A Modular and Extensible Framework for Open Learning Analytics

Authors

  • Arham Muslim RWTH Aachen
  • Mohamed Amine Chatti University of Duisburg Essen
  • Muhammad Bassim Bashir RWTH Aachen
  • Oscal Eduardo Barrios Varela RWTH Aachen
  • Ulrik Schroeder RWTH Aachen

DOI:

https://doi.org/10.18608/jla.2018.51.7

Keywords:

Personalised learning analytics, OpenLAP, analytics framework, modularity, extensibility.

Abstract

Open Learning Analytics (OLA) is an emerging concept in the field of Learning Analytics (LA). It deals with learning data collected from multiple environments and contexts, analyzed with a wide range of analytics methods to address the requirements of different stakeholders. Due to this diversity in different dimensions of OLA, the LA developers and researchers face numerous challenges while designing solutions for OLA. The Open Learning Analytics Platform (OpenLAP) is a framework that addresses these issues and lays the foundation for an ecosystem of OLA that aims at supporting learning and teaching in fragmented, diverse, and networked learning environments. It follows a user-centric approach to engage end users in flexible definition and dynamic generation of personalized indicators. In this paper, we address a subset of OLA challenges and present the conceptual and implementation details of the analytics framework component of OpenLAP, which follows a flexible architecture that allows the easy integration of new analytics methods and visualization techniques in OpenLAP to support end users in defining indicators based on their needs in order to embed the results into their personal learning environment.

References

Chatti, M. A. (2010). Personalization in technology enhanced learning: A social software perspective. Dissertation RWTH Aachen, Shaker Verlag.

Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In Big Data and Learning Analytics in Higher Education (pp. 195-219). Springer.

Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns: elements of reusable object-oriented software. Pearson Education.

Guazzelli, A., Zeller, M., Lin, W.-C., & Williams, G. (2009). PMML: An open standard for sharing models. The R Journal, 1, 60-65.

Lukarov, V., Chatti, M. A., Thüs, H., Kia, F. S., Muslim, A., Greven, C., & Schroeder, U. (2014). Data Models in Learning Analytics. Proceedings of DeLFI Workshops, (pp. 88-95).

Muslim, A., Chatti, M. A., Mahapatra, T., & Schroeder, U. (2016). A Rule-based Indicator Definition Tool for Personalized Learning Analytics. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 264-273). New York, NY, USA: ACM. doi:10.1145/2883851.2883921

Muslim, A., Chatti, M. A., Mughal, M., & Schroeder, U. (2017). The Goal - Question - Indicator Approach for Personalized Learning Analytics. Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU (pp. 371-378). ScitePress. doi:10.5220/0006319803710378

Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46, 30.

Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S. B., Ferguson, R., . . . Baker, R. S. (2011). Open Learning Analytics: an integrated & modularized platform. Proposal to design, implement and evaluate an open platform to integrate heterogeneous learning analytics techniques.

Thüs, H., Chatti, M. A., Greven, C., & Schroeder, U. (2014). Kontexterfassung,-modellierung und-auswertung in Lernumgebungen. DeLFI 2014-Die 12. e-Learning Fachtagung Informatik (pp. 157-162). Gesellschaft für Informatik.

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Published

2018-04-09

How to Cite

Muslim, A., Chatti, M. A., Bashir, M. B., Barrios Varela, O. E., & Schroeder, U. (2018). A Modular and Extensible Framework for Open Learning Analytics. Journal of Learning Analytics, 5(1), 92–100. https://doi.org/10.18608/jla.2018.51.7

Issue

Vol. 5 No. 1 (2018): It's About Time: Temporal Analyses of Learning Data (Part 2)

Section

Data and Tool Reports

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Copyright (c) 2018 Journal of Learning Analytics

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