The emergence of Technology Enhanced Learning environments has led to the continual growth of the availability of digital educational resources. In this paper, the potential of enabling their reuse into student-centric services – such as recommender systems or adaptive tutoring tools – is discussed through the proposal and comparison of procedures for automatically detecting the mutual relatedness among learning objects. Since the choice of the similarity measure is fundamental for clustering digital materials, this paper addresses the investigation on two distinct approaches: the content-based semantic similarity, compared to the closeness measure on natural language descriptions of metadata – namely prerequisites and educational objectives. The analysis is conducted on a collection of mathematical problems, equipped with metadata which facilitate their retrieval in Virtual Learning Environments, created by Secondary School teachers with the support of University experts. Natural Language Processing techniques are exploited for extracting relevant information from the metadata, while the developments in the emergent field of Mathematical Language Processing are proposed for the treatment of mathematical expressions included in the resources. The distinct similarity measures presented are examined considering the compared results, and their correlation is evaluated. This study is intended to be the first step towards the definition of a model for structuring shared materials available in disciplinary repositories of virtual communities. This model will be used for implementing a system for the delivery of learning objects trajectories on a digital map automatically generated. The system’s efficacy will be tested through its integration to a Learning Management System hosting secondary school classrooms’ courses. The research is part of a PhD in Pure and Applied Mathematics in apprenticeship, conducted in partnership with leading providers of software based on Computer Algebra System engine.

Alignment of Content, Prerequisites and Educational Objectives: Towards Automated Mapping of Digital Learning Resources

Michele FIORAVERA;Marina MARCHISIO;Luigi DI CARO;Sergio RABELLINO
2018-01-01

Abstract

The emergence of Technology Enhanced Learning environments has led to the continual growth of the availability of digital educational resources. In this paper, the potential of enabling their reuse into student-centric services – such as recommender systems or adaptive tutoring tools – is discussed through the proposal and comparison of procedures for automatically detecting the mutual relatedness among learning objects. Since the choice of the similarity measure is fundamental for clustering digital materials, this paper addresses the investigation on two distinct approaches: the content-based semantic similarity, compared to the closeness measure on natural language descriptions of metadata – namely prerequisites and educational objectives. The analysis is conducted on a collection of mathematical problems, equipped with metadata which facilitate their retrieval in Virtual Learning Environments, created by Secondary School teachers with the support of University experts. Natural Language Processing techniques are exploited for extracting relevant information from the metadata, while the developments in the emergent field of Mathematical Language Processing are proposed for the treatment of mathematical expressions included in the resources. The distinct similarity measures presented are examined considering the compared results, and their correlation is evaluated. This study is intended to be the first step towards the definition of a model for structuring shared materials available in disciplinary repositories of virtual communities. This model will be used for implementing a system for the delivery of learning objects trajectories on a digital map automatically generated. The system’s efficacy will be tested through its integration to a Learning Management System hosting secondary school classrooms’ courses. The research is part of a PhD in Pure and Applied Mathematics in apprenticeship, conducted in partnership with leading providers of software based on Computer Algebra System engine.
2018
14th International Scientific Conference "eLearning and Software for Education"
Bucarest
19-20 aprile 2018
Proceedings of 14th International Scientific Conference "eLearning and Software for Education"
Advanced Distributed Learning Association
193
194
Semantic similarity, Natural Language Processing, mathematical problems
Michele FIORAVERA, Marina MARCHISIO,Luigi DI CARO, Sergio RABELLINO
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1667814
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