OpenLAP
Open Learning Analytics Platform (OpenLAP)
Open Learning Analytics (OLA) is an emerging field, that deals with learning data collected from various environments and contexts, analyzed with a wide range of analytics methods to address the requirements of different stakeholders. OLA introduces a set of requirements and implications for LA practitioners, developers, and researchers. These include data aggregation and integration, interoperability, reusability, modularity, flexibility, extensibility, performance, scalability, usability, privacy, and personalization. The Open Learning Analytics Platform (OpenLAP) is a framework that addresses these issues and lays the foundation for an ecosystem of OLA.
OpenLAP provides a detailed technical OLA architecture with a concrete implementation of all its components, seamlessly integrated into a platform. It encompasses different stakeholders associated through a common interest in learning analytics but with diverse needs and objectives, a wide range of data coming from various learning environments and contexts, as well as multiple infrastructures and methods that enable to draw value from data in order to gain insight into learning processes.
OpenLAP follows a Human-Centered Learning Analytics (HCLA) approach and aims at engaging end-users in flexible definition and dynamic generation of personalized indicators. The generated indicators are executed by querying data, applying filters, performing analysis, and generating visualizations to be rendered on the client-side. To meet the requirements of diverse users, OpenLAP provides a modular and extensible architecture that allows the easy integration of new analytics modules, analytics methods, and visualization techniques.
Project Features
Abstract Architecture
The three main components of OpenLAP are Indicator Engine, Analytics Framework, and Data Collection and Management. The Indicator Engine is responsible for providing an intuitive and interactive User Interface (UI) to help users develop their indicators. The Analytics Framework has various core modules that allow the generation, execution, and management of indicators. The Data Collection and Management component is responsible for xAPI-based data collection from various learning sources as well as maintaining data privacy policies.
Indicator Specification Cards
Indicator Specification Cards (ISC) represent a theory-informed method that helps different LA stakeholders systematically co-design LA indicators. It follows the Goal-Question-Indicator (GQI) approach to design LA indicators that meet users’ goals and applies information visualization guidelines from Munzner’s What-Why-How visualization framework. Concretely, it describes a systematic workflow to get from the Why? (i.e., user goal/question) to the How? (i.e., visualization). The ISC Creator module in OpenLAP provides an intuitive user interface (UI) that allows the low cost design of low-fidellity LA indicators using ISCs.
xAPI-CSV Converter
The xAPI-CSV Converter module in OpenLAP addresses the data interoperability and flexibility challenges in OLA. It provides an intutive UI that supports LA stakeholders in transforming xAPI-based to CSV-based data and vice versa with minimum effort. For example, LA researchers can transform learning activity data available as xAPI to CSV format and use it as input to the ISC Creator in order to co-design low-fidelity LA indicators with learners and teachers. And, LA designers can transform learning activity data available as CSV to xAPI format and use it as input to the OpenLAP Indicator Editor to develop high-fidelity LA indicators.
Indicator Editor
The Indicator Editor is one of the main components of the Indicator Engine. It is responsible for providing users with an intuitive and interactive UI that guides them throughout the entire indicator development process, following a Goal-Question-Indicator (GQI) approach. The Indicator Editor supports three different types of indicators, namely Basic, Composite, and Multi-level Analysis.
Team Members
Videos
xAPI-CSV Converter Video
Watch the video on YouTube UDE SoCo Channel
Posters
LAK24
The Social Computing Group was present at the prestigious International Learning Analytics and Knowledge Conference (LAK24) in Kyoto, Japan, from March 18-22, 2024. LAK is the premier conference in the field of learning analytics and educational data science. Shoeb Joarder presented our poster paper "A No-Code Environment for Implementing Human-Centered Learning Analytics Indicators", available in LAK24 Companion Proceedings here.
Learning AID
The Learning AID 2023 was held at the Ruhr-Universität Bochum on August 28 & 29, 2023. The state-funded project KI:edu.nrw - Didactics, Ethics and Technology of Learning Analytics and AI in Higher Education initiated and organized the conference and workshop day. The event focused on the practical use of learning analytics, artificial intelligence, and data mining in higher education.
Publications
- Shoeb Joarder, Mohamed Amine Chatti, and Ao Sun
A No-Code Environment for Implementing Human-Centered Learning Analytics Indicators Inproceedings
In Companion Proceedings of the 14th International Learning Analytics and Knowledge Conference (LAK’24)
- Shoeb Joarder, Mohamed Amine Chatti, Seyedemarzie Mirhashemi, and Qurat Ul Ain
Towards a Flexible User Interface for 'Quick and Dirty' Learning Analytics Indicator Design Inproceedings
In Proceedings of the Fourth International Workshop on Human-Centered Learning Analytics (HCLA) at the LAK'23 conference.
- Mohamed Amine Chatti, Volkan Yücepur, Arham Muslim, Mouadh Guesmi, Shoeb Joarder
Designing Theory-Driven Analytics-Enhanced Self-Regulated Learning Applications Book Chapter
In: Sahin M., Ifenthaler D. (eds) Visualizations and Dashboards for Learning Analytics. Advances in Analytics for Learning and Teaching (pp. 47-68). Springer, Cham. https://doi.org/10.1007/978-3-030-81222-5_3
- Mohamed Amine Chatti, Arham Muslim, Mouadh Guesmi, Florian Richtscheid, Dawood Nasimi, Amin Shahin, and Ritesh Damera
How to Design Effective Learning Analytics Indicators? A Human-Centered Design Approach Inproceedings
In Proceedings of the Fifteenth European Conference on Technology Enhanced Learning (ECTEL'20), pp. 303-317, 2020.
- Mohamed Amine Chatti, Arham Muslim, Manpriya Guliani, Mouadh Guesmi
The LAVA Model: Learning Analytics Meets Visual Analytics Book Chapter
In D. Ifenthaler & D. Gibson (Eds.), Adoption of Data Analytics in Higher Education Learning and Teaching (pp. 71-93). Cham: Springer.
- Arham Muslim, Mohamed Amine Chatti, Mouadh Guesmi
Open Learning Analytics: A Systematic Literature Review and Future Perspectives Book Chapter
In N. Pinkwart & S. Liu (Eds.), Artificial Intelligence Supported Educational Technologies (pp. 3-29). Cham: Springer.