Facilitating Group Learning Using an Apprenticeship Model: Which Master is More Effective in Programming Instruction?

Garcia, Manuel B.

doi:10.1177/07356331231170382

Cited by 7 publications

(1 citation statement)

References 57 publications

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“…Hence, it is important to supervise the learning phase meticulously to guarantee that the degree of learning aligns with the learning process. However, the educator must apply and implemented more deliberations in facilitating the learning process of programming to maximise the effectiveness of the acquired knowledge [23].…”

Section: Programming Learner's Experiencementioning

confidence: 99%

A Review of Learner’s Model for Programming in Teaching and Learning

Hafizul Fahri Hanafi,

Abu Zarrin Selamat,

Miharaini Md Ghani

et al. 2023

ARASET

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Over recent years, computer science (CS) teachers and instructor have faced several challenges in assisting students to strengthen their understanding of programming. To make matters worse, existing assessment methods could be more effective in assessing students' programming skills and knowledge, thus entailing a review of issues surrounding the instructions of programming courses. Against this backdrop, the authors accomplished a systematic review of the current literature to identify several socio-cognitive factors that can help develop a learner model for learning programming. Specifically, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique was utilized to identify and select pertinent articles from three primary online databases: Scopus, Web of Science, and Eric. Initially, 401 relevant papers were identified and retrieved, which were further reduced to only 24 articles based on specific selection criteria. As revealed, several demographic factors (such as gender, age, ethnicity, and SES) and socio-cognitive factors (motivation, attitude, and interest) have been shown to wield significant impacts on student learning of programming. The authors' findings of the systematic literature review helped synthesize the learner model's essential elements that must be carefully considered and used. Arguably, the use of such a new learner model can oblige guide instructors to teach programming more efficaciously by crystallizing several students' socio-cognitive backgrounds, which collectively have a significant impact on student learning of programming courses or subjects at the primary, secondary, and tertiary levels of education, especially in the Malaysian educational context.

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Section: Programming Learner's Experiencementioning

confidence: 99%

A Review of Learner’s Model for Programming in Teaching and Learning

Hafizul Fahri Hanafi,

Abu Zarrin Selamat,

Miharaini Md Ghani

et al. 2023

ARASET

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Effect of jigsaw‐integrated task‐driven learning on students' motivation, computational thinking, collaborative skills, and programming performance in a high‐school programming course

Zhan,

Li,

2024

Comp Applic In Engineering

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Computer programming has emerged as an important field in K‐12 science, technology, engineering, and maths (STEM) education in the AI era. However, contemporary programming education is hindered by fragmented course content, high complexity, and difficulties in maintaining engagement, impeding smooth progress. More effective collaborative learning strategies need to be explored. This study constructed jigsaw‐integrated task‐driven learning (jigsaw‐TDL) in a high school Python programming course under a STEM curriculum and verified its teaching effectiveness on students’ learning motivation, computational thinking, collaborative skills, and programming performance both quantitatively and qualitatively. Nighty‐nine high school students were randomly assigned to a jigsaw‐TDL group and a general collaborative task‐driven learning group (collaborative‐TDL). During the experiment, a Python programming course was introduced over 7 weeks. Questionnaires, programming tasks, and semistructured interviews were comprehensively applied to examine students’ learning outcomes. Finally, the jigsaw‐TDL group showed significantly better performance than the collaborative‐TDL group in learning motivation, computational thinking, and collaborative skills. However, it only led to better programming performance in the less complex tasks. The majority of students held a positive attitude toward the jigsaw‐TDL model, acknowledging its benefits in group collaboration, programming knowledge acquisition, and application. This research provides empirical evidence and potential guidance for task organization and collaborative learning support in programming courses and STEM education.

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