Extracting Learning Performance Indicators from Digital Learning Environments

Prem Sundaramoorthy, Cornelis Vertegaal, Cecilia Martinez, Ramiro Serra, Chris Verhoeven, Anton Montagne, Mark J Bentum

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
32 Downloads (Pure)


In the last decades, there has been a steady adoption of digital online platforms as learning environments applied to all levels of education. This increasing adoption forces a transition in educational resources which has further been accelerated by the recent pandemic, leading to an almost complete online-only learning environment in some cases. The aim of this paper is to outline the methodology involved in setting up a framework for mapping course-specific data based on student activity to standard learning indicators, which will serve as an input to performance prediction algorithms. The process involves systematically surveying, capturing, and categorising the vast range of data available in digital learning platforms. The data are collected from two sample courses and distilled into five dimensions represented by the generic learning indicators: prior knowledge, preparation, participation, interaction, and performance. The data is weighted based on course development and teaching member’s perspectives to account for course-wise variations. The framework established will allow portability of prediction algorithms between courses and provide a means for meaningful and directed learner formative feedback. Two courses, both bachelor-level and worth 5 European Credits (ECs), that use several online learning platforms in their teaching tools have been chosen in this study to explore the nature and range of student interaction data available, accessible, and usable in a course. The first course is Electromagnetics II at Eindhoven University of Technology, and the second course is Electronics at Delft University of Technology. Both Universities are located in the Netherlands. This work is in the scope of a broader study to use such learning indicators with predictive algorithms to provide a prognosis on individual student performance. The findings in this paper will enable the realization of student performance prediction at a very early stage in the course.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE Global Engineering Education Conference (EDUCON)
Subtitle of host publicationDigital Transformation for Sustainable Engineering Education
EditorsMohammed Jemni, Ilhem Kalle, Abdeljalil Akkari
Number of pages7
ISBN (Electronic)978-1-6654-4434-7
ISBN (Print)978-1-6654-4435-4
Publication statusPublished - 2022
Event2022 IEEE Global Engineering Education Conference
- Tunis, Tunisia
Duration: 28 Mar 202231 Mar 2022


Conference2022 IEEE Global Engineering Education Conference
Abbreviated titleEDUCON 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Learning indicators
  • learning analytics
  • data portability
  • student progress monitoring
  • student prognosis
  • early warning


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