic performance (R2 = .22) and that mathematical ability is an important factor influencing academic performance in engineering programmes. Outside of Mathematics, support for the hypothesis that prior educational attainment in STEM Leaving Certificate subjects is a significant influencing factor in the academic performance of computing and engineering students, proved less conclusive. Also of note, and in contrast to previous studies, Leaving Certificate performance in English was not found to be a predictor of tertiary academic performance within either of the computing and engineering cohorts analysed.
... 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. ...
Purpose
The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.
Design/methodology/approach
The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.
Findings
The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.
Research limitations/implications
First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.
Practical implications
The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.
Originality/value
This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.
Introduction: Academic commitment is an important factor in students' commitment to further education and
Children and adolescents face many challenges in today’s fast changing society and constantly have to overcome increasing levels of adversity in order to achieve success. Enhancing the ability of young people to cope with adversity by training in resilience skills has been the objective of several interventions and programs in the past years. Resilience programs promote the development of protective and preventive factors, both at a personal and social level, that can help to overcome socio-emotional challenges in a positive and adaptive way. Past work has shown the importance of training resilience of youth by leveraging on relevant activities they typically perform in formal and informal learning environments. This Frontiers Research Topics eBook presents 20 peer reviewed papers published in Frontiers in Psychiatry on promoting resilience in young people, with a particular focus on evidence-based resilience programs in promoting mental well-being in youth, both in the short and long term. Several contributions present evaluations of existing and new resilience programs for children and young people.
Because of the massive amount of data in educational databases, predicting student performance is still proving to be increasingly fruitful. This notion is used in a variety of evaluations, and the field of application is continually expanding. Examples include student grades, attendance, semester scores, grade emphasis, structure, well-being, educational program activities, and so on. The amount of information stored in late trends is rapidly increasing. The main goal is to provide an overview of “An astute approach to anticipating student conduct and performance” to create a model for predicting student behavior and performance. In an instructional situation, student performance in training is really important. Student data is stored in the put away database in order to improve student attitudes and behavior. This study demonstrates the patterns and types of breakdowns that should be conducted in order to improve the training process. We anticipated student quirks and produced a report for staff and parents. In light of the display, we provide suggestions on how to improve their presentation. Predicting student achievement is all the more difficult given the massive amount of data in informative archives. This research also looks at how the expected data may be used to determine the most important qualities in a student data set. With the use of instructive information mining processes, we might significantly boost student achievement and accomplishment. It has the potential to benefit students, teachers, and educational institutions.KeywordsAIHAAR detectionFacial gesture recognitionPCAICA
Studies revealed that college readiness promotes college success and higher education student learning outcomes. This study opted to 1) analyze the total effect and the conditional effect of college readiness on college success by university generations and departments; 2) analyze the differences in the probability of college success across departments and university generations; 3) describe the quality of university generations in terms of the conditional effects and the probabilities of college success. The study is an ex post facto research. The Ethiopian 1st, 2nd, and 3rd generation universities; and the National Educational Assessments and Evaluations Agency officers were the population of the study. The total sample size was 551. The Ethiopian General Education School Leaving Certificate Examination Grade Point Average, the Ethiopian Higher Education Entrance Examination score, and the College Cumulative Grade Point Average of the students were sources of the data. Using the Process Procedure for Software Package for Social Sciences, the binomial logistic regression was conducted. Maintaining the highest total conditional effect of college readiness on college success while heightening the probability of college success at a value of college readiness has been interpreted as a trait of the high performing university generation. Highlights • College readiness affects college success. • The conditional effect of college readiness on college success varies by university generations and departments. • The probability of college success at the value of college readiness varies by university generations and departments. • Maintaining the effect of college readiness on college success while heightening the probability of college success is a trait of high-performing university generations.
Purpose:
The aim of the study is to empirically investigate and compare the use of multiple data sources, different classifiers and ensembles of classifiers technique in predicting student academic performance. The study will compare the performance and efficiency of ensemble techniques that make use of different combination of data sources with that of base classifiers with single data source
Design/methodology/approach:
Using a quantitative research methodology, data samples of 141 learners enrolled in the University of the West of Scotland (UWS) were extracted from the institution’s databases and also collected through survey questionnaire. The research focused on three data sources; Student Record System (SRS), Learning Management System (LMS) and Survey and also used three state-of-art data mining classifiers; Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM) for the modelling. In addition, the ensembles of these base-classifiers were used in the student performance prediction and the performances of the seven different models developed were compared using six different evaluation metrics.
Findings:
The results show that the approach of using multiple data sources along with heterogeneous ensemble techniques is very efficient and accurate in prediction of student performance as well as help in proper identification of student –at risk of attrition.
Practical implications:
The approach proposed in this study will help the educational administrators and policy makers working within educational sector in the development of new policies and curriculum on higher education that are relevant to student retention. In addition, the general implications of this research to practice is its ability to accurately help in early identification of students at risk of dropping out of HE from the combination of data sources so that necessary support and intervention can be provided.
Originality/value:
The research empirically investigated and compared the performance accuracy and efficiency of single classifiers and ensemble of classifiers that make use of single and multiple data sources. The study has developed a novel hybrid model that can be used for predicting student performance that is high in accuracy and efficient in performance. Generally, this research study advances the understanding of the application of ensemble techniques to predicting student performance using learner data and has successfully addressed these fundamental questions: a) What combination of variables will accurately predict student academic performance? b)What is the potential of the use of stacking ensemble techniques in accurately predicting student academic performance?
The literature relevant to the combined area of personality and education and learning is summarized, covering almost a century of research and theorizing. Different topics considered important from the aspect of education and learning or from the aspect of personality are represented. For personality this means that broad domains such as motivation and disposition are represented, and that reference is made to topics such as achievement motivation, character education, and goal orientation. The first few decades of the century are coloured by the unitary character-derived construct persistence of motives (Webb, 1915), which has an inherent connection to learning and education. The last three or four decades are characterized by a growing consensus in the personality field about basic constructs such as those represented by the Big-Five factors. In addition, emerging issues covering the relationship between personality and intelligence and personality, motivation, and achievement-orientation are described. This review is necessarily incomplete, but most of the central topics in the field of personality and education have been given a place.
This submission reports on a collaborative project aimed at both promoting awareness of diversity in
learning in higher education, and piloting inclusive teaching practices across a number of academic
disciplines, with a view to developing a sustainable model of optimal access and performance for both
learners and teachers.
The task of accurately profiling a student’s cognitive strengths and preferences is difficult, particularly
amongst an adolescent student population. This paper proposes that it is more important to understand
that diversity exists rather than attempt to accurately classify individual learning styles and behaviour.
Methodologies employed in teaching and learning must reflect an understanding of the range and scope
of learning differences rather than placing a focus on individuals with atypical learning behaviour. This
teaching approach would work in tandem with the need for individual assessment and support for students
with specific learning difficulties.
This chapter reviews the recent research on motivation, beliefs, values, and goals, focusing on developmental and educational psychology. The authors divide the chapter into four major sections: theories focused on expectancies for success (self-efficacy theory and control theory), theories focused on task value (theories focused on intrinsic motivation, self-determination, flow, interest, and goals), theories that integrate expectancies and values (attribution theory, the expectancy-value models of Eccles et al., Feather, and Heckhausen, and self-worth theory), and theories integrating motivation and cognition (social cognitive theories of self-regulation and motivation, the work by Winne & Marx, Borkowski et al., Pintrich et al., and theories of motivation and volition). The authors end the chapter with a discussion of how to integrate theories of self-regulation and expectancy-value models of motivation and suggest new directions for future research.
Society expects physicians to always improve their competencies and to be up to date with developments in their field. Therefore, an important aim of medical schools is to educate future medical doctors to become self-regulated, lifelong learners. However, it is unclear if medical students become better self-regulated learners during the pre-clinical stage of medical school, and whether students develop self-regulated learning skills differently, dependent on the educational approach of their medical school. In a cross-sectional design, we investigated the development of 384 medical students' self-regulated learning skills with the use of the Self-Regulation of Learning Self-Report Scale. Next, we compared this development in students who enrolled in two distinct medical curricula: a problem-based curriculum and a lectured-based curriculum. Analysis showed that more skills decreased than increased during the pre-clinical stage of medical school, and that the difference between the curricula was mainly caused by a decrease in the skill evaluation in the lecture-based curriculum. These findings seem to suggest that, irrespective of the curriculum, self-regulated learning skills do not develop during medical school.
The manner in which students are admitted for third level education and in particular the points system have become matters of increasing public controversy in recent years. While the subject is understandably emotive, a sad feature of many of the arguments presented to date has been the very limited factual
The authors performed a Monte Carlo simulation to empirically investigate the robustness and power of 4 methods in testing mean differences for 2 independent groups under conditions in which 2 populations may not demonstrate the same pattern of nonnormality. The approaches considered were the t test, Wilcoxon rank-sum test, Welch-James test with trimmed means and Winsorized variances, and a nonparametric bootstrap test. Results showed that the Wilcoxon rank-sum test and Welch-James test with trimmed means and Winsorized variances were not robust in terms of type I error control when the 2 populations showed different patterns of nonnormality. The nonparametric bootstrap test provided power advantages over the t test. The authors discuss other results from the simulation study and provide recommendations.
Medical educators attempt to create reliable and valid tests and questionnaires in order to enhance the accuracy of their assessment and evaluations. Validity and reliability are two fundamental elements in the evaluation of a measurement instrument. Instruments can be conventional knowledge, skill or attitude tests, clinical simulations or survey questionnaires. Instruments can measure concepts, psychomotor skills or affective values. Validity is concerned with the extent to which an instrument measures what it is intended to measure. Reliability is concerned with the ability of an instrument to measure consistently.1 It should be noted that the reliability of an instrument is closely associated with its validity. An instrument cannot be valid unless it is reliable. However, the reliability of an instrument does not depend on its validity.2 It is possible to objectively measure the reliability of an instrument and in this paper we explain the meaning of Cronbach’s alpha, the most widely used objective measure of reliability.
It is important to study learning styles because recent studies have shown that a match between teaching and learning styles helps to motivate students´ process of learning. That is why teachers should identify their own teaching styles as well as their learning styles to obtain better results in the classroom. The aim is to have a balanced teaching style and to adapt activities to meet students´ style and to involve teachers in this type of research to assure the results found in this research study. Over 100 students complete a questionnaire to determine if their learning styles are auditory, visual, or kinesthetic. Discovering these learning styles will allow the students to determine their own personal strengths and weaknesses and learn from them. Teachers can incorporate learning styles into their classroom by identifying the learning styles of each of their students, matching teaching styles to learning styles for difficult tasks, strengthening weaker learning styles. The purpose of this study is to explain learning styles, teaching styles match or mismatch between learning and teaching styles, visual, auditory, and kinesthetic learning styles among Iranian learners, and pedagogical implications for the EFL/ESL classroom. A review of the literature along with analysis of the data will determine how learning styles match the teaching styles.
The Open Academic Analytics Initiative (OAAI) is a collaborative, multi‐year grant program aimed at researching issues related to the scaling up of learning analytics technologies and solutions across all of higher education. The paper describes the goals and objectives of the OAAI, depicts the process and challenges of collecting, organizing and mining student data to predict academic risk, and report results on the predictive performance of those models, their portability across pilot programs at partner institutions, and the results of interventions on at‐risk students.
Increasing college participation rates, and diversity in student population, is posing a
challenge to colleges in their attempts to facilitate learners achieve their full academic potential.
Learning analytics is an evolving discipline with capability for educational data analysis that could
enable better understanding of learning process, and therefore mitigate these challenges. The
outcome from such data analysis will be dependent on the range, type, and quality of available
data and the type of analysis performed. This study reviewed factors that could be used to
predict academic performance, but which are currently not systematically measured in tertiary
education. It focused on psychometric factors of ability, personality, motivation, and learning
strategies. Their respective relationships with academic performance are enumerated and
discussed. A case is made for their increased use in learning analytics to enhance the
performance of existing student models. It is noted that lack of independence, linear additivity,
and constant variance in the relationships between psychometric factors and academic
performance suggests increasing relevance of data mining techniques, which could be used to
provide useful insights on the role of such factors in the modelling of learning process.
The objective of this study was to investigate the differential effectiveness of teaching methods on students' academic performance. A sample of 109 undergraduate students from the College's Department of Economic and Business Sciences was used for the study. Using the inferential statistics course, students' assessment test scores were derived from the internal class test prepared by the lecturer. The differential effectiveness of the three teaching methods on student academic performance was analysed using the General Linear Model based univariate ANOVA technique. The F(2, 106) statistic (= 10.125; p < 0.05) and the Tukey HSD post-hoc results indicate significant differences on the effectiveness of the three teaching methods. The mean scores results demonstrate that teacher-student interactive method was the most effective teaching method, followed by student-centered method while the teacher-centered approach was the least effective teaching method.
Learning analytics seeks to enhance the learning process through systematic measurements of learning related data, and informing learners and teachers of the results of these measurements, so as to support the control of the learning process. Learning analytics has various sources of information, two main types being intentional and learner activity related metadata [1]. This contribution aims to provide a practical application of Shum and Crick's theoretical framework [1] of a learning analytics infrastructure that combines learning dispositions data with data extracted from computer-based, formative assessments. The latter data component is derived from one of the educational projects of ONBETWIST, part of the SURF program 'Testing and Test Driven Learning'.
Many competing models have been proposed in the past decade for predicting student knowledge within educational software. Recent research attempted to combine these models in an effort to improve performance but have yielded inconsistent results. While work in the 2010 KDD Cup data set showed the benefits of ensemble methods, work in the Genetics Tutor failed to show similar benefits. We hypothesize that the key factor has been data set size. We explore the potential for improving student performance prediction with ensemble methods in a data set drawn from a different tutoring system, the ASSISTments Platform, which contains 15 times the number of responses of the Genetics Tutor data set. We evaluated the predictive performance of eight student models and eight methods of ensembling predictions. Within this data set, ensemble approaches were more effective than any single method with the best ensemble approach producing predictions of student performance 10% better than the best individual student knowledge model.
This study investigates the impact of self-efficacy, achievement motivation, and learning strategies on students’ academic achievement. Conducting this research is important since there is a lack of educational research on the above research components as an integrated motivational model. Selected undergraduateHstudentsEtsarticipatedHniahelStudy.EirhelStructuralH equationHnodellingl^SEMlll/vas&ppliedHo&nswer the following research Question: What is the impact of self-efficacy beliefs, achievement motivation, and self- learning strategies on academic achievement of the UKM undergraduate students? Scientifically,&esults&ffflirectandHndirectaechnique indicated the effects of self-efficacy beliefs, achievement motivation, and elf learning strategies on academic achievement. Self-efficacy beliefs were significantly enhanced learning attainment.Tel: +60 5 450 5522; Fax: +60 173058930.
Educational researchers have begun recently to identify and study key processes through which students self-regulate their academic learning. In this overview, I present a general definition of self-regulated academic learning and identify the distinctive features of this capability for acquiring knowledge and skill. Drawing on subsequent articles in this journal issue as well as my research with colleagues, I discuss how the study of component processes contributes to our growing understanding of the distinctive features of students' self-regulated learning. Finally, the implications of self-regulated learning perspective on students' learning and achievement are considered.
Since the early 1980s, a bewildering array of methods for constructing bootstrap confidence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap confidence intervals be used. Secondly, which method should be chosen, and thirdly, how should it be implemented. In order to do this, we review the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses. We then present a simulation study, a flow chart for choosing an appropriate method and a survival analysis example. Copyright © 2000 John Wiley & Sons, Ltd.
This study investigated the question of whether a student's expectancy, values and negative affect influenced their deep information processing approach and achievement at the end of the first and second academic year. Five hundred and sixty-five first-year students completed a self-report questionnaire on three different occasions. The departmental administrations provided data on the students' achievement. Covariance analysis indicated that student's expectancy and values positively affected the total number of credits. However, the expected relationship through the deep information processing approach was not found. Even though the analysis showed a relationship between students' expectancy, values and the deep information processing approach, this approach did not affect academic achievement.
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.
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.
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.
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.
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.
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.
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, ...
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.