Improving Learner-Computer Interaction through Intelligent Learning Material Delivery Using Instructional Design Modeling

• Main Authors: Christos Troussas, Akrivi Krouska, Cleo Sgouropoulou
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Entropy
• Subjects: adaptive learning material delivery; Component Display Theory; content-based filtering; Intelligent Tutoring Systems; Multiple-Criteria Decision Analysis; online learning
• Online Access: https://www.mdpi.com/1099-4300/23/6/668

This paper describes an innovative and sophisticated approach for improving learner-computer interaction in the tutoring of Java programming through the delivery of adequate learning material to learners. To achieve this, an instructional theory and intelligent techniques are combined, namely the Component Display Theory along with content-based filtering and multiple-criteria decision analysis, with the intention of providing personalized learning material and thus, improving student interaction. Until now, the majority of the research efforts mainly focus on adapting the presentation of learning material based on students’ characteristics. As such, there is free space for researching issues like delivering the appropriate type of learning material, in order to maintain the pedagogical affordance of the educational software. The blending of instructional design theories and sophisticated techniques can offer a more personalized and adaptive learning experience to learners of computer programming. The paper presents a fully operating intelligent educational software. It merges pedagogical and technological approaches for sophisticated learning material delivery to students. Moreover, it was used by undergraduate university students to learn Java programming for a semester during the COVID-19 lockdown. The findings of the evaluation showed that the presented way for delivering the Java learning material surpassed other approaches incorporating merely instructional models or intelligent tools, in terms of satisfaction and knowledge acquisition.