Understanding Fun in Learning to Code: A Multi-Modal Data approach

Gabriella Tisza; Kshitij Sharma; Sofia Papavlasopoulou; Panos Markopoulos; Michail Giannakos

doi:10.1145/3501712.3529716

Understanding Fun in Learning to Code: A Multi-Modal Data approach

Gabriella Tisza, Kshitij Sharma, Sofia Papavlasopoulou, Panos Markopoulos, Michail Giannakos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

Abstract

The role of fun in learning, and specifically in learning to code, is critical but not yet fully understood. Fun is typically measured by post session questionnaires, which are coarse-grained, evaluating activities that sometimes last an hour, a day or longer. Here we examine how fun impacts learning during a coding activity, combining continuous physiological response data from wristbands and facial expressions from facial camera videos, along with self-reported measures (i.e. knowledge test and reported fun). Data were collected from primary school students (N = 53) in a single-occasion, two-hours long coding workshop, with the BBC micro:bits. We found that a) sadness, anger and stress are negatively, and arousal is positively related to students' relative learning gain (RLG), b) experienced fun is positively related to students' RLG and c) RLG and fun are related to certain physiological markers derived from the physiological response data.

Original language	English
Title of host publication	Proceedings of Interaction Design and Children, IDC 2022
Publisher	Association for Computing Machinery, Inc
Pages	274-287
Number of pages	14
ISBN (Electronic)	9781450391979
DOIs	https://doi.org/10.1145/3501712.3529716
Publication status	Published - 27 Jun 2022
Event	21st ACM Interaction Design and Children Conference, IDC 2022 - Virtual, Online, Portugal Duration: 27 Jun 2022 → 30 Jun 2022

Conference

Conference	21st ACM Interaction Design and Children Conference, IDC 2022
Country/Territory	Portugal
City	Virtual, Online
Period	27/06/22 → 30/06/22

Bibliographical note

Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement NO 787476. This paper reflects only the authors’ views. The Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.

Funding Information:
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement NO 787476. This paper reflects only the authors' views. The Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains

Publisher Copyright:
© 2022 Owner/Author.

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement NO 787476. This paper reflects only the authors’ views. The Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement NO 787476. This paper reflects only the authors' views. The Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains

Funders	Funder number
European Union's Horizon 2020 - Research and Innovation Framework Programme
European Commission
Research Executive Agency
European Union's Horizon 2020 - Research and Innovation Framework Programme	787476

Keywords

Fun
FunQ
Learning
Multimodal Learning Analytics (MMLA)
Programming

Access to Document

10.1145/3501712.3529716

Cite this

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title = "Understanding Fun in Learning to Code: A Multi-Modal Data approach",

abstract = "The role of fun in learning, and specifically in learning to code, is critical but not yet fully understood. Fun is typically measured by post session questionnaires, which are coarse-grained, evaluating activities that sometimes last an hour, a day or longer. Here we examine how fun impacts learning during a coding activity, combining continuous physiological response data from wristbands and facial expressions from facial camera videos, along with self-reported measures (i.e. knowledge test and reported fun). Data were collected from primary school students (N = 53) in a single-occasion, two-hours long coding workshop, with the BBC micro:bits. We found that a) sadness, anger and stress are negatively, and arousal is positively related to students' relative learning gain (RLG), b) experienced fun is positively related to students' RLG and c) RLG and fun are related to certain physiological markers derived from the physiological response data.",

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note = "Funding Information: This project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement NO 787476. This paper reflects only the authors{\textquoteright} views. The Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains. Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement NO 787476. This paper reflects only the authors' views. The Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 21st ACM Interaction Design and Children Conference, IDC 2022 ; Conference date: 27-06-2022 Through 30-06-2022",

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Tisza, G, Sharma, K, Papavlasopoulou, S, Markopoulos, P & Giannakos, M 2022, Understanding Fun in Learning to Code: A Multi-Modal Data approach. in Proceedings of Interaction Design and Children, IDC 2022. Association for Computing Machinery, Inc, pp. 274-287, 21st ACM Interaction Design and Children Conference, IDC 2022, Virtual, Online, Portugal, 27/06/22. https://doi.org/10.1145/3501712.3529716

Understanding Fun in Learning to Code: A Multi-Modal Data approach. / Tisza, Gabriella; Sharma, Kshitij; Papavlasopoulou, Sofia et al.
Proceedings of Interaction Design and Children, IDC 2022. Association for Computing Machinery, Inc, 2022. p. 274-287.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N1 - Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement NO 787476. This paper reflects only the authors’ views. The Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains. Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement NO 787476. This paper reflects only the authors' views. The Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains Publisher Copyright: © 2022 Owner/Author.

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AB - The role of fun in learning, and specifically in learning to code, is critical but not yet fully understood. Fun is typically measured by post session questionnaires, which are coarse-grained, evaluating activities that sometimes last an hour, a day or longer. Here we examine how fun impacts learning during a coding activity, combining continuous physiological response data from wristbands and facial expressions from facial camera videos, along with self-reported measures (i.e. knowledge test and reported fun). Data were collected from primary school students (N = 53) in a single-occasion, two-hours long coding workshop, with the BBC micro:bits. We found that a) sadness, anger and stress are negatively, and arousal is positively related to students' relative learning gain (RLG), b) experienced fun is positively related to students' RLG and c) RLG and fun are related to certain physiological markers derived from the physiological response data.

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Understanding Fun in Learning to Code: A Multi-Modal Data approach

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Keywords

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