Abstract
Game-based learning offers rich learning opportunities, but open-ended games make it difficult to identify struggling students. Prior work compares student paths to a single expert’s “golden path.” This effort focuses on efficiency, but additional pathways may be required for learning. We examine data from middle schoolers who played Crystal Island, a learning game for microbiology. Results show higher learning gains for students with exploratory behaviors, with interactions between prior knowledge and frustration. Results have implications for designing adaptive scaffolding for learning and affective regulation.
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References
Andres, J.M., Hutt, S., Ocumpaugh, J., Baker, R.S., Nasiar, N., Porter, C.: How anxiety affects affect: a quantitative ethnographic investigation using affect detectors and data-targeted interviews. In: International Conference on Quantitative Ethnography, pp. 268–283 (2021)
Baker, R., Clark-Midura, J., Ocumpaugh, J.: Towards general models of effective science inquiry in virtual performance assessments. J. Comput. Assist. Learn. 32(3), 267–280 (2016)
Baker, R., et al.: Affect-targeted interviews for understanding student frustration. In: Proceedings of the International Conference on Artificial Intelligence & Education (2021)
DeFalco, J., et al.: Detecting and addressing frustration in a serious game for military training. Int. J. Artif. Intell. Educ. 28(2), 152–193 (2018)
D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instr. 22(2), 145–157 (2012)
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183 (1988)
Hart, S.G.: NASA-task load index (NASA-TLX); 20 years later. In: Proceedings of theHuman Factors & Ergonomics Society Annual Meeting, vol. 50, no. 9, pp. 904–908 (2006)
Hutt, S., Grafsgaard, J., D’Mello, S.: Time to scale: Generalizable affect detection for tens of thousands of students across an entire school year. In: Proceedings of the 2019, CHI, pp. 1–14 (2019)
Jensen, E., Hutt, S., D’Mello, S.: Generalizability of sensor-free affect detection models in a longitudinal dataset of tens of thousands of students. In: International EDM (2019)
Karumbaiah, S., Baker, R.S., Ocumpaugh, J.: The case of self-transitions in affective dynamics. In: Proceedings of the 20th International Conference on Artificial Intelligence in Education, pp. 172–181 (2019)
Liu, Z., Pataranutaporn, V., Ocumpaugh, J., Baker, R.S.J.d.: Sequences of frustration and confusion, and learning. In: Proceedings of the 6th International Conference on Educational Data Mining, pp. 114–120 (2013)
Min, W., et al.: Multimodal goal recognition in open-world digital games. In: Thirteenth Artificial Intelligence and Interactive Digital Entertainment Conference (2017)
Pedaste, M., et al.: Phases of inquiry-based learning: definitions and the inquiry cycle. Educ. Res. Rev. 14, 47–61 (2015)
Sabourin, J., Mott, B., Lester, J.: Discovering behavior patterns of self-regulated learners in an inquiry-based learning environment. In: International Conference on Artificial Intelligence in Education, pp. 209–218 (2013)
Snow, E., Likens, A., Jackson, T., McNamara, D.: Students’ walk through tutoring: Using a random walk analysis to profile students. In: Proceedings of the International Conference on Educational Data Mining (2013)
Sawyer, R., Rowe, J., Azevedo, R., Lester, J.: Filtered time series analyses of student problem-solving behaviors in game-based learning. In: International EDM (2018)
Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327 (1977)
Vail, A.K., Grafsgaard, J.F., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Predicting learning from student affective response to tutor questions. In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 154–164. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39583-8_15
Voloshin: Introduction to graph theory. Nova Science Publishers (2009)
Acknowledgement
This study was supported by NSF under IIS grant Award #2016943, Award #2016993, and #1409639. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.
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Nasiar, N. et al. (2023). It’s Good to Explore: Investigating Silver Pathways and the Role of Frustration During Game-Based Learning. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_77
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