Identifying individual differences using log-file analysis: Distributed learning as mediator between conscientiousness and exam grades

Abstract Online learning poses major challenges on students' self-regulated learning. This study investigated the role of learning strategies and individual differences in cognitive abilities, high school GPA and conscientiousness for successful online learning. We used longitudinal log-file data to examine learning strategies of a large cohort (N = 424) of university students taking an online class. Distributed learning, the use of self-tests and a better high school GPA was associated with better exam grades. The positive effect of conscientiousness on exam grades was mediated by distributed learning. Conscientious students distributed their studying over the course of the semester, which in turn, improved grades. The results provide insights into objective study behavior of online students and shed light on the question of how individual differences in cognitive and non-cognitive prerequisites shape the use of learning strategies and exam grades. Practical implications for online course designers and ideas for further research are discussed.

[1]  B. Schmitz,et al.  Applying a web-based training to foster self-regulated learning - Effects of an intervention for large numbers of participants , 2016, Internet High. Educ..

[2]  P. Costa,et al.  Validation of the five-factor model of personality across instruments and observers. , 1987, Journal of personality and social psychology.

[3]  J. Broadbent,et al.  Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review , 2015, Internet High. Educ..

[4]  Youngjun Lee,et al.  A review of online course dropout research: implications for practice and future research , 2011 .

[5]  Shane Dawson,et al.  Mining LMS data to develop an "early warning system" for educators: A proof of concept , 2010, Comput. Educ..

[6]  Mar Pérez-Sanagustín,et al.  Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses , 2017, Comput. Educ..

[7]  B. Means,et al.  The Effectiveness of Online and Blended Learning: A Meta-Analysis of the Empirical Literature , 2013, Teachers College Record: The Voice of Scholarship in Education.

[8]  R. Eynon,et al.  Digital natives: where is the evidence? , 2010 .

[9]  N. Kuncel,et al.  Study Habits, Skills, and Attitudes: The Third Pillar Supporting Collegiate Academic Performance , 2008, Perspectives on psychological science : a journal of the Association for Psychological Science.

[10]  Philip H. Winne,et al.  Studying as self-regulated learning. , 1998 .

[11]  Simon M. Moon,et al.  Exploring the dynamic nature of procrastination: A latent growth curve analysis of academic procrastination , 2005 .

[12]  T. Rammsayer,et al.  Bericht: Studierendenauswahl – Eignungsdiagnostischer Nutzen von Noten aus Schule und Studium , 2008 .

[13]  Michael Schneider,et al.  Variables Associated With Achievement in Higher Education: A Systematic Review of Meta-Analyses , 2017, Psychological bulletin.

[14]  L. Morris,et al.  Tracking student behavior, persistence, and achievement in online courses , 2005, Internet High. Educ..

[15]  Hiroshi Kato,et al.  Procrastination and other learning behavioral types in e-learning and their relationship with learning outcomes , 2015 .

[16]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[17]  Bernhard Schmitz,et al.  Assessing self-regulated learning in higher education: a systematic literature review of self-report instruments , 2016 .

[18]  Bernhard Schmitz,et al.  New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data , 2006 .

[19]  J. Ormrod.  Educational Psychology : Developing Learners , 1997 .

[20]  S. Paunonen,et al.  Big Five personality predictors of post-secondary academic performance , 2007 .

[21]  Kenneth A. Graetz,et al.  Procrastination in Online Courses: Performance and Attitudinal Differences , 2003 .

[22]  A. Poropat.  The Eysenckian personality factors and their correlations with academic performance. , 2011, The British journal of educational psychology.

[23]  A. Poropat.  A meta-analysis of the five-factor model of personality and academic performance. , 2009, Psychological bulletin.

[24]  Daniel Danner,et al.  Die deutsche Version des Big Five Inventory 2 (BFI-2) , 2016 .

[25]  Ruth Woodfield,et al.  Individual differences and undergraduate academic success: the roles of personality, intelligence, and application , 2003 .

[26]  Steve Joordens,et al.  Assessing the effectiveness of a voluntary online discussion forum on improving students' course performance , 2011, Comput. Educ..

[27]  Chris Piech,et al.  Deconstructing disengagement: analyzing learner subpopulations in massive open online courses , 2013, LAK '13.

[28]  Kristy J. Lauver,et al.  Do psychosocial and study skill factors predict college outcomes? A meta-analysis. , 2004, Psychological bulletin.

[29]  P. Kirschner,et al.  The myths of the digital native and the multitasker , 2017 .

[30]  Wendelien van Eerde,et al.  A meta-analytically derived nomological network of procrastination , 2003 .

[31]  D. Nicol,et al.  Formative assessment and self‐regulated learning: a model and seven principles of good feedback practice , 2006 .

[32]  Bryan K. Saville,et al.  Evidence‐based teaching: Tools and techniques that promote learning in the psychology classroom , 2013 .

[33]  Henri C. Schouwenburg,et al.  Trait procrastination and the Big-five factors of personality , 1995 .

[34]  Dragan Gasevic,et al.  Learning analytics to unveil learning strategies in a flipped classroom , 2017, Internet High. Educ..

[35]  R. Bjork,et al.  Self-regulated learning: beliefs, techniques, and illusions. , 2013, Annual review of psychology.

[36]  Anastasios A. Economides,et al.  Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence , 2014, J. Educ. Technol. Soc..

[37]  Dragan Gasevic,et al.  Relevance of learning analytics to measure and support students' learning in adaptive educational technologies , 2017, LAK.

[38]  John Dunlosky,et al.  Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology , 2012 .

[39]  D. Watson,et al.  Procrastination and the five-factor model: a facet level analysis , 2001 .

[40]  Jaclyn Broadbent,et al.  Comparing online and blended learner's self-regulated learning strategies and academic performance , 2017, Internet High. Educ..

[41]  D. Dai,et al.  The relationship between the big-five model of personality and self-regulated learning strategies. , 2007 .

[42]  Elmar Souvignier,et al.  Prognose der Studierfähigkeit , 2005 .

[43]  Regan A. R. Gurung,et al.  Focusing on how students study , 2010 .

[44]  M. Imhof,et al.  The Role of Motivation, Cognition, and Conscientiousness for Academic Achievement. , 2013 .

[45]  C. Abraham,et al.  Psychological correlates of university students' academic performance: a systematic review and meta-analysis. , 2012, Psychological bulletin.

[46]  Damaris Zurell,et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .

[47]  J. Allik,et al.  Personality and intelligence as predictors of academic achievement : A cross-sectional study from elementary to secondary school , 2007 .

[48]  B. Zimmerman.  Becoming a Self-Regulated Learner: An Overview , 2002 .

[49]  Aaron S. Benjamin,et al.  What makes distributed practice effective? , 2010, Cognitive Psychology.

[50]  Florian Domnick,et al.  Intelligence and school grades: A meta-analysis , 2015 .

[51]  Carolyn G. Hart,et al.  Factors Associated With Student Persistence in an Online Program of Study: A Review of the Literature , 2012 .

[52]  Nicolás Morales,et al.  Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses , 2018, Comput. Hum. Behav..

[53]  Kyung Ryung Kim,et al.  The relationship between procrastination and academic performance: A meta-analysis , 2015 .

[54]  Christopher A. Rowland.  The effect of testing versus restudy on retention: a meta-analytic review of the testing effect. , 2014, Psychological bulletin.

[55]  Adrian Furnham,et al.  Personality traits and intelligence predict academic school grades , 2009 .

[56]  Robert J. Blake,et al.  Individual factors and successful learning in a hybrid course , 2012 .