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Explainable Artificial Intelligence for Human-Centric Data Analysis in Virtual Learning Environments

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Higher Education Learning Methodologies and Technologies Online (HELMeTO 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1091))

Abstract

The amount of data to analyze in virtual learning environments (VLEs) grows exponentially everyday. The daily interaction of students with VLE platforms represents a digital foot print of the students’ engagement with the learning materials and activities. This big and worth source of information needs to be managed and processed to be useful. Educational Data Mining and Learning Analytics are two research branches that have been recently emerged to analyze educational data. Artificial Intelligence techniques are commonly used to extract hidden knowledge from data and to construct models that could be used, for example, to predict students’ outcomes. However, in the educational field, where the interaction between humans and AI systems is a main concern, there is a need of developing new Explainable AI (XAI) systems, that are able to communicate, in a human understandable way, the data analysis results. In this paper, we use an XAI tool, called ExpliClas, with the aim of facilitating data analysis in the context of the decision-making processes to be carried out by all the stakeholders involved in the educational process. The Open University Learning Analytics Dataset (OULAD) has been used to predict students’ outcome, and both graphical and textual explanations of the predictions have shown the need and the effectiveness of using XAI in the educational field.

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Notes

  1. 1.

    European Commission, Artificial Intelligence for Europe, Brussels, Belgium, “Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions”, Tech. Rep., 2018, (SWD(2018) 137 final) https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe.

  2. 2.

    ExpliClas API: https://demos.citius.usc.es/ExpliClasAPI/.

  3. 3.

    ExpliClas Web Client: https://demos.citius.usc.es/ExpliClas/.

  4. 4.

    Open University (OU) website: http://www.open.ac.uk/.

  5. 5.

    OU Open Data: https://analyse.kmi.open.ac.uk/open_dataset#data.

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Acknowledgments

Jose M. Alonso is Ramón y Cajal Researcher (RYC-2016-19802). This research was also funded by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-B-I00, TIN2017-84796-C2-1-R and TIN2017-90773-REDT) and the Galician Ministry of Education, University and Professional Training (grants ED431F 2018/02, ED431C 2018/29 and “accreditation 2016–2019, ED431G/08”) which is co-funded by the European Regional Development Fund (ERDF/FEDER program).

Gabriella Casalino is member of the INdAM Research group GNCS.

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Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    José M. Alonso

  2. Department of Computer Science, University of Bari Aldo Moro, Bari, Italy

    Gabriella Casalino

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  1. José M. Alonso
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  2. Gabriella Casalino
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Corresponding author

Correspondence to Gabriella Casalino .

Editor information

Editors and Affiliations

  1. Universidad Internacional de La Rioja, Logroño, La Rioja, Spain

    Daniel Burgos

  2. Unitelma Sapienza University, Rome, Italy

    Marta Cimitile

  3. Department of Information Engineering, University of Pisa, Pisa, Italy

    Pietro Ducange

  4. Università degli Studi eCampus, Novedrate, Como, Italy

    Riccardo Pecori

  5. SMARTEST Research Centre, Università degli Studi eCampus, Novedrate, Como, Italy

    Pietro Picerno

  6. Università degli Studi eCampus, Novedrate, Italy

    Paolo Raviolo

  7. Open University in the Netherlands, Heerlen, The Netherlands

    Christian M. Stracke

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Alonso, J.M., Casalino, G. (2019). Explainable Artificial Intelligence for Human-Centric Data Analysis in Virtual Learning Environments. In: Burgos, D., et al. Higher Education Learning Methodologies and Technologies Online. HELMeTO 2019. Communications in Computer and Information Science, vol 1091. Springer, Cham. https://doi.org/10.1007/978-3-030-31284-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-31284-8_10

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