Educational Data Mining and Personalized Support in Online Introductory Physics Courses

Physics has always been a challenging subject for many students. Research also shows a gap between instructional goals and actual student learning in introductory physics courses. This study focuses on two online first-year courses that cover classical mechanics of the physics curriculum at an open university in Canada. Each of the two courses is developed around a textbook and includes a locally created study guide enriched with animated videos, dynamic diagrams, and interactive exercises. This study aims at introducing a simple feature to provide physics students with personalization based on their background knowledge and at examining students’ interactions with the online course materials. Relevant educational data are compiled using checkpoint quizzes, self-reflection questionnaires, examinations, and log data collected through the learning management system (Moodle). In addition, peer faculty feedback is collected. Positive correlations are expected between regular learning behavior and engagement in personalized support and students’ performance on examinations.

[1]  Romiro G. Bautista.  THE EFFECTS OF PERSONALIZED INSTRUCTION ON THE ACADEMIC ACHIEVEMENT OF STUDENTS IN PHYSICS , 2012 .

[2]  P. Bergamin,et al.  IMPLEMENTATION OF AN ADAPTIVE INSTRUCTIONAL DESIGN FOR A PHYSICS MODULE IN A LEARNING MANAGEMENT SYSTEM , 2018 .

[3]  Gerd Kortemeyer,et al.  Work Habits of Students in Traditional and Online Sections of an Introductory Physics Course: A Case Study , 2016 .

[4]  G. Kortemeyer.  It's All in the Data - But What is It? Learning Analytics and Data Mining of Multimedia Physics Courses , 2019, International Journal of Physics and Chemistry Education.

[5]  Florence Martin,et al.  Using Learning Analytics to Enhance Student Learning in Online Courses Based on Quality Matters Standards , 2016 .

[6]  Janet E. Hurn,et al.  Using learning analytics to predict (and improve) student success: a faculty perspective , 2013 .

[7]  Hendrik Drachsler,et al.  Translating Learning into Numbers: A Generic Framework for Learning Analytics , 2012, J. Educ. Technol. Soc..

[8]  Abelardo Pardo,et al.  Understanding students' engagement with personalised feedback messages , 2020, LAK.

[9]  Chun Meng Tang,et al.  What Makes Learning Management Systems Effective for Learning? , 2018, Journal of Educational Technology Systems.

[10]  BY MARGARET MARTINEZ.  What Is Personalized Learning ? , .

[11]  Lynne D. Roberts,et al.  Give Me a Customizable Dashboard: Personalized Learning Analytics Dashboards in Higher Education , 2017, Technology, Knowledge and Learning.

[12]  Kinshuk,et al.  Analyzing Learner Characteristics and Courses Based on Cognitive Abilities, Learning Styles, and Context , 2015 .

[13]  Janet S Twyman,et al.  Handbook on Personalized Learning for States, Districts, and Schools. , 2016 .

[14]  Shanshan Ma,et al.  Personalized Adaptive Learning: An Emerging Pedagogical Approach Enabled by a Smart Learning Environment , 2019 .

[15]  Lillian C. McDermott,et al.  Millikan Lecture 1990: What we teach and what is learned—Closing the gap , 1991 .

[16]  R. Baker.  Using Learning Analytics in Personalized Learning , 2019 .

[17]  Fuhua Lin,et al.  Modeling and Personalizing Curriculum Using Petri Nets , 2010 .

[18]  Li-Ling Chen.  Enhancing Teaching With Effective Data Mining Protocols , 2019 .

[19]  Dragan Gasevic,et al.  Development and Adoption of an Adaptive Learning System: Reflections and Lessons Learned , 2020, SIGCSE.