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Yang, Christopher C. Y.; Ogata, Hiroaki – Educational Technology & Society, 2023
Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence…
Descriptors: Blended Learning, Sequential Approach, Notetaking, Electronic Publishing
Faisal Channa; Muhterem Dindar; Andy Nguyen; Rohit Mishra – Scandinavian Journal of Educational Research, 2024
This study explored the sequential interplay between challenges and regulatory processes in high- and low-performing collaborative groups. 66 students from a Finnish higher education institution participated in a collaborative task in groups of three. Approximately 34 h of video data were coded. The sequential analysis revealed that both groups…
Descriptors: Cooperative Learning, High Achievement, Low Achievement, Foreign Countries
Khan, Md Akib Zabed; Polyzou, Agoritsa – International Educational Data Mining Society, 2023
Academic advising plays an important role in students' decision-making in higher education. Data-driven methods provide useful recommendations to students to help them with degree completion. Several course recommendation models have been proposed in the literature to recommend courses for the next semester. One aspect of the data that has yet to…
Descriptors: Course Selection (Students), Learning Analytics, Academic Advising, Decision Making
Kleinman, Erica; Shergadwala, Murtuza N.; Teng, Zhaoqing; Villareale, Jennifer; Bryant, Andy; Zhu, Jichen; Seif El-Nasr, Magy – Journal of Learning Analytics, 2022
Educational technology is shifting toward facilitating personalized learning. Such personalization, however, requires a detailed understanding of students' problem-solving processes. Sequence analysis (SA) is a promising approach to gaining granular insights into student problem solving; however, existing techniques are difficult to interpret…
Descriptors: Problem Solving, Learning Analytics, Decision Making, Educational Technology
Akpinar, Nil-Jana; Ramdas, Aaditya; Acar, Umut – International Educational Data Mining Society, 2020
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern…
Descriptors: Learning Strategies, Blended Learning, Learning Analytics, Student Behavior