Call for special issue papers (due: Jan 31, 2024)


Research and Practice in Technology Enhanced Learning
Call for papers for a Special Issue on
Learning Analytics and Evidence-Informed Education”

Guest Editors:
Rwitajit Majumdar (Kumamoto University, Japan)
Hiroaki Ogata (Kyoto University, Japan)
Ting-Chia Hsu (National Taiwan National University, Taiwan)
Gökhan Akçapınar (Hacettepe University, Turkey)
Ramkumar Rajendran (IIT Bombay, India)

The increasing amount of data generated in digital learning contexts provides opportunities to benefit from learning analytics and challenges related to interoperability, privacy, and pedagogical and organizational models. Consequently, new methodologies and technological tools are necessary to analyze and make sense of these data and provide personalized scaffolding and services to stakeholders, including students, faculty/teachers, administrators, and parents. Pedagogical and organizational models must also be incorporated to take advantage of personalized scaffolding and services to ensure productive learning and teaching. In addition, access to data from different sources raises many concerns related to data sharing and interoperability and privacy protection for individuals and business interests for institutions. The objective of the proposed special issue is to gather researchers and stakeholders, including educational technologists, researchers, and practitioners involved in the analysis and deployment process, and to discuss the challenges and approaches evidence-based education with integrating learning analytics (LA) practices into education.

The effectiveness of technology-enhanced educational practices can be analyzed at many different levels within educational institutions. To conceptualize a framework for analyzing evidence of improvement in teaching-learning practices through learning analytics, we look at three different levels, as illustrated from an institutional perspective.
■ The Micro level looks at the impact on the individual students when LA systems or techniques are implemented in a single class or course.
■ The Meso level focuses on institutional implementations. In such cases, practice is adopted and evaluated by more than one faculty member.
■ The Macro level goes beyond one institution and involves policies or practices mandated by the state or national regulatory bodies. Hence it looks at practices that are followed at multiple institutions.

Interested researchers are encouraged to share their research as a paper on either analyzing the evidence on effective LA or relating their contribution from the perspective of the three levels of implementation and adoption of LA. We also call for papers covering technical, theoretical, pedagogical, and organizational perspectives in LA related to the topics concerning the following list (though not restrictive):
■ Making Sense of Learning Analytics
■ Implementation and organizational development with Learning Analytics
■ Pedagogical models and Learning Analytics
■ Data-driven and evidence-based learning design
■ Cross-platforms Learning Analytics
■ Algorithms for analytics based on learning logs
■ Predictive models, visualization, and statistical analysis of learning logs
■ Data sharing for learning analytics
■ Accessible Learning Analytics
■ Standardization and Interoperability of Learning Analytics
■ Challenges and approaches for scaling up Learning Analytics
■ Privacy concerns and policy aspects related to Learning Analytics
■ Learning Analytics in Humanities and Design Education
■ Effectiveness of Learning Analytics interventions
■ Adaptive learning and personalization through analytics
■ Social and emotional Learning Analytics
■ Explainable AI and Learning Analytics


Submission Guidelines

All submissions of the Special Issue should comply with the Author Guidelines of Research and Practice in Technology Enhanced Learning (RPTEL) – the official journal of The Asia-Pacific Society for Computers in Education (APSCE), available on The submissions to the special issue should fit within the scope of RPTEL as described in the Aims and Scope of RPTEL ( Of the utmost importance is that RPTEL publishes the research that well bridges the pedagogy and practice in advanced technology for evidence-based and meaningful educational applications. Papers collected and analyzed only self-reported data that obtained from interview or questionnaire survey without a meaningful educational treatment are NOT within the scope of RPTEL.

Please submit your anonymous manuscript and title page using the submission system available When submitting your manuscript, please include a remark in title page: Submission to RPTEL Special Issue on " Learning Analytics and Evidence-Informed Education."


Important Dates

Jan 31, 2024  ---- Manuscript Submission Due Date

Mar 18, 2024 ---- Review Notification

May 3, 2024   ---- Revision Submission Due Date

June 3, 2024   ---- Final Acceptance Notification

July 1, 2024     ---- Final Camera-ready Manuscript Due Date



Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019). Developing an Early-Warning System for Spotting At-Risk Students by using eBook Interaction Logs. Smart Learning Environments, 6(4), 1-15. doi: 10.1186/s40561-019-0083-4

Biswas, G., Rajendran, R., Mohammed, N., Goldberg, B. S., Sottilare, R. A., Brawner, K., & Hoffman, M. (2019). Multilevel learner modeling in training environments for complex decision making. IEEE Transactions on Learning Technologies, 13(1), 172-185.

Kuromiya H., Majumdar R., & Ogata H. (2020) Fostering Evidence-Based Education with Learning Analytics: Capturing Evidence from Teaching-Learning Logs. in Educational Technology & Society, 23 (4), 14–29.

Ogata H., Majumdar R., Flanagan B. (2023) Learning and Evidence Analytics Framework Bridges Research and Practice for Educational Data Science. Communications of the ACM, July 2023, Vol. 66 No. 7, Pages 72-74

Ogata H., Majumdar R., Flanagan B. (2023) Learning in the Digital Age: Power of Shared Learning Logs to Support Sustainable Educational Practices. accepted in IEICE transactions on information and systems. Vol.E106-D,No.2,pp.-,Feb. 2023

Ogata H., Majumdar R., Flanagan B., Kuromiya H. (2022) Learning Analytics and Evidence-based K12 Education in Japan: Usage of Data-driven Services and Mobile Learning Across Two Years. International Journal of Mobile Learning and Organisation

Rajendran, R., Munshi, A., Emara, M., & Biswas, G. (2018, November). A temporal model of learner behaviors in OELEs using process mining. In Proceedings of ICCE (pp. 276-285).

Önder, A., Akçapınar, G. (2023) Investigating the effect of prompts on learners’ academic help-seeking behaviours on the basis of learning analytics. Education and Information Technologies