Work

Empowering Student Learning in Intelligent Tutoring Systems: Building a Comprehensive Learner Model With Feedback From Students, Parents, and Instructors

Year: 2025

Type: article

Abstract: This paper introduces a novel approach for developing a learner model for school students by incorporating assessments from multiple sources, including students, parents, and teachers, in addition to exams. Traditional Intelligent Tutoring Systems (ITS) typically rely on a single source, such as exams, which often results in an incomplete picture of a student’s learning needs. To address this limitation, we developed a multi-source ITS that integrates feedback from various stakeholders to enhance the accuracy and personalization of the learner model. In a study involving 75 high school students, participants were divided into three groups: one receiving traditional classroom instruction, one using a conventional ITS, and one using our multi-source ITS. Results showed that the group using the multi-source system demonstrated the greatest improvement, with an average 38% increase in post-test scores, compared to 19% for the traditional instruction group and 26% for the conventional ITS group. Moreover, students using the multi-source ITS reported higher engagement and motivation, driven by their ability to interact with and adjust their learner model. These findings highlight the significant benefits of incorporating multi-source feedback into ITS to boost student performance, engagement, and motivation.

Source: IEEE Access

DOI: 10.1109/access.2025.3579575

Authors: Abdallatif Abu-Issa, Ali Aljadaa, Abualsoud A. Hanani, Iyad Tumar, Mohammed S. Hussein

Institution: Birzeit University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,075