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Liyanachchi Mahesha Harshani De Silva; María Jesús Rodríguez-Triana; Irene-Angelica Chounta; Gerti Pishtari – Journal of Computing in Higher Education, 2025
With technological advances, institutional stakeholders are considering evidence-based developments such as Curriculum Analytics (CA) to reflect on curriculum and its impact on student learning, dropouts, program quality, and overall educational effectiveness. However, little is known about the CA state of the art in Higher Education Institutions…
Descriptors: Learning Analytics, Curriculum Evaluation, Higher Education, Stakeholders
Lili Aunimo; Janne Kauttonen; Marko Vahtola; Salla Huttunen – Journal of Computing in Higher Education, 2025
Institutions of higher education possess large amounts of learning-related data in their student registers and learning management systems (LMS). This data can be mined to gain insights into study paths, study styles and possible bottlenecks on the study paths. In this study, we focused on creating a predictive model for study completion time…
Descriptors: Data Collection, Learning Management Systems, Study Habits, Time on Task
Yuqin Yang; Yewen Chen; Xueqi Feng; Daner Sun; Shiyan Pang – Journal of Computing in Higher Education, 2024
Helping students gradually develop collective knowledge is critical but generally faces great challenges. Employing a quasi-experimental design, this study investigated the impacts and mechanisms of analytics-supported reflective assessment on the collective knowledge advancement of undergraduates. The experimental group (n = 55) engaged in…
Descriptors: Undergraduate Students, Learning Processes, Learning Analytics, Learner Engagement
Wong, Billy Tak-ming; Li, Kam Cheong; Cheung, Simon K. S. – Journal of Computing in Higher Education, 2023
This paper presents an analysis of learning analytics practices which aimed to achieve personalised learning. It addresses the need for a systematic analysis of the increasing amount of practices of learning analytics which are targeted at personalised learning. The paper summarises and highlights the characteristics and trends in relevant…
Descriptors: Learning Analytics, Individualized Instruction, Context Effect, Stakeholders
Amida, Ademola; Herbert, Michael J.; Omojiba, Makinde; Stupnisky, Robert – Journal of Computing in Higher Education, 2022
The purpose of this mixed-methods study was to explore factors affecting faculty members' motivation to use learning analytics (LA) to improve their teaching. In the quantitative phase, 107 faculty members completed an online survey about their motivation to use LA. The results showed that cost, utility, attainment value, and competence all…
Descriptors: Teacher Motivation, Teacher Effectiveness, College Faculty, Learning Analytics
Muljana, Pauline Salim; Luo, Tian – Journal of Computing in Higher Education, 2021
Studies in learning analytics (LA) have garnered positive findings on learning improvement and advantages for informing course design. However, little is known about instructional designers' perception and their current state of LA-related adoption. This qualitative study explores the perception of instructional designers in higher education…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Attitudes
Jeongwon Lee; Dongho Kim – Journal of Computing in Higher Education, 2025
Although learning analytics dashboards (LADs) are being recognized as tools that can enhance engagement--a crucial factor for the success of asynchronous online higher education--their impact may be limited without a solid theoretical basis for motivation. Furthermore, the processes through which students make decisions using dashboards and engage…
Descriptors: Self Determination, Learning Analytics, Educational Technology, Learner Engagement
Jones, Kyle M. L.; VanScoy, Amy; Bright, Kawanna; Harding, Alison; Vedak, Sanika – Journal of Computing in Higher Education, 2022
Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. In…
Descriptors: College Faculty, Teacher Attitudes, College Students, Privacy
Li, Xu; Ouyang, Fan; Chen, WenZhi – Journal of Computing in Higher Education, 2022
Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students' characteristics within a group. But there are two dominant challenges…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Group Dynamics, Grouping (Instructional Purposes)
Klein, Carrie; Lester, Jaime; Rangwala, Huzefa; Johri, Aditya – Journal of Computing in Higher Education, 2019
Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives…
Descriptors: Learning Analytics, Barriers, Incentives, Adoption (Ideas)

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