This study introduces DK-PRACTICE (Dynamic Knowledge Prediction and Educational Content Recommendation System), an intelligent online platform that leverages machine learning to provide personalized learning recommendations based on student knowledge state. Students participate in a short, adaptive assessment using the question-and-answer method regarding key concepts in a specific knowledge domain. The system dynamically selects the next question for each student based on the correctness and accuracy of their previous answers. After the test is completed, DK-PRACTICE analyzes students' interaction history to recommend learning materials to empower the student's knowledge state in identified knowledge gaps. Both question selection and learning material recommendations are based on machine learning models trained using anonymized data from a real learning environment. To promote self-assessment and monitor learning progress, DK-PRACTICE allows students to take two tests: one pre-teaching and one post-teaching. After each test, a report is generated with detailed results. In addition, the platform offers functions to visualize learning progress based on recorded test statistics. DK-PRACTICE promotes adaptive and personalized learning by empowering students with self-assessment capabilities and providing instructors with valuable information about students' knowledge levels.

Authors:
Marina Delianidi, International Hellenic University, Greece
Konstantinos Diamantaras, International Hellenic University, Greece
Ioannis Moras, International Hellenic University, Greece
Antonis Sidiropoulos, International Hellenic University, Greece

About the Presenter(s)
Ms Marina Delianidi is a PhD candidate in Educational Data Mining at the Department of Information and Electronic Engineering, Thessaloniki, Greece.

Connect on Linkedin
https://www.linkedin.com/in/marina-delianidi-a2901bab/

See this presentation on the full schedule – Wednesday Schedule

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