Teaching Mental Models in Introductory Programming

2025-05-26T07:12:16+00:00

In this article, an evaluation of syllabi, books, teaching materials, and examinations concerning introductory programming revealed that the subject disproportionately focuses on teaching and learning language features instead of mental models. It is demonstrated that the shift from low-level to high-level languages resulted in the "language-trap" that leads to an emphasis on language features at the expense of mental models. To mitigate the language-trap effect, a novel instructional approach called MTL three-tier that combines low-level syntax, Memory Transfer Language, and high-level syntax is proposed. Results from two experiments show that using assembly codes in combination with the MTL-three-tier approach at the beginning of the course assists instructors in avoiding the language-trap. For novices, the cognitive load is reduced, consequently, increasing the ability to form viable mental models. Results from the first experiment show that novices in the experimental group were 2.17 times more likely to form viable mental models than those in the control sample. From the second experiment, results show that novices from the experimental group were 14.50 times more likely to avoid common errors in introductory programming than were the novices in the control group.

Students’ Behavioral Intention towards Adoption of Peer Recommender Systems in Tanzania

2025-06-12T08:59:49+00:00

This work investigates factors influencing students’ behavioral intention to use peer recommender systems for collaborative learning in Tanzanian secondary schools. Peer-assisted learning (PAL) is a longstanding educational approach, and recommender systems (RS) offer a technological means to enhance PAL by matching students with suitable peers beyond their immediate classrooms. However, the successful adoption of such systems depends on user acceptance, especially in developing contexts where technological and cultural factors play a significant role. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT), we developed a research model with four core determinants: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. These factors were hypothesized to predict students’ intention to adopt a peer recommender system. A survey of 1,029 secondary students from 8 schools in Tanzania was conducted. Results indicate that all four factors significantly affect behavioral intention. Performance expectancy, social influence, and facilitating conditions showed positive effects, while effort expectancy demonstrated a significant negative effect. The UTAUT model explained approximately 74% of the variance in students’ behavioral intention, demonstrating its strong explanatory power in this context. Key recommendations include investing in necessary ICT infrastructure, ensuring the system is easy to use, leveraging social support from teachers and peers, and clearly communicating learning benefits to students. With the proper supportive conditions and user-centric design, peer recommender systems can be a viable tool to foster online peer learning among secondary school students in Tanzania.

Journal of ICT Systems

Teaching Mental Models in Introductory Programming

Students’ Behavioral Intention towards Adoption of Peer Recommender Systems in Tanzania