The Web offers an unprecedented number of resources and has become the most popular source of information for students shaping their understanding of a new topic, and for instructors selecting relevant material for learning and teaching activities. Even though search engines are the most widely used tools for searching for educational content, the realities of the learning and teaching processes make the retrieval and evaluation of educational resources more complex than they are for other goods or services. The lack of recourse to educational metadata in web pages, as well as the size of the Web itself, call for specific techniques to be adopted for a more effective ranking of educational content. In this study, we propose an innovative approach based on semantic technologies. The SemanticSearch approach described in this paper leverages knowledge graph representation of teaching contexts and proposes a new ranking method for rating educational web content. In the literature we find an Educational Ranking Principle that ranks web pages for a specific teaching context. In this study, we integrate the Educational Ranking Principle with semantic data to extend the experimentation and analyse performance further. We undertake an evaluation involving university teachers, considering more than 70 queries to measure the SemanticSearch performance against the Educational Ranking Principle in addition to two state-of-the-art methodologies: Tf-Idf and BM25F. Paired t-tests of four accuracy measures provide statistical evidence for improvements made by using SemanticSearch method when compared to the three baselines.

Limongelli, C., Lombardi, M., Marani, A., Taibi, D. (2022). A Semantic Approach to Ranking Techniques: Improving Web Page Searches for Educational Purposes. IEEE ACCESS, 10, 68885-68896 [10.1109/ACCESS.2022.3186356].

A Semantic Approach to Ranking Techniques: Improving Web Page Searches for Educational Purposes

Limongelli, C;Lombardi, M;Marani, A;Taibi, D
2022

Abstract

The Web offers an unprecedented number of resources and has become the most popular source of information for students shaping their understanding of a new topic, and for instructors selecting relevant material for learning and teaching activities. Even though search engines are the most widely used tools for searching for educational content, the realities of the learning and teaching processes make the retrieval and evaluation of educational resources more complex than they are for other goods or services. The lack of recourse to educational metadata in web pages, as well as the size of the Web itself, call for specific techniques to be adopted for a more effective ranking of educational content. In this study, we propose an innovative approach based on semantic technologies. The SemanticSearch approach described in this paper leverages knowledge graph representation of teaching contexts and proposes a new ranking method for rating educational web content. In the literature we find an Educational Ranking Principle that ranks web pages for a specific teaching context. In this study, we integrate the Educational Ranking Principle with semantic data to extend the experimentation and analyse performance further. We undertake an evaluation involving university teachers, considering more than 70 queries to measure the SemanticSearch performance against the Educational Ranking Principle in addition to two state-of-the-art methodologies: Tf-Idf and BM25F. Paired t-tests of four accuracy measures provide statistical evidence for improvements made by using SemanticSearch method when compared to the three baselines.
Limongelli, C., Lombardi, M., Marani, A., Taibi, D. (2022). A Semantic Approach to Ranking Techniques: Improving Web Page Searches for Educational Purposes. IEEE ACCESS, 10, 68885-68896 [10.1109/ACCESS.2022.3186356].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/420348
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