Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 1

NLP-based personal learning assistant for school education

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 5 (2021)

DOI
10.11591/ijece.v11i5.pp4522-4530
ISSN
2722-2578, 2088-8708
Published
2021-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.

Collaboration network of the authors

5 authors · 5 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Christ University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Chaudhary Jashubhai RameshbhaiChrist University1
I PratheekChrist University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>NLP-based personal learning assistant for school education</dc:title><dc:creator>Mathew, Ann Neethu</dc:creator><dc:creator>V., Rohini</dc:creator><dc:creator>Paulose, Joy</dc:creator><dc:description>&lt;jats:p&gt;Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v11i5.pp4522-4530</dc:identifier><dc:identifier>10.11591/ijece.v11i5.pp4522-4530</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 5 (2021); 4522</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/25404/15138</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>