AHP4Edu: An AHP-Based Assessment Model for Learning Effectiveness of Education

Yu Lun Huang*, Yu Hsin Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The modern e-learning environment generates new types of students’ learning records, including their operation records in the learning management system. Recently, wearable devices and a variety of sensors have become common in our daily life. By using these devices, we can access students’ information such as heart rates and facial features. Previous studies [1, 2] have used the bioinformatics data mentioned above to analyze students’ learning effectiveness. However, these approaches only utilize partial information and the diversity of data has not been put into consideration. This paper tries to better address this inefficiency by proposing an Analytic Hierarchy Process (AHP)-based model integrated with professional expertise in education. With this model, lecturers can customize the selection and importance of the criteria according to the used teaching strategy. Then, AHP4Edu can analyzes students’ learning effectiveness scores from the sub-scores of the sub-criteria specified by an expert or a lecturer. We present simulations on assessing students’ learning effectiveness for distance learning. We also demonstrate how AHP4Edu integrates heterogeneous data and provides a reliable learning effectiveness assessment for the lecturer.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - 4th International Conference, ICITL 2021, Proceedings
EditorsYueh-Min Huang, Chin-Feng Lai, Tânia Rocha
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030915391
StatePublished - 2021
Event4th International Conference on Innovative Technologies and Learning, ICITL 2021 - Virtual, Online
Duration: 29 Nov 20211 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13117 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Innovative Technologies and Learning, ICITL 2021
CityVirtual, Online


  • Analytic hierarchy process
  • Learning effectiveness
  • Multi-criteria decision-making approach


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