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Article
Publication date: 10 July 2024

Samuel Boguslawski, Rowan Deer and Mark G. Dawson

Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the…

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Abstract

Purpose

Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the experiences of programming educators and students to inform future education provision.

Design/methodology/approach

Twelve students and six members of faculty in a small technology-focused university were interviewed. Thematic analysis of the interview data was combined with data collected from a survey of 44 students at the same university. Self-determination theory was applied as an analytical framework.

Findings

Three themes were identified – bespoke learning, affect and support – that significantly impact motivation and learning outcomes in programming education. It was also found that students are already making extensive use of large language models (LLMs). LLMs can significantly improve learner autonomy and sense of competence by improving the options for bespoke learning; fostering emotions that are conducive to engendering and maintaining motivation; and inhibiting the negative affective states that discourage learning. However, current LLMs cannot adequately provide or replace social support, which is still a key factor in learner motivation.

Research limitations/implications

Integrating the use of LLMs into curricula can improve learning motivation and outcomes. It can also free educators from certain tasks, leaving them with more time and capacity to focus their attention on developing social learning opportunities to further enhance learner motivation.

Originality/value

To the best of the authors’ knowledge, this is the first attempt to explore the relationship between motivation and LLM use in programming education.

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Article
Publication date: 29 April 2024

Mohd Hasfarisham Abd Halim, Nor Khairunnisa Talib, Shyeh Sahibul Karamah Masnan and Mokhtar Saidin

This study was conducted with the main purpose of recording primary data related to environmental factors, which has become the main criteria in the selection of the Sungai Batu…

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Abstract

Purpose

This study was conducted with the main purpose of recording primary data related to environmental factors, which has become the main criteria in the selection of the Sungai Batu Archaeological Complex (SBAC) as the center of the iron smelting industry and trade in ancient Kedah.

Design/methodology/approach

To fulfill this purpose, field studies involving drone photogrammetry mapping, augering, core drilling and geophysical mapping methods were carried out.

Findings

The results obtained through the application of the method have shown that SBAC has a good environment, which has a wide and deep river flow, the existence of Mount Jerai and the abundance of iron ores, mangrove Merbok and clay.

Research limitations/implications

Resources did not allow for environment studies of the by-products tourism sites as part of the current study.

Practical implications

The study also included a survey and mapping to obtain potential primary data around SBAC in the process of developing it as the center of the world iron industry.

Social implications

One finding is that attention to heritage policy and protection must be ongoing at all levels of government and the local community to ensure that the survey and mapping data carried out can be developed as a sustainable heritage tourism product.

Originality/value

This study reveals primary data related to the suitability of paleoenvironment in the SBAC development process as a world iron smelting industry area.

Details

Journal of Cultural Heritage Management and Sustainable Development, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-1266

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