1,034
views
0
recommends
+1 Recommend
1 collections
    0
    shares

      Celebrating 65 years of The Computer Journal - free-to-read perspectives - bcs.org/tcj65

      scite_
       
      • Record: found
      • Abstract: found
      • Conference Proceedings: found
      Is Open Access

      The Impact of Language on Students’ Emotional States in Educational Games: A Comparative Study

      Published
      proceedings-article
      , ,
      35th International BCS Human-Computer Interaction Conference (HCI2022)
      Towards a Human-Centred Digital Society
      July 11th to 13th, 2022
      Educational game, emotions, Arabic language, English language
      Bookmark

            Abstract

            Capturing students’ emotions while playing an educational game is one approach to assess their motivation towards learning. The language of educational games could serve as a motivating factor for players. This study compares two languages (Arabic and English) in an educational game to understand and compare the effect of the two languages on learning motivation via emotions. An experimental study was conducted with 30 Arabic-speaking students (Male n=13, Female n= 17) while playing an educational game in both Arabic and English languages, and their emotions were recorded. The result shows that participants express significant negative emotions (anger [p < 0.05], contempt [p < 0.05], and sadness [p < 0.05]) while playing the Arabic version of the game than the English version. indicating that participants preferred the English version. These findings suggest that emotion might help evaluate language preference in educational games development.

            Content

            Author and article information

            Contributors
            Conference
            July 2022
            July 2022
            : 1-10
            Affiliations
            [0001]Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University

            Doha-Qatar
            Article
            10.14236/ewic/HCI2022.10
            ca302eb1-38f5-4c5d-8982-c485440f85e7
            © Assaf et al. Published by BCS Learning & Development. Proceedings of the 35th British HCI and Doctoral Consortium 2022, UK

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            35th International BCS Human-Computer Interaction Conference
            HCI2022
            35
            Keele, Staffordshire
            July 11th to 13th, 2022
            Electronic Workshops in Computing (eWiC)
            Towards a Human-Centred Digital Society
            History
            Product

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2022.10
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            emotions,Arabic language,Educational game,English language

            REFERENCES

            1. Antón, E., & Soleto, N. (2020). Recycling in babel: The impact of foreign languages in rule learning. International Journal of Environmental Research and Public Health, 17(11), 3784.

            2. Banerjee, A., Chitnis, U., Jadhav, S., Bhawalkar, J., & Chaudhury, S. (2009). Hypothesis testing, type Iand type II errors. Industrial Psychiatry Journal, 18, 127.

            3. Behoora, I., & Tucker, C. S. (2015). Machine learning classification of design team members’ body language patterns for real time emotional state detection. Design Studies, 39, 100--127.

            4. Bontchev, B., & Vassileva, D. (2016). Assessing engagement in an emotionally-adaptive applied game. Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, 747–754. https://doi.org/10.1145/3012430.3012602

            5. Council of Europe (2001) Common European Framework of Reference for Languages: Learning, teaching and assessment. Strasbourg: Council of

            6. Europe, Language Policy Unit: www.coe.int/lang-cefr (accessed 15 June 2019).

            7. Eberhard, DM, Gary, FS, & Fennig, CD. (2021). What are the top 200 most spoken languages. Ethnologue: Languages of the World.

            8. Fong, K. N., Ma, W., Pang, H., Tang, P. P., & Law, L.

            9. L. (2019). Immediate effects of coloured overlays on the reading performance of preschool children with an autism spectrum disorder using eye tracking. Research in Developmental Disabilities, 89, 141–148.

            10. Fugo Games. (n.d.). Fugo. Retrieved November 27, 2021, from https://fugo.com.tr/games/

            11. Goumas, S., Terzopoulos, G., Tsompanoudi, D., & Iliopoulou, A. (2020). Wordsearch, an Educational Game in Language Learning. Journal of Engineering Science \& Technology Review, 13.

            12. Iliou, T., & Anagnostopoulos, C.-N. (2009). Comparison of Different Classifiers for Emotion Recognition. In 2009 13th Panhellenic Conference on Informatics (pp. 102--106). IEEE.

            13. iMotions. (2017). iMotion Biometric Tool. Retrieved from https://imotions.com

            14. Judge, Gary. (2013). Instant BlueStacks: Packt Publishing Ltd.

            15. Kreitlon, J., Chen, L., Santos, L., Nascimento, R., Carrara, M. R., & Paiva Guedes, G. (2019). Affective Multisensorial Books. 190–195.

            16. Lazar, J., Feng, J. H., & Hochheiser, H. (2017). Research methods in human-computer interaction.

            17. Logitech Capture Video Recording & Streaming Software. (2021). https://www.logitech.com/en-us/product/capture

            18. Love, J., Selker, R., Marsman, M., Jamil, T., Tahira, D., Damian, V., Josine, L., Alexander, G., & Quentin, F. (2019). JASP: Graphical statistical software for common statistical designs. Journal of Statistical Software, 88, 1–17.

            19. MacKenzie, I. S. (2012). Human-computer interaction: An empirical research perspective. Newnes.

            20. McDuff, D., Mahmoud, A., Mavadati, M., Amr, M., Turcot, J., & Kaliouby, R. el. (2016). AFFDEX SDK: a cross-platform real-time multi-face expression recognition toolkit. Meyer, D. K., & Turner, J. C. (2006). Re-conceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review, 18(4), 377--390.

            21. Mustafawi, E., & Shaaban, K. (2019). Language policies in education in Qatar between 2003 and 2012: From local to global then back to local. Language Policy, 18(2), 209--242.

            22. Peterson, M. (2016). Computer games and language learning. Springer.

            23. Picard, R. W. (1999). Affective Computing for HCI. HCI (1), 829–833.

            24. Razali, N. M., & Wah, Y. B. (2011). Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. Journal of Statistical Modeling and Analytics, 2, 21--33.

            25. Reinders, H. (2012). Digital games in language learning and teaching. Springer.

            26. Sawyer, R., Smith, A., Rowe, J., Azevedo, R., & Lester, J. (2017). Enhancing student models in game-based learning with facial expression recognition. Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 192–201

            27. Squire, Kurt. (2003). Video games in education. Int. J. Intell. Games \& Simulation, 2(1), 49--62.

            28. Sykes, J. M. (2018). Digital games and language teaching and learning. Foreign Language Annals, 51(1), 219--224.

            29. Tekinbas, K. S., & Zimmerman, E. (2003). Rules of play: Game design fundamentals.

            30. Verma, V., Rheem, H., Amresh, A., Craig, S. D., & Bansal, A. (2020). Predicting Real-Time Affective States by Modeling Facial Emotions Captured During Educational Video Game Play. In International Conference on Games and Learning Alliance (pp. 447–452). Springer.

            31. Wang, Y.-H. (2010). Using communicative language games in teaching and learning English in Taiwanese primary schools. 7, 126--142.

            32. Wiklund, M., Rudenmalm, W., Norberg, L., Westin, T., & Mozelius, P. (2015). Evaluating educational games using facial expression recognition software: Measurement of gaming emotion. Proceedings of the European Conference on Games Based Learning, 605–612.

            33. Yadegaridehkordi, E., Noor, N. F. B. M., Ayub, M. N. B., Affal, H. B., & Hussin, N. B. (2019). Affective computing in education: A systematic review and future research. Computers & Education, 142, 103649.

            34. Yannakakis, G. N., & Paiva, A. (2014). Emotion in games. Handbook on Affective Computing, 2014, 459--471.

            Comments

            Comment on this article