Reproducibility of Published Educational Recommendation Systems: Systematic Review
Date Issued
2022-09
Author(s)
Peshovski, Ivica
Madevska Bogdanova, Ana
Abstract
The purpose of this paper is to conduct a systematic review
of the available literature on explainable recommendation systems in education and their reproducibility, particularly when recommendations
are integrated as part of learning management systems. The first part
of the paper’s methodology employs an NLP-powered toolkit that automates a large portion of the review process by automatically analyzing
articles indexed in the IEEE Xplore, PubMed, Springer, Elsevier, and
MDPI digital libraries. A quantitative review of all available literature
is carried, followed by a qualitative review of the few selected articles
that do indeed focus on the explainability approach when implementing
recommendation systems. The relevant articles are thoroughly analyzed
and compared based on a variety of indicators such as the purpose of the
recommendations, tools and techniques used, and whether the research
is easy or hard to reproduce. The findings show that, while the amount of
available research is increasing and new learning management systems
are continuously being developed in recent years, the explainability of
the machine learning techniques used in recommendation systems isn’t
a primary focus among researchers and developers, and the scope of the
available literature is quite limited.
of the available literature on explainable recommendation systems in education and their reproducibility, particularly when recommendations
are integrated as part of learning management systems. The first part
of the paper’s methodology employs an NLP-powered toolkit that automates a large portion of the review process by automatically analyzing
articles indexed in the IEEE Xplore, PubMed, Springer, Elsevier, and
MDPI digital libraries. A quantitative review of all available literature
is carried, followed by a qualitative review of the few selected articles
that do indeed focus on the explainability approach when implementing
recommendation systems. The relevant articles are thoroughly analyzed
and compared based on a variety of indicators such as the purpose of the
recommendations, tools and techniques used, and whether the research
is easy or hard to reproduce. The findings show that, while the amount of
available research is increasing and new learning management systems
are continuously being developed in recent years, the explainability of
the machine learning techniques used in recommendation systems isn’t
a primary focus among researchers and developers, and the scope of the
available literature is quite limited.
Subjects
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