Publication Date
In 2025 | 1 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 4 |
Since 2016 (last 10 years) | 6 |
Since 2006 (last 20 years) | 6 |
Descriptor
College Students | 6 |
Learning Analytics | 6 |
Scores | 6 |
Electronic Learning | 3 |
Academic Achievement | 2 |
Attention | 2 |
Computer Science Education | 2 |
Correlation | 2 |
Foreign Countries | 2 |
Lecture Method | 2 |
Markov Processes | 2 |
More ▼ |
Source
International Educational… | 2 |
Current Issues in Education | 1 |
Distance Education | 1 |
Information Systems Education… | 1 |
Journal of Educational… | 1 |
Author
Allen, Laura K. | 1 |
Baldasare, Angela | 1 |
Bekkering, Ernst | 1 |
Butterfuss, Reese | 1 |
Christhilf, Katerina | 1 |
Dai, Miao | 1 |
Di Zhang | 1 |
Du, Xu | 1 |
Hung, Jui-Long | 1 |
Jia-hua Zhang | 1 |
Kil, David | 1 |
More ▼ |
Publication Type
Reports - Research | 5 |
Journal Articles | 4 |
Speeches/Meeting Papers | 2 |
Reports - Descriptive | 1 |
Education Level
Higher Education | 6 |
Postsecondary Education | 6 |
High Schools | 1 |
Secondary Education | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Wen-shuang Fu; Jia-hua Zhang; Di Zhang; Tian-tian Li; Min Lan; Na-na Liu – Journal of Educational Computing Research, 2025
Cognitive ability is closely associated with the acquisition of programming skills, and enhancing learners' cognitive ability is a crucial factor in improving the efficacy of programming education. Adaptive feedback strategies can provide learners with personalized support based on their learning context, which helps to stimulate their interest…
Descriptors: Feedback (Response), Cognitive Ability, Programming, Computer Science Education
Christhilf, Katerina; Newton, Natalie; Butterfuss, Reese; McCarthy, Kathryn S.; Allen, Laura K.; Magliano, Joseph P.; McNamara, Danielle S. – International Educational Data Mining Society, 2022
Prompting students to generate constructed responses as they read provides a window into the processes and strategies that they use to make sense of complex text. In this study, Markov models examined the extent to which: (1) patterns of strategies; and (2) strategy combinations could be used to inform computational models of students' text…
Descriptors: Markov Processes, Reading Strategies, Reading Comprehension, Models
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'ĂȘtre 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
Bekkering, Ernst; Ward, Ted – Information Systems Education Journal, 2020
Student performance in classes can be affected by lack of attendance and attention while in class. This paper examines the effect of student participation on performance in two Computer Science classes. Attendance and attentiveness are automatically recorded by the videoconferencing software used for the classes. Student participation is measured…
Descriptors: Class Activities, Student Participation, Performance Factors, Attendance
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