Athitaya Nitchot

This study examines how Generative AI and knowledge-mapping tools support student learning and engagement in programming education. A quasi-experimental design was conducted with 30 undergraduate students enrolled in an object-oriented programming course, where participants used both tools across a four-week intervention. Data were collected through task performance and learner perception surveys. The results indicate that students reported higher ease of use and immediate support when using Generative AI, while knowledge mapping was associated with stronger support for conceptual understanding and reflective learning in later stages. These findings suggest that the two approaches support different aspects of learning, with Generative AI facilitating rapid clarification and knowledge-mapping tools encouraging structured conceptual engagement. The study contributes to the e-learning field by providing empirical insight into how different forms of learning support function within the same instructional context. Rather than positioning the tools as direct alternatives, the findings highlight their complementary pedagogical roles and offer guidance for integrating adaptive AI support with structured learning approaches in programming education.

Purpose: Our study aims to focus on the application of knowledge mapping to provide pedagogically-structured learners' competences. Design/methodology/approach: We conducted an experiment examined the associations between the pedagogical quality of students’ pedagogically-informed knowledge (PIK) maps, class assignment scores and perceptions of PIK mapping’s uses. Findings: The results showed that higher assignment scores were significantly predicted by higher quality PIK maps, ratings for PIK mapping were significantly higher than other mappings, and the learners’ experience of PIK mapping led to a significant change of attitude towards mapping as a learning activity and to a positive opinion of the value of PIK mapping in particular. Interestingly, there was no significant relation between learners’ opinion ratings of the uses of PIK mapping in learning and their assignment scores. Originality/value: Questions remain on the generalizability of the findings, and on the features of a PIK map which are particularly useful to a learner. This study investigated the value of PIK mapping in the context of a practical class on the building of simple DIY (do-it-yourself) holographic projectors; it may be thought that the applied nature of the topic was more suited to the PIK mapping of learner competences and intended learning outcomes than a more theoretic classroom topic on holography. A future study is planned to address this issue.

The research is interesting and matches the scope of the journal. However, I was looking for experimental studies to prove and answer the research questions. Without such studies, the authors cannot conclude or draw any conclusions from this.

The author has reviewed the areas of research very well. Both the learning outcomes and LMS sides are clearly discussed. However, the study should conduct the experiment to see its effectiveness in the real classroom. Also, the topic is too broad, an...

The approach and ideas are really great. Good examples are provided, but they are still limited. At some points, I feel this article is more of a blog than an academic journal article. Anyway, I enjoy reading it. I would like to see more experiments...

* The authors should state the research questions clearly. Please identify each point and how the research solves such problems. * The authors should involve some experimental studies as the proof of concept and make the conclusion from it. * The...

Mobile applications such as Google Maps can provide suggestions for nearby locations. However, some issues with personalized presentation and recommendations and suggested locations are not ordered. This paper proposes context-awareness on place types using linear classifiers. The context-aware ubiquitous support is concerned with recommending nearby locations based on rating and distance. We use the scenario of types of places to process the recommendation. Two experiment studies were conducted, and the results showed that our approach is significantly better than a normal Google places search. Overall, the users were satisfied with our approach.