ERIC Number: EJ1480214
Record Type: Journal
Publication Date: 2025-Sep
Pages: 47
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1042-1726
EISSN: EISSN-1867-1233
Available Date: 2024-08-23
Curriculum Analytics in Higher Education Institutions: A Systematic Literature Review
Liyanachchi Mahesha Harshani De Silva1; María Jesús Rodríguez-Triana4; Irene-Angelica Chounta2; Gerti Pishtari3
Journal of Computing in Higher Education, v37 n3 p898-944 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 (HEIs), where dropout is a worldwide problem. Through a systematic literature review, this study summarizes 59 manuscripts about CA published until April 2024. The aim of this review is to identify: (1) WHERE CA was used; (2) WHO were the CA target stakeholders; (3) WHY CA was proposed; (4) WHAT types of data and what types of data gathering and analysis methods are employed; (5) HOW CA was designed, implemented and evaluated and what the stakeholders' role was; and (6) WHICH limitations and constraints affect CA and WHICH recommended practices could contribute to the CA success. Results from our review reveal a considerable number of CA solutions available. However, there is a need for more evidence on how CA solutions inform decision-making among various stakeholders. Thus, more longitudinal studies are needed, involving stakeholders not only in the design but also in the implementation and evaluation of CA solutions. At the same time, findings suggest that including multiple data sources can enrich the analysis and enable triangulation. Additionally, the lack of evidence on the role of CA in dropouts and decision-making in higher education institutions requires more future research on this aspect. Finally, researchers, practitioners, and decision-makers can use the findings obtained in this review to inform future research and practices on how to leverage CA to improve student learning, enhance the learning experience, and reduce student dropouts.
Descriptors: Learning Analytics, Curriculum Evaluation, Higher Education, Stakeholders, Data Collection, Evidence Based Practice, Decision Making
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link-springer-com.bibliotheek.ehb.be/
Publication Type: Journal Articles; Information Analyses; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: 1Tallinn University, School of Digital Technologies, Tallinn, Estonia; 2University of Duisburg-Essen, LF Building, Department of Computer Science and Applied Cognitive Science, Duisburg, Germany; 3University for Continuing Education Krems (Danube University Krems), Krems an der Donau, Austria; 4Universidad de Valladolid, Valladolid, Spain