Active and Independent Learning in Blended Learning. An Analysis of Student Characteristics, Trace Data, and Academic Performance

  • Author / Creator
    Marin, Luis Fernando
  • In Higher Education, instructors provide students with opportunities to develop essential knowledge, competencies and skills. To offer students the highest quality learning experiences, effective instructors analyze their practice, intentionally seek to identify and check their teaching assumptions, and make iterative instructional decisions based on evidence. However, teaching and learning situations are complex and ill-defined and there is a lack of a parsimonious theory of student characteristics and learning conditions that elicit optimal performance in students. Moreover, learning analytics support the processing, analysis and translation of data into actionable knowledge but there is no consensus yet on which interactions are relevant for effective learning. Thus, this study sought to gain a deeper understanding on how and why students thrived and were productively engaged with insights from psychometric information and course trace data. Findings of this study contribute to the literature that seek to 1) translate trace data into actionable knowledge, 2) understand those characteristics and conditions that elicit optimal student performance, or 3) demonstrate how to use academic achievement, trace data, and psychometric characteristics to analyze an instructor’s practice. This study reports on research into 4,150 unique student course interactions clustered within 46 undergraduate student trajectories during an elective blended-learning course. It sought to describe changes in students’ active and independent online interaction behaviours; explore differences in interaction trajectories between students; and examine the relationship between students’ interaction trajectories, psychometric characteristics and levels of achievement. Students’ course interaction trace data was captured by a Learning Management System (LMS). Student characteristics were collected through self-report psychometric instruments completed as supplemental, non-graded, in-class learning activities. Finally, student achievement through total course, summative exams and formative assignment grades. Restricted Maximum Likelihood (REML) linear regressions described interindividual differences in students’ growing proportion of course objects accessed across time (interaction trajectories). Maximum Likelihood (ML) multilevel longitudinal regression models, with changes in the proportion of course objects nested within individuals, significantly described students’ average and individual trajectories of interaction and differences between course assessment periods and conscientiousness levels. Pearson and Spearman correlations found significant relationships between interaction trajectories and personality traits, psychosocial maturity resolutions, self-efficacy, self-regulation, reasons for studying, and major life goals, and between interaction trajectories and student achievement (knowledge/exam grades). Significant negative relationships were found between academic achievement, psychosocial intimacy-isolation resolutions, and major life aspirations to have a family life, to make meaningful contributions, and to have fun. After reflecting on these results, this instructor concluded that the courses, although beneficial, could have better promoted students’ optimal performance by shifting to a more streamlined set of outcomes and a clearer learning path; and by realigning learning activities and intended learning outcomes to better match students’ long-range aspirations. Findings from this study suggest that students should be treated not only as cognitive systems but that students may be productively engaged as human beings continually seeking to realize their own possibilities. Although these propositions may not be statistically generalizable, they may be analytically generalized if replicated in more education contexts.

  • Subjects / Keywords
  • Graduation date
    Spring 2021
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.