Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Vassar, Alexandra"'
This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,
Externí odkaz:
http://arxiv.org/abs/2411.01765
This paper introduces DCC Sidekick, a web-based conversational AI tool that enhances an existing LLM-powered C/C++ compiler by generating educational programming error explanations. The tool seamlessly combines code display, compile- and run-time err
Externí odkaz:
http://arxiv.org/abs/2408.02378
In the challenging field of introductory programming, high enrollments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated tools that scale to large cohorts. This paper presents and evaluates the
Externí odkaz:
http://arxiv.org/abs/2308.11873
Autor:
KEE, Sophia, VALERIE, Ivy Cerelia, KENNEDY, Georgina, FINDLAY, Merran, CHURCHES, Timothy, VASSAR, Alexandra
Publikováno v:
Studies in Health Technology & Informatics; 2024, Vol. 318, p60-65, 6p
Autor:
Vassar, Alexandra
Teaching mathematical concepts is often accompanied by the use of worked examples, and the use of manipulative materials. Worked examples have been shown to be an effective method of instruction with novice learners, as shown by higher test performan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f9e261da395e072ac4b8275bea1d533
Autor:
Vassar, Alexandra
The aim of this thesis is to determine whether selecting usability testing participants on the basis of their personality, as measured by the Myers-Briggs Type Indicator (MBTI) extraversion/introversion scale, can enhance the results obtained in usab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a21ff5abe57f09e06c5fe82ffc22ffe1