Towards a K-12 Game-based Educational Platform with Automatic Student Monitoring: “INTELLIFUN”

Erbad, Aiman; Malaeb, Sarah; Ja'am, Jihad · 2016 · Crossref

DOI: 10.5339/qfarc.2016.ictpp2519

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Summary

This paper addresses the challenges facing K-12 education, specifically low student motivation, weak problem-solving abilities, and limited working memory in children under 11. The authors highlight Qatar’s poor performance in international assessments (PISA) despite high educational expenditures, attributing part of the issue to insufficient family engagement and the lack of granular, skills-based assessment tools. Traditional grading systems provide global topic-based grades rather than detailed feedback on specific learning skills, hindering parents’ ability to support their children and teachers’ ability to personalize tutoring. To address these gaps, the authors propose “INTELLIFUN,” a game-based educational platform designed to automatically monitor and assess elementary students’ progress while enhancing learning motivation through interactive gaming. The development of INTELLIFUN was informed by a survey of 31 leading digital learning technologies, which revealed that most existing solutions lack automated progress reporting and require manual data entry. The platform utilizes an ontology-based approach to map curriculum standards and learning objectives to game content. Specifically, the authors designed a new ontology model that links programs from the Supreme Education Council of Qatar with flow-driven game elements. Student performance is evaluated through this ontology using an automated reasoning mechanism guided by inference rules, which extracts information about correct, incorrect, and incomplete actions. The system is implemented in a 3-tier architecture where mobile game applications query and update the ontology in real time via a web service using the Apache Jena Ontology API. This technical design allows for the fusion of digital education, semantic web technologies, and artificial intelligence to dynamically generate game content based on student preferences and acquired knowledge. The paper outlines the platform’s capabilities to provide detailed, skills-based monitoring that empowers both teachers and parents. Teachers can observe student progress in correspondence with specific learning objectives to focus on weaknesses, while parents receive timely, detailed feedback on their children’s strengths and areas for improvement. The platform aims to improve long-term retention of information and exam scores by integrating entertainment features with serious educational outcomes. The significance of this work lies in its novel integration of automated monitoring with game-based learning, addressing the complexity of designing educational data models and providing advanced reasoning over student data. The authors plan to test the platform using the Grade 1 mathematics curriculum as a case study, involving students, parents, and teachers in a user study to evaluate efficacy. Future research directions include employing data mining techniques within the reasoning engine to handle complex performance indicators and further refining the dynamic generation of game worlds based on individual learning skills.

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