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Lu, Chang; Cutumisu, Maria – International Journal of Educational Technology in Higher Education, 2022
In traditional school-based learning, attendance was regarded as a proxy for engagement and key indicator for performance. However, few studies have explored the effect of in-class attendance in technology-enhanced courses that are increasingly provided by secondary institutions. This study collected n = 367 undergraduate students' log files from…
Descriptors: Learner Engagement, Academic Achievement, Formative Evaluation, Attendance Patterns
Dommett, Eleanor J.; Dinu, Larisa M.; Van Tilburg, Wijnand; Keightley, Samuel; Gardner, Benjamin – International Journal of Educational Technology in Higher Education, 2022
Lecture capture is popular within Higher Education, but previous research suggests that students do not always optimally select content to review, nor do they make the most of specific functions. In the current study conducted in the 2019/20 academic year, we used a repeated-measures crossover design to establish the effects of transcripts with…
Descriptors: Visual Aids, Transcripts (Written Records), Prompting, Lecture Method
Baig, Maria Ijaz; Shuib, Liyana; Yadegaridehkordi, Elaheh – International Journal of Educational Technology in Higher Education, 2020
Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data…
Descriptors: Educational Research, Educational Trends, Learning Analytics, Student Behavior
Ouyang, Fan; Wu, Mian; Zheng, Luyi; Zhang, Liyin; Jiao, Pengcheng – International Journal of Educational Technology in Higher Education, 2023
As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI…
Descriptors: Technology Integration, Artificial Intelligence, Performance, Prediction
Foung, Dennis; Chen, Julia; Cheung, Kin – International Journal of Educational Technology in Higher Education, 2023
College transfer students are those who follow a different trajectory in their higher education journeys than traditional students, completing a sub-degree before pursuing a bachelor's degree at a university. While the possibility of transferring makes higher education accessible to these students, previous studies have found that they face…
Descriptors: College Transfer Students, Student Needs, Barriers, Academic Achievement