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Kil, David; Baldasare, Angela; Milliron, Mark – Current Issues in Education, 2021
Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'être of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy…
Descriptors: Learning Analytics, Academic Achievement, College Students, Electronic Learning
Tang, Hengtao; Dai, Miao; Yang, Shuoqiu; Du, Xu; Hung, Jui-Long; Li, Hao – Distance Education, 2022
The purpose of this research was to apply multimodal learning analytics in order to systemically investigate college students' attention states during their collaborative problem-solving (CPS) in online settings. Existing research on CPS relies on self-reported data, which limits the validity of the findings. This study looked at data in a…
Descriptors: Learning Analytics, College Students, Attention, Cooperative Learning
Ramli, Izzat S. Mohd; Maat, Siti M.; Khalid, Fariza – Pegem Journal of Education and Instruction, 2022
The boom of the 4.0 industrial revolution and the Covid-19 pandemic have changed the teaching and learning process, where digital learning environments have become increasingly necessary and convenient. The application of game-based learning (GBL) provides many benefits, such as helping to improve the quality of the mathematics teaching and…
Descriptors: Computer Games, Educational Games, Game Based Learning, Learning Analytics
Šaric-Grgic, Ines; Grubišic, Ani; Šeric, Ljiljana; Robinson, Timothy J. – International Journal of Distance Education Technologies, 2020
The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning…
Descriptors: Learning Analytics, Intelligent Tutoring Systems, Grouping (Instructional Purposes), Undergraduate Students
Chen, Zhongzhou; Xu, Mengyu; Garrido, Geoffrey; Gunthrie, Matthew W. – Physical Review Physics Education Research, 2020
This study examines whether including more contextual information in data analysis could improve our ability to identify the relation between students' online learning behavior and overall performance in an introductory physics course. We created four linear regression models correlating students' pass-fail events in a sequence of online learning…
Descriptors: Correlation, Electronic Learning, Performance Factors, Learning Analytics
Shimada, Atsushi; Mouri, Kousuke; Taniguchi, Yuta; Ogata, Hiroaki; Taniguchi, Rin-ichiro; Konomi, Shin'ichi – International Educational Data Mining Society, 2019
In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students'…
Descriptors: Student Placement, Learning Activities, Learning Analytics, Cognitive Style