A Novel Multi-Dimensional Analysis Approach to Teaching and Learning Analytics in Higher Education

Qingyun LI, Peter DUFFY*, Zhongyang ZHANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)


E-learning platforms are being used in higher education to liberate teaching and learning from the constraints of time, pace, and space. These platforms provide rich media content, collaborative assessment, and analytics tools and support learning through a variety of online possibilities. However, the focus of most of this research is on learning analytics inside e-learning platforms and does not cover other related institutional surveys and educator professional development activities. This paper outlines a novel conceptual approach for the creation of a teaching and learning data warehouse analytics system that utilizes a Multi-Dimensional Analysis approach to teaching and learning analytics across these e-learning platforms in combination with other institutional data sources, such as various institutional surveys, tracking of professional development activities, and analysis of the use of the learning management system. The novel teaching and learning data warehouse analytics system (TLDWAS) is being developed at Lingnan University, a leading liberal arts university in Hong Kong. The genesis of this project is analyzing large volumes of teaching and learning data and presenting a big picture for senior management to gain insight from massive amounts of student courses and teacher evaluation data, fill the research gaps mentioned above, and help educators identify problems and solutions more effectively. The TLDWAS provides a visual interface for identifying some indicators and storing, classifying, and analyzing teaching and learning data in various forms to generate statistics, analyze models, and identify meaningful patterns in the data in order to improve teaching and learning. The TLDWAS not only provides more evidence-based decision-making for senior management but is also capable of being applied to identify patterns and relations pertaining to students’ academic performance, usage of e-learning systems, and associated staff development activities.
Original languageEnglish
Article number96
Issue number4
Early online date5 Jul 2022
Publication statusPublished - Aug 2022

Bibliographical note

Funding Information:
Funding: This research was funded by the Special Grant for Strategic Development of Virtual Teaching and Learning: Creating a Learning Enhancement Analytics Platform/Teaching and Learning Datawarehouse. And the APC was funded by this project.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • higher education
  • multi-dimensional analysis
  • quality assurance
  • teaching and learning analytics


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