Learning Analytics aims at supporting the understanding of learning mechanisms and their effects by means of data-driven strategies. LA approaches commonly face two big challenges: first, due to privacy reasons, most of the analyzed data are not in the public domain. Secondly, the open data collections, which come from diverse learning contexts, are quite heterogeneous. Therefore, the research findings are not easily reproducible and the publicly available datasets are often too small to enable further data analytics. To overcome these issues, there is an increasing need for integrating open learning data into unified models. This paper proposes UNIFORM, an open relational database integrating various learning data sources. It presents also a machine learning supported approach to automatically extending the integrated dataset as soon as new data sources become available. The proposed approach exploits a classifier to predict attribute alignments based on the correlations among the corresponding textual attribute descriptions. The integration phase has reached a promising quality level on most of the analyzed benchmark datasets. Furthermore, the usability of the UNIFORM data model has been demonstrated in a real case study, where the integrated data have been exploited to support learners’ outcome prediction. The F1-score achieved on the integrated data is approximately 30% higher than those obtained on the original data.

UNIFORM: Automatic Alignment of Open Learning Datasets / Cagliero, Luca; Canale, Lorenzo; Farinetti, Laura. - STAMPA. - (2020), pp. 95-102. ((Intervento presentato al convegno 44th Annual Computers, Software, and Applications Conference (COMPSAC) nel 13-17 July 2020 [10.1109/COMPSAC48688.2020.00022].

UNIFORM: Automatic Alignment of Open Learning Datasets

Luca Cagliero;Lorenzo Canale;Laura Farinetti
2020

Abstract

Learning Analytics aims at supporting the understanding of learning mechanisms and their effects by means of data-driven strategies. LA approaches commonly face two big challenges: first, due to privacy reasons, most of the analyzed data are not in the public domain. Secondly, the open data collections, which come from diverse learning contexts, are quite heterogeneous. Therefore, the research findings are not easily reproducible and the publicly available datasets are often too small to enable further data analytics. To overcome these issues, there is an increasing need for integrating open learning data into unified models. This paper proposes UNIFORM, an open relational database integrating various learning data sources. It presents also a machine learning supported approach to automatically extending the integrated dataset as soon as new data sources become available. The proposed approach exploits a classifier to predict attribute alignments based on the correlations among the corresponding textual attribute descriptions. The integration phase has reached a promising quality level on most of the analyzed benchmark datasets. Furthermore, the usability of the UNIFORM data model has been demonstrated in a real case study, where the integrated data have been exploited to support learners’ outcome prediction. The F1-score achieved on the integrated data is approximately 30% higher than those obtained on the original data.
978-1-7281-7303-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2846442