Rivalizar o noanálisis del modo competición de Wooclap basado en rendimiento y procesamiento de audio
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Universidad de Murcia
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- Cruz Lemus, José Antonio (coord.)
- Medina Medina, Nuria (coord.)
- Rodríguez Fortiz, María José (coord.)
ISSN: 2531-0607
Ano de publicación: 2023
Título do exemplar: Actas de las XXIX Jornadas sobre la Enseñanza Universitaria de la Informática. Granada, del 5 al 7 de julio de 2023
Número: 8
Páxinas: 65-72
Tipo: Artigo
Outras publicacións en: Actas de las Jornadas sobre la Enseñanza Universitaria de la Informática (JENUI)
Resumo
This paper presents the results of applying, in a subject of the Degree in Computer Science, different modes of use for an interactive response system. The study used the Wooclap platform, which allows the teacher to prompt questions to the students to be answered in class using their mobile devices. An experience has been designed in which two different uses of the platform were analyzed: without competition and with point-based competition. Two separate groups of students answered, in different tests guided by the same teacher, the same questions but using different modes of use in each case. This quasi-experimental research takes as data sources the performance of the students in these tests (questiones correctly answered) and the general level of interaction in the classroom. We have used an AI-based audio recording analysis system to characterize the participation of different actors. The results obtained show that, depending on the mode, there are different patterns in the level of interaction of the students during the tests. However, the results also indicate that the mode used does not influence the performance obtained by the students.
Referencias bibliográficas
- Indrani Bhattacharya, Michael Foley, Ni Zhang, Tongtao Zhang, Christine Ku, Cameron Mine, Heng Ji, Christoph Riedl, Brooke Foucault Welles y Richard J Radke. A multimodal-sensor enabled room for unobtrusive group meeting analysis. En Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 347–355, 2018.
- Óscar Cánovas. Sistemas interactivos de respuesta: hay vida más allá de los concursos. Actas de las XXVII JENUI, pp. 243–250, 2021.
- Óscar Cánovas y Félix J. García. Analysis of classroom interaction using speaker diarization and discourse features from audio recordings. En Learning in the Age of Digital and Green Transition, pp. 67–74, 2023.
- José I. Castillo-Manzano, Mercedes Castro Nuño, Lourdes López-Valpuesta, María Teresa Sanz-Díaz y Rocío Yñiguez. Measuring the effect of ars on academic performance: A global meta-analysis. Computers & Education, 96:109– 121, 2016.
- Emily Dill. Do clickers improve library instruction? Lock in your answers now. The Journal of Academic Librarianship, 34(6):527–529, 2008.
- Debra Filer. Everyone’s answering: Using technology to increase classroom participation. Nursing education perspectives, 31(4):247–250, 2010.
- Jérôme Hutain y Nicolas Michinov. Improving student engagement during in-person classes by using functionalities of a digital learning environment. Computers & Education, 183:104496, 2022.
- Anusha James, Yi Han Victoria Chua, Tomasz Maszczyk, Ana Moreno Núñez, Rebecca Bull, Kerry Lee y Justin Dauwels. Automated classification of classroom climate by audio analysis. En 9th International Workshop on Spoken Dialogue System Technology, pp. 41–49. Springer, 2019.
- Robin H Kay y Ann LeSage. Examining the benefits and challenges of using audience response systems: A review of the literature. Computers & Education, 53(3):819–827, 2009.
- Catherine Lai, Jean Carletta y Steve Renals. Modelling participant affect in meetings with turn taking features. En Proc. Workshop of Affective Social Speech Signals, 2013.
- Tuan Dinh Nguyen, Marisa Cannata y Jason Miller. Understanding student behavioral engagement: Importance of student interaction with peers and teachers. The Journal of Educational Research, 111(2):163–174, 2018.
- Tae Jin Park, Naoyuki Kanda, Dimitrios Dimitriadis, Kyu J Han, Shinji Watanabe y Shrikanth Narayanan. A review of speaker diarization: Recent advances with deep learning. Computer Speech & Language, 72:101317, 2022.
- Alf Inge Wang. The wear out effect of a game-based student response system. Computers & Education, 82:217–227, 2015.
- Weiwen Wang, Sun Ran, Linda Huang y Valerie Swigart. Student perceptions of classic and game-based online student response systems. Nurse educator, 44(4):6–9, 2019