Effects of Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts with Open-Ended Task

Valguima Odakura; Ishari Amarasinghe; Davinia Hernández-Leo; Roberto Sánchez-Reina; Emily Theophilou; René Lobo-Quintero

doi:10.1007/978-3-031-20218-6_2

Effects of Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts with Open-Ended Task

Valguima Odakura, Ishari Amarasinghe, Davinia Hernández-Leo, Roberto Sánchez-Reina, Emily Theophilou, René Lobo-Quintero

Research output: Book / Book Chapter / Report › Research Books › peer-review

Abstract

In this study we investigate the benefits that debriefing can add to collaborative Pyramid script with open-ended tasks. The open-ended task allows students to produce multiple possible solutions to a given problem and requires learners to express personal opinions based on previous experiences and intuitions. In this sense, misconceptions and gaps can appear in the collaboration process, demanding a teacher intervention. Debriefing, as part of teacher orchestration tasks, enables teachers to facilitate students’ reflection about the learning experience, correcting mistakes and filling gaps. Qualitative analysis of students’ answers through concepts and their relations was developed. The examination of concepts and relations supported the benefits of the Pyramid script with open-ended tasks and debriefing to learning. The results of the study indicate that students increased the concepts mentioned and built more relations between concepts after debriefinLg.

Original language	English
Title of host publication	Collaboration Technologies and Social Computing - 28th International Conference, CollabTech 2022, Proceedings
Editors	Lung-Hsiang Wong, Yugo Hayashi, Cesar A. Collazos, Claudio Alvarez, Gustavo Zurita, Nelson Baloian
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	23-37
Number of pages	15
ISBN (Print)	9783031202179
DOIs	https://doi.org/10.1007/978-3-031-20218-6_2
State	Published - 2022
Externally published	Yes
Event	28th International Conference on Collaboration Technologies and Social Computing, CollabTech 2022 - Santiago, Chile Duration: 8 Nov 2022 → 11 Nov 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13632 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th International Conference on Collaboration Technologies and Social Computing, CollabTech 2022
Country/Territory	Chile
City	Santiago
Period	8/11/22 → 11/11/22

Keywords

Computer-supported collaborative learning
Debriefing
Open-ended task
Pyramid script
Scripts

Access to Document

10.1007/978-3-031-20218-6_2

Cite this

Odakura, V., Amarasinghe, I., Hernández-Leo, D., Sánchez-Reina, R., Theophilou, E., & Lobo-Quintero, R. (2022). Effects of Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts with Open-Ended Task. In L-H. Wong, Y. Hayashi, C. A. Collazos, C. Alvarez, G. Zurita, & N. Baloian (Eds.), Collaboration Technologies and Social Computing - 28th International Conference, CollabTech 2022, Proceedings (pp. 23-37). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13632 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20218-6_2

Odakura, Valguima ; Amarasinghe, Ishari ; Hernández-Leo, Davinia et al. / Effects of Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts with Open-Ended Task. Collaboration Technologies and Social Computing - 28th International Conference, CollabTech 2022, Proceedings. editor / Lung-Hsiang Wong ; Yugo Hayashi ; Cesar A. Collazos ; Claudio Alvarez ; Gustavo Zurita ; Nelson Baloian. Springer Science and Business Media Deutschland GmbH, 2022. pp. 23-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this study we investigate the benefits that debriefing can add to collaborative Pyramid script with open-ended tasks. The open-ended task allows students to produce multiple possible solutions to a given problem and requires learners to express personal opinions based on previous experiences and intuitions. In this sense, misconceptions and gaps can appear in the collaboration process, demanding a teacher intervention. Debriefing, as part of teacher orchestration tasks, enables teachers to facilitate students{\textquoteright} reflection about the learning experience, correcting mistakes and filling gaps. Qualitative analysis of students{\textquoteright} answers through concepts and their relations was developed. The examination of concepts and relations supported the benefits of the Pyramid script with open-ended tasks and debriefing to learning. The results of the study indicate that students increased the concepts mentioned and built more relations between concepts after debriefinLg.",

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author = "Valguima Odakura and Ishari Amarasinghe and Davinia Hern{\'a}ndez-Leo and Roberto S{\'a}nchez-Reina and Emily Theophilou and Ren{\'e} Lobo-Quintero",

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Odakura, V, Amarasinghe, I, Hernández-Leo, D, Sánchez-Reina, R, Theophilou, E & Lobo-Quintero, R 2022, Effects of Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts with Open-Ended Task. in L-H Wong, Y Hayashi, CA Collazos, C Alvarez, G Zurita & N Baloian (eds), Collaboration Technologies and Social Computing - 28th International Conference, CollabTech 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13632 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 23-37, 28th International Conference on Collaboration Technologies and Social Computing, CollabTech 2022, Santiago, Chile, 8/11/22. https://doi.org/10.1007/978-3-031-20218-6_2

Effects of Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts with Open-Ended Task. / Odakura, Valguima; Amarasinghe, Ishari; Hernández-Leo, Davinia et al.

Collaboration Technologies and Social Computing - 28th International Conference, CollabTech 2022, Proceedings. ed. / Lung-Hsiang Wong; Yugo Hayashi; Cesar A. Collazos; Claudio Alvarez; Gustavo Zurita; Nelson Baloian. Springer Science and Business Media Deutschland GmbH, 2022. p. 23-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13632 LNCS).

Research output: Book / Book Chapter / Report › Research Books › peer-review

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AU - Theophilou, Emily

AU - Lobo-Quintero, René

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AB - In this study we investigate the benefits that debriefing can add to collaborative Pyramid script with open-ended tasks. The open-ended task allows students to produce multiple possible solutions to a given problem and requires learners to express personal opinions based on previous experiences and intuitions. In this sense, misconceptions and gaps can appear in the collaboration process, demanding a teacher intervention. Debriefing, as part of teacher orchestration tasks, enables teachers to facilitate students’ reflection about the learning experience, correcting mistakes and filling gaps. Qualitative analysis of students’ answers through concepts and their relations was developed. The examination of concepts and relations supported the benefits of the Pyramid script with open-ended tasks and debriefing to learning. The results of the study indicate that students increased the concepts mentioned and built more relations between concepts after debriefinLg.

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Odakura V, Amarasinghe I, Hernández-Leo D, Sánchez-Reina R, Theophilou E, Lobo-Quintero R. Effects of Debriefing in Computer-Supported Collaborative Learning Pyramid Scripts with Open-Ended Task. In Wong L-H, Hayashi Y, Collazos CA, Alvarez C, Zurita G, Baloian N, editors, Collaboration Technologies and Social Computing - 28th International Conference, CollabTech 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 23-37. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-20218-6_2