Wyniki wyszukiwania - Biblioteka Nauki

1

A transformative approach to developing global human resources: An interdisciplinary framework for promoting personalised learning in Japanese universities

100%

Fritz R.

e-mentor

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2024

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tom 104

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nr 2

34-43

EN

Japanese government internationalisation initiatives in recent years have aimed to create undergraduate courses that nurture “global human resources”. These initiatives involve objectives associated with developing skills, attitudes, and knowledge related to foreign language skills and intercultural understanding. However, educators at Japanese universities responsible for their development and implementation often lack adequate guidance to achieve such objectives. To address this gap, the author employed an interdisciplinary approach to establish a framework for cultivating global human resources, by means of a qualitative inquiry involving visual and narrative methods carried out over a one-semester period. The study's results emphasise the transformative and personal relationships that learners establish when engaging in the development of global human resources, a perspective that is mostly absent in the intercultural competence literature. Moreover, this study underlines the importance of producing and implementing interdisciplinary educational solutions that encourage university students to become personally invested in developing global human resources, contributing to the advancement of knowledge in the field.

2

Do English learners from different countries approach (“compose”) topics differently? A study report

100%

Daszkiewicz M.

Crossroads. A Journal of English Studies

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2017

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tom 16

EN

The paper reports on a study with over 600 respondents (EFL students) from 18 countries, concerning their personal approach to mastering two selected topics (Clothes and Sport). The hypotheses and the major conclusions relate to four facets – structure, lexis, correctness, fluency – which are presented in the paper as components to which L2 students need to be positively oriented to fully master any given topic. The study reveals structural orientation (within and across topics) to be approached least positively and, contrary to initial expectations – correct use of language being as much desired as the mastery of lexis and attainment of fluency. Most crucial empirical observations concern intercultural differences detected on the topical level, discrepancies between declared practices and indecisiveness in learning, and resignation from learning habits boosting control over the language learnt. The study is grounded in the concept of ‘composing one’s own English’ as a personalised approach conducive to what is referred in the paper as formal control over it.

3

A Personalised Optimising Level Adaptation (OLA) Difficulty Algorithm for Scenario Simulations in Professional VR Simulators

58%

Wolański R. M. , Jędrasiak K.

Safety & Fire Technology

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2024

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tom Vol. 64, iss. 2

56--65

EN

Purpose: This study introduces the Optimising Level Adaptation (OLA) algorithm, designed to enhance scenario simulations for professional VR training by dynamically adjusting difficulty levels to match user performance, thereby supporting personalised learning and readiness for high-stakes situations such as firefighting and emergency response. Project and methods: The OLA algorithm divides scenario activities into blocks and adjusts their difficulty based on user performance in comparison to a reference group of AI-controlled agents. The algorithm’s efficacy was tested across three proprietary VR simulators covering diverse professional scenarios: public speaking, hydrogen electrolysis and mechanical technician operations. Each scenario was divided into ten blocks of varying difficulty (easy, medium, difficult), dynamically adjusted based on the user's performance. This structure enables rapid adaptation, making it particularly beneficial for fire and rescue training, where realistic, yet scalable, scenario complexity is critical to preparing for unpredictable conditions in the field. Results: Testing with 30 participants per simulator revealed an average final score of approximately 75%, closely aligning with the target success rate of 70%. The average number of difficulty level switches (between 0.8 and 1.16 across scenarios) demonstrated the algorithm’s effective adaptation to user performance, thus ensuring optimal engagement. The OLA algorithm’s capacity to tailor training difficulty in real time reflects its potential to enhance skill retention and readiness in emergency response settings, where maintaining user engagement at appropriate challenge levels is essential for preparedness in life-threatening situations. Conclusions: The OLA algorithm provides significant advancements in personalised VR training, particularly within fire- and rescue-related applications, by maintaining optimal engagement and adaptive challenge levels. The adaptability demonstrated across multiple scenarios indicates its versatility and potential for use in diverse high-risk training applications. Future research could enhance the OLA algorithm’s effectiveness by refining scenario block determination, therefore contributing to improved response times, decision-making and operational efficiency in the emergency services.

PL

Cel: W niniejszym badaniu przedstawiono algorytm Optimizing Level Adaptation (OLA), który ma na celu ulepszenie symulacji scenariuszy na potrzeby profesjonalnych szkoleń VR poprzez dynamiczne dostosowywanie poziomów trudności do wydajności użytkownika, wspierając w ten sposób spersonalizowane nauczanie i gotowość do radzenia sobie w sytuacjach wysokiego ryzyka, takich jak gaszenie pożarów i reagowanie na sytuacje awaryjne. Projekt i metody: Algorytm OLA dzieli działania scenariusza na bloki i dostosowuje ich trudność na podstawie wyników użytkownika w porównaniu do grupy referencyjnej agentów kontrolowanych przez AI. Skuteczność algorytmu została przetestowana w trzech zastrzeżonych symulatorach VR obejmujących różne scenariusze zawodowe: wystąpienia publiczne, elektrolizę wodoru i operacje technika mechanicznego. Każdy scenariusz został podzielony na dziesięć bloków o różnym stopniu trudności (łatwy, średni, trudny), dynamicznie dostosowywanych na podstawie wyników użytkownika.