A Proposed Framework for Learning Assessment Ontology Generator

Martinus Maslim, Hei Chia Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Different institutions have shown interest in standardizing the learning result. It may be used in the same way to assess students’ learning status. The teacher must quantify the learning outcomes for evaluation purposes. It often requires a great deal of time and effort to do paper tasks. Additionally, this activity prevents instructors from concentrating on the learning process. Teachers are continuously burdened with administrative responsibilities that should be alleviated using technology that adheres to the current framework. The Bloom Taxonomy, a widely used framework for defining learning outcomes, allows for the assessment of learning outcomes at several levels. The purpose of this research is to provide a framework that will assist the instructor in completing the evaluation more quickly and accurately. This study provided an algorithm for adapting ontology and text classification technologies to detect correlations between words and keywords to aid in evaluation. It is anticipated that the categorization findings will assist in shortening the time required to complete the evaluation.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - 5th International Conference, ICITL 2022, Proceedings
EditorsYueh-Min Huang, Shu-Chen Cheng, João Barroso, Frode Eika Sandnes
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783031152726
Publication statusPublished - 2022
Event5th International Conference on Innovative Technologies and Learning, ICITL 2022 - Virtual, Online
Duration: 2022 Aug 292022 Aug 31

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Innovative Technologies and Learning, ICITL 2022
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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