{'fraction_non_alphanumeric': 0.017035775127768313, 'fraction_numerical': 0.0, 'mean_word_length': 5.871345029239766, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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.