Maoinithe ag an Aontas Eorpach. Mar sin féin is tuairimí agus tuairimí an údair amháin nó na n-údar amháin iad na tuairimí agus na tuairimí a nochtar agus ní gá go léiríonn siad tuairimí an Aontais Eorpaigh nó na Gníomhaireachta Eorpaí Feidhmiúcháin Oideachais agus Cultúir (EACEA). Ní fhéadfar an tAontas Eorpach ná EACEA a chur i gcuntas astu. Uimhir an tionscadail: 2023-1-NL01-KA220-HED-000155675. Cuid 6: Cás-Staidéir Soláthraíonn an rannán seo tríocha sé (36) cás-staidéar náisiúnta/AE a thairgeann paraidímí fianaise-bhunaithe maidir le comhtháthú uirlisí AI in Institiúidí Ardoideachais, a nacmhainneacht agus a ndúshláin maidir le cleachtas gairmiúil agus oideolaíoch. Cás-Staidéar 1: Creat comhtháite chun córas uathoibrithe measúnaithe ar stíl léachta a fhorbairt agus a mheasúnú General information Dimitriadou, E., & Lanitis, A. (2023). An integrated framework for developing and evaluating an automated lecture style assessment system. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2312.00201 The study aims to develop and evaluate an integrated system that provides an automated evaluation of an instructor's lecture style. This system aims to help teachers by giving instant feedback on their lecturing style, to improve quality and enhance student learning experiences. Description of case The proposed application analysed and extracted measurable biometric characteristics from video cameras and audio sensors using machine learning. These characteristics included facial expressions, body activity, speech rate and tone, hand movements, and facial pose. These features, in combination, provided a score reflecting the quality of the lecture style. The system’s performance was evaluated by comparing its assessments with human evaluations and through feedback from education officers, teachers, and students. Lessons learned The results indicated that the system effectively provided automated feedback that participants received well. It performed comparably to humans and, in some cases, even outperformed them. Participants appreciated the application's utility in enhancing lecture delivery through immediate feedback. Implications for practice With similar or even fewer differences between AI-driven and human evaluation of lecture quality, the system can be used in natural settings (e.g., a university classroom) to support teachers in improving their lecturing and increasing student engagement. The researchers aim to further improve the system by refining the biometric metrics used in the automated lecture-style evaluation
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