Dynamics Personalized Learning Path Based on Triple Criteria using Deep Learning and Rule-Based Method

Imamah, Umi Laili Yuhana, Arif Djunaidy, Mauridhi Hery Purnomo

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


Personalized learning paths are designed to optimize learning time and improve student learning performance by providing an appropriate learning sequence based on the unique characteristics of each student. A common method for constructing personalized learning paths is based on the student's knowledge but disregards the student's interest in the subject matter. This research employs a deep learning and rule-based approach to recommend suitable material based on the topic's difficulty, student interest, and knowledge level. The difficulty level of the topic is predicted using deep learning. A questionnaire is used to determine the level of student interest, which is then processed using a rule-based approach to generate a learning path. Modeling a dynamic learning path requires measuring student knowledge in each topic and updating the learning path accordingly. Comparing the learning outcomes of students who utilized conventional e-learning versus those who followed a personalized learning path constitutes the evaluation. The results demonstrated that students scored 29% higher, or 15.06 points, than those who utilized conventional e-learning.

Original languageEnglish
Title of host publicationTENCON 2023 - 2023 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350302196
Publication statusPublished - 2023
Event38th IEEE Region 10 Conference, TENCON 2023 - Chiang Mai, Thailand
Duration: 31 Oct 20233 Nov 2023

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference38th IEEE Region 10 Conference, TENCON 2023
CityChiang Mai


  • Deep Learning
  • Personalized Learning Path
  • Rule-Based
  • difficulty level
  • student interest


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