A Scalable, Flexible, and Interpretable Analytic Pipeline for Stealth Assessment in Complex Digital Game-Based Learning Environments: Towards Generalizability
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Dec 17, 2024
Wenyi Lu
James Laffey
Troy D. Sadler
Joe Griffin
Sean P. Goggins
https://orcid.org/0000-0002-6449-3284
Abstract
The rapid advancement of technology necessitates innovative educational tools and curricula that empower learners to acquire and apply new knowledge and skills, particularly for complex, real-world problem-solving. Digital Game-Based Learning (DGBL) has emerged as a promising approach to engage students in meaningful learning experiences. However, one major challenge for DGBL adoption in formal education is the effective assessment of learners' performance aligned with specific educational standards, such as the Next Generation Science Standards (NGSS). This study addresses this challenge by proposing and evaluating a novel stealth assessment (SA) pipeline that leverages educational data mining techniques to enhance the generalizability and scalability of learning assessments across various DGBL contexts, while maintaining model interpretability and improving the flexibility of model selection. Our proposed analytical pipeline integrates both machine-learned and expert-crafted features to predict multiple learning outcomes, content knowledge, and scientific argumentation skills. The pipeline offers several innovations: (1) it captures intricate in-game behaviors and decision-making strategies; (2) it employs a three-layered unsupervised learning approach to reduce dimensionality and identify critical features; and (3) it provides a flexible framework by combining both in-game and learning progress data. We validate this pipeline within a 3D narrative DGBL environment, Mission HydroSci (MHS), demonstrating its utility in accurately assessing learning outcomes across multiple game contexts (units). Moreover, by employing Accumulated Local Effects (ALE) plots, this study interprets the black-box models' results, offering actionable insights into game design and pedagogical arrangements. Our findings reveal unexpected relationships between in-game performance and post-game learning outcomes, leading to recommendations for future DGBL design improvements. This study advances educational data mining by providing a scalable, flexible and interpretable framework for embedding SA into DGBL environments, thus extending the reach of data-driven learning assessments in educational game contexts. Future research will further explore the applicability and limitations of this pipeline across diverse educational settings. Codes and sample datasets can be found at https://github.com/augurlabs/2025-Lu-Et-Al
How to Cite
Lu, W., Laffey, J., Sadler, T., Griffin, J., & Goggins, S. (2024). A Scalable, Flexible, and Interpretable Analytic Pipeline for Stealth Assessment in Complex Digital Game-Based Learning Environments: Towards Generalizability. Journal of Educational Data Mining, 16(2), 214–303. https://doi.org/10.5281/zenodo.14503598
##plugins.themes.bootstrap3.article.details##
Keywords
stealth assessment, evidence-centered design, learning analytics, accumulated local effects plots, ensemble learning, latent variable learning, dimension reduction, unsupervised learning, digital game-based learning, machine learning, educational data mining
References
Abd-Alsabour, N. 2018. On the role of dimensionality reduction. J. Comput. 13, 5, 571–579.
Abdi, H. and Williams, L. J. 2010. Principal component analysis. Wiley interdisciplinary reviews: computational statistics 2, 4, 433–459.
Afyouni, I., Murad, A., and Einea, A. 2020. Adaptive rehabilitation bots in serious games. Sensors 20, 24, 7037.
Akpan, B. 2020. Classical and operant conditioning—ivan pavlov; burrhus skinner. In Science education in theory and practice: an introductory guide to learning theory, B. Akpan, Ed. Springer, Cham, 71–84.
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K. E., and Lester, J. 2018. Improving stealth assessment in game-based learning with lstm-based analytics. In Proceedings of International Conference on Educational Data Mining, M. Y. Kristy Elizabeth Boyer, Ed. International Educational Data Mining Society, Buffalo, NY, 208–218.
Alam, A. 2021. Should robots replace teachers? mobilisation of ai and learning analytics in education. In 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3). IEEE, IEEE, Mumbai, India, 1–12.
Altmann, A., Toloşi, L., Sander, O., and Lengauer, T. 2010. Permutation importance: a corrected feature importance measure. Bioinformatics 26, 10, 1340–1347.
Aluthman, E. S. 2016. The effect of using automated essay evaluation on esl undergraduate students’ writing skill. International Journal of English Linguistics 6, 5, 54–67.
Annetta, L. A., Murray, M. R., Laird, S. G., Bohr, S. C., and Park, J. C. 2006. Serious games: Incorporating video games in the classroom. Educause quarterly 29, 3, 16–22.
Apley, D. W. and Zhu, J. 2020. Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society Series B: Statistical Methodology 82, 4, 1059–1086.
Assiri, A. S., Nazir, S., and Velastin, S. A. 2020. Breast tumor classification using an ensemble machine learning method. Journal of Imaging 6, 6, 39.
Ayesha, S., Hanif, M. K., and Talib, R. 2020. Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion 59, 44–58.
Bada, S. O. and Olusegun, S. 2015. Constructivism learning theory: A paradigm for teaching and learning. Journal of Research & Method in Education 5, 6, 66–70.
Bahrami, M. R., Bahrami, B., Behboodi, F., and Pourrafie, S. 2023. Teaching the future: The vision of ai/chatgpt in education. In Agents and Multi-Agent Systems: Technologies and Applications, G. Jezic, J. Chen-Burger, R. S. M. Kusek, R. J. Howlett, and L. C. Jain, Eds. Vol. 1. Springer, Singapore, Rome, Italy, 393–402.
Barab, S. A., Gresalfi, M., and Ingram-Goble, A. 2010. Transformational play: Using games to position person, content, and context. Educational researcher 39, 7, 525–536.
Bartholomew, D. J., Knott, M., and Moustaki, I. 2011. Latent variable models and factor analysis: A unified approach. John Wiley & Sons, Online.
Bauer, G. R., Mahendran, M., Walwyn, C., and Shokoohi, M. 2022. Latent variable and clustering methods in intersectionality research: systematic review of methods applications. Social psychiatry and psychiatric epidemiology 57, 1–17.
Belland, B. R., Walker, A. E., and Kim, N. J. 2017. A bayesian network meta-analysis to synthesize the influence of contexts of scaffolding use on cognitive outcomes in stem education. Review of educational research 87, 6, 1042–1081.
Bellotti, F., Kapralos, B., Lee, K., Moreno-Ger, P., and Berta, R. 2013. Assessment in and of serious games: An overview. In Advances in Human-Computer Interaction, A. B. Barreto, Ed. Vol. 2013. Hindawi Limited London, UK, United Kingdom, London, UK, United Kingdom, 1–11.
Biedermann, D., Ciordas-Hertel, G.-P., Winter, M., Mordel, J., and Drachsler, H. 2023. Contextualized logging of on-task and off-task behaviours during learning. Journal of Learning Analytics 10, 2, 115–125.
Bird, K. A., Castleman, B. L., Mabel, Z., and Song, Y. 2021. Bringing transparency to predictive analytics: A systematic comparison of predictive modeling methods in higher education. AERA Open 7, 23328584211037630.
Blikstein, P. and Worsley, M. 2016. Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics 3, 2, 220–238.
Block, J. H. and Burns, R. B. 1976. Mastery learning. Review of research in education 4, 3–49.
Botelho, A. F., Baker, R. S., and Heffernan, N. T. 2019. Machine-learned or expert-engineered features? exploring feature engineering methods in detectors of student behavior and affect. In Proceedings of the Twelfth International Conference on Educational Data Mining, C. Lynch and A. Merceron, Eds. International Educational Data Mining Society., Montréal, Canada, 1–4.
Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., Lim, T., Ninaus, M., Ribeiro, C., and Pereira, J. 2016. An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education 94, 178–192.
Braad, E., Žavcer, G., and Sandovar, A. 2016. Processes and models for serious game design and development. In Entertainment Computing and Serious Games: International GI-Dagstuhl Seminar 15283, Dagstuhl Castle, Germany, July 5-10, 2015, Revised Selected Papers, R. Dörner, S. Göbel, M. Kickmeier-Rust, M. Masuch, and K. Zweig, Eds. Springer, Springer Cham, Online, 92–118.
Breiman, L. 2001. Random forests. Machine learning 45, 5–32.
Caballero-Hernández, J. A., Palomo-Duarte, M., Dodero, J. M., and Gaševic, D. 2024. Supporting skill assessment in learning experiences based on serious games through process mining techniques. In International Journal of Interactive Multimedia and Artificial Intelligence, D. Verdú, Ed. Vol. 8. Universidad Internacional de La Rioja (UNIR) in Spain, Spain, 146–159.
Calixto, I., Rios, M., and Aziz, W. 2019. Latent variable model for multi-modal translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, A. Korhonen, D. Traum, and L. Màrquez, Eds. Association for Computational Linguistics, Florence, Italy, 6392–6405.
Carbonaro, M., Cutumisu, M., Duff, H., Gillis, S., Onuczko, C., Schaeffer, J., Schumacher, A., Siegel, J., Szafron, D., and Waugh, K. 2006. Adapting a commercial role-playing game for educational computer game production. In GameOn North America Conference. Vol. 13. Institute for Operations Research and the Management Sciences (INFORMS), Monterey, CA, USA, 1–8.
Carvalho, M. B., Bellotti, F., Berta, R., De Gloria, A., Sedano, C. I., Hauge, J. B., Hu, J., and Rauterberg, M. 2015. An activity theory-based model for serious games analysis and conceptual design. Computers & education 87, 166–181.
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., and Lopez, A. 2020. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 408, 189–215.
Chaiklin, S. et al. 2003. The zone of proximal development in Vygotsky’s analysis of learning and instruction. Vygotsky’s educational theory in cultural context 1, 2, 39–64.
Champion, C. and Elkan, C. 2017. Visualizing the consequences of evidence in bayesian networks. arXiv preprint arXiv:1707.00791 1, 1, 1–9.
Chen, T. and Guestrin, C. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, B. Krishnapuram, M. Shah, A. Smola, C. Aggarwal, D. Shen, and R. Rastogi, Eds. Association for Computing Machinery, New York, NY, United States, San Francisco, USA, 785–794.
Chopade, P., Edwards, D., Khan, S. M., Andrade, A., and Pu, S. 2019. Cpsx: using ai-machine learning for mapping human-human interaction and measurement of cps teamwork skills. In 2019 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, IEEE, Arlington, VA, USA, 1–6.
Christensen, D. S., Jakobsen, M., and Kraus, M. 2018. The engagement effect of players’ agency over their characters’ motivation. In Interactivity, Game Creation, Design, Learning, and Innovation: 6th International Conference, ArtsIT 2017, and Second International Conference, DLI 2017, Heraklion, Crete, Greece, October 30–31, 2017, Proceedings 6, N. V. Anthony L. Brooks, Eva Brooks, Ed. Springer, Springer, Crete, Greece, 184–193.
Ciolacu, M., Tehrani, A. F., Binder, L., and Svasta, P. M. 2018. Education 4.0-artificial intelligence assisted higher education: early recognition system with machine learning to support students’ success. In 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging(SIITME). IEEE, IEEE, Online, 23–30.
Clark, D. B., Tanner-Smith, E. E., and Killingsworth, S. S. 2016. Digital games, design, and learning: A systematic review and meta-analysis. Review of educational research 86, 1, 79–122.
Conati, C., Porayska-Pomsta, K., and Mavrikis, M. 2018. Ai in education needs interpretable machine learning: Lessons from open learner modelling. In ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden, A. W. Been Kim, Kush R. Varshney, Ed. arXiv, Stockholm, Sweden, 21–27.
Covitt, B. A., Gunckel, K. L., and Anderson, C. W. 2009. Students’ developing understanding of water in environmental systems. The Journal of Environmental Education 40, 3, 37–51.
Darvenkumar, T. and Devi, V. A. 2022. A review on the impact of using e-games for social development and language acquisition in the english classroom. ECS Transactions 107, 1, 15713.
Dewey, J. 1974. John Dewey on education: Selected writings. University of Chicago, Chicago, Illinois, USA.
Dickey, M. D. 2011. Murder on grimm isle: The impact of game narrative design in an educational game-based learning environment. British Journal of Educational Technology 42, 3, 456–469.
Divjak, B. and Tomić, D. 2011. The impact of game-based learning on the achievement of learning goals and motivation for learning mathematics-literature review. Journal of information and organizational sciences 35, 1, 15–30.
Doleck, T., Lemay, D. J., Basnet, R. B., and Bazelais, P. 2020. Predictive analytics in education: a comparison of deep learning frameworks. Education and Information Technologies 25, 1951–1963.
Drey, T., Jansen, P., Fischbach, F., Frommel, J., and Rukzio, E. 2020. Towards progress assessment for adaptive hints in educational virtual reality games. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, S. Barbosa, C. Lampe, C. Appert, D. A. Shamma, S. Drucker, J. Williamson, and K. Yatani, Eds. Association for Computing Machinery, New York, NY, United States, Honolulu, HI, USA, 1–9.
Dyulicheva, Y. Y., Kosova, Y. A., and Uchitel, A. D. 2020. The augmented reality portal and hints usage for assisting individuals with autism spectrum disorder, anxiety and cognitive disorders. In Proceedings of the 3rd International Workshop on Augmented Reality in Education, A. E. K. Oleksandr Yu. Burov, Ed. CEUR Workshop Proceedings, CEUR, Kryvyi Rih, Ukraine, 251–262.
Eliseyev, A. and Aksenova, T. 2019. Personalized adaptive instruction design (paid) for brain–computer interface using reinforcement learning and deep learning: simulated data study. Brain-Computer Interfaces 6, 1-2, 36–48.
Eseryel, D., Ge, X., Ifenthaler, D., and Law, V. 2011. Dynamic modeling as a cognitive regulation scaffold for developing complex problem-solving skills in an educational massively multiplayer online game environment. Journal of Educational Computing Research 45, 3, 265–286.
Eseryel, D., Law, V., Ifenthaler, D., Ge, X., and Miller, R. 2014. An investigation of the interrelationships between motivation, engagement, and complex problem solving in game-based learning. Journal of Educational Technology & Society 17, 1, 42–53.
Estevez, J., Garate, G., Guede, J., and Graña, M. 2019. Using scratch to teach undergraduate students’ skills on artificial intelligence. arXiv preprint arXiv:1904.00296. 1, 1, 1–6.
Frommel, J., Rogers, K., Brich, J., Besserer, D., Bradatsch, L., Ortinau, I., Schabenberger, R., Riemer, V., Schrader, C., and Weber, M. 2015. Integrated questionnaires: Maintaining presence in game environments for self-reported data acquisition. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, A. L. Cox, P. Cairns, R. Bernhaupt, and L. Nacke, Eds. Association for Computing Machinery, New York, NY, United States, Seattle, Washington, USA, 359–368.
Gaikwad, P. 2022. Sustainable learning: Implications from the law of the minimum. Spicer Adventist University Research Articles Journal 1, 1, 18–25.
Gee, J. P. 2003. What video games have to teach us about learning and literacy. Computers in entertainment (CIE) 1, 1, 20–20.
Georgiadis, K., van Lankveld, G., Bahreini, K., and Westera, W. 2019. Learning analytics should analyse the learning: proposing a generic stealth assessment tool. In 2019 IEEE Conference on Games (CoG). IEEE, IEEE, London, United Kingdom, 1–8.
Georgiadis, K., van Lankveld, G., Bahreini, K., and Westera, W. 2020. On the robustness of stealth assessment. IEEE Transactions on Games 13, 2, 180–192.
Göbel, S., de Carvalho Rodrigues, A., Mehm, F., and Steinmetz, R. 2009. Narrative game-based learning objects for story-based digital educational games. narrative 14, 16.
Göbel, S. and Mehm, F. 2013. Personalized, adaptive digital educational games using narrative game-based learning objects. In Serious Games and Virtual Worlds in Education, Professional Development, and Healthcare, W. B. Klaus Bredl, Ed. Informaiton Science Reference, Online, 74–84.
Grey, S., Grey, D., Gordon, N., and Purdy, J. 2017. Using formal game design methods to embed learning outcomes into game mechanics and avoid emergent behaviour. International journal of game-based learning (IJGBL) 7, 3, 63–73.
Gris, G. and Bengtson, C. 2021. Assessment measures in game-based learning research: A systematic review. International Journal of Serious Games 8, 1, 3–26.
Gunckel, K. L., Covitt, B. A., and Anderson, C. W. 2009. Learning a secondary discourse: shifts from force-dynamic to model-based reasoning in understanding water in socioecological systems. In Proceedings of Learning Progressions in Science (LeaPS) Conference, Iowa City, IA, A. W. G. Alicia C. Alonzo, Ed. LeaPS Learning Progressions in Science, Iowa City, IA, USA, 1–19.
Gupta, A., Carpenter, D., Min, W., Rowe, J. P., Azevedo, R., and Lester, J. C. 2021. Multimodal multi-task stealth assessment for reflection-enriched game-based learning. In Proceedings of the First International Workshop on Multimodal Artificial Intelligence in Education (MAIED 2021) At the 22nd International Conference on Artificial Intelligence in Education (AIED 2021), Online, June 14th, 2021, D. D. Mitri, R. M. Maldonado, O. C. Santos, J. Schneider, K. A. M. Sanusi, M. Cukurova, D. Spikol, I. Molenaar, M. N. Giannakos, R. Klemke, and R. Azevedo, Eds. CEUR Workshop Proceedings, vol. 2902. CEUR-WS.org, Online, 93–102.
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., and Hussain, A. 2024. Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation 16, 1, 45–74.
Heine, C. 2020. Towards modeling visualization processes as dynamic bayesian networks. IEEE Transactions on Visualization and Computer Graphics 27, 2, 1000–1010.
Henderson, N., Acosta, H., Min, W., Mott, B., Lord, T., Reichsman, F., Dorsey, C., Wiebe, E., and Lester, J. 2022. Enhancing stealth assessment in game-based learning environments with generative zero-shot learning. In Proceedings of the 15th International Conference on Educational Data Mining, A. Mitrovic and N. Bosch, Eds. International Educational Data Mining Society, Durham, United Kingdom, 171–182.
Henderson, N., Kumaran, V., Min, W., Mott, B., Wu, Z., Boulden, D., Lord, T., Reichsman, F., Dorsey, C., Wiebe, E., et al. 2020. Enhancing student competency models for game-based learning with a hybrid stealth assessment framework. In Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020), A. Rafferty and J. R. Whitehill, Eds. International Educational Data Mining Society, Online, 92–103.
Hooshyar, D., Ahmad, R. B., Yousefi, M., Fathi, M., Horng, S.-J., and Lim, H. 2016. Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills. Computers & Education 94, 18–36.
Hooshyar, D., Malva, L., Yang, Y., Pedaste, M., Wang, M., and Lim, H. 2021. An adaptive educational computer game: Effects on students’ knowledge and learning attitude in computational thinking. Computers in Human Behavior 114, 106575.
Hosmer Jr, D. W., Lemeshow, S., and Sturdivant, R. X. 2013. Applied logistic regression. John Wiley & Sons, Online.
Huang, W.-H. 2011. Evaluating learners’ motivational and cognitive processing in an online game-based learning environment. Computers in Human Behavior 27, 2, 694–704.
Huang, X., Wu, L., and Ye, Y. 2019. A review on dimensionality reduction techniques. International Journal of Pattern Recognition and Artificial Intelligence 33, 10, 1950017.
Hyvärinen, A. and Oja, E. 2000. Independent component analysis: algorithms and applications. Neural networks 13, 4-5, 411–430.
Ifenthaler, D. 2014. Toward automated computer-based visualization and assessment of team-based performance. Journal of Educational Psychology 106, 3, 651.
Ifenthaler, D., Eseryel, D., and Ge, X. 2012. Assessment for game-based learning. Springer, Online.
Ishak, S. A., Hasran, U. A., and Din, R. 2023. Media education through digital games: a review on design and factors influencing learning performance. Education Sciences 13, 2, 102.
Jackson, L. C., O’Mara, J., Moss, J., and Jackson, A. C. 2018. A critical review of the effectiveness of narrative-driven digital educational games. International Journal of Game-Based Learning (IJGBL) 8, 4, 32–49.
Johnson, C. I., Bailey, S. K., and Van Buskirk, W. L. 2017. Designing effective feedback messages in serious games and simulations: A research review. In Instructional Techniques to Facilitate Learning and Motivation of Serious Games, P. Wouters and H. van Oostendorp, Eds. Springer Cham, Switzerland, 119–140.
Kamel, H., Abdulah, D., and Al-Tuwaijari, J. M. 2019. Cancer classification using gaussian naive bayes algorithm. In 2019 International Engineering Conference (IEC). IEEE, IEEE, Erbil, Iraq, 165–170.
Kanal, V., Brady, J., Nambiappan, H., Kyrarini, M., Wylie, G., and Makedon, F. 2020. Towards a serious game based human-robot framework for fatigue assessment. In Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, F. Makedon, Ed. Association for Computing Machinery, New York, NY, United States, Online, 1–6.
Khenissi, M. A., Essalmi, F., and Jemni, M. 2013. Toward the personalization of learning games according to learning styles. In 2013 International Conference on Electrical Engineering and Software Applications. IEEE, IEEE, Hammamet, Tunisia, 1–6.
Kim, Y. and Rosenheck, L. 2020. Reimagining assessment through play: A case study of metarubric. In Re-imagining University Assessment in a Digital World, M. Bearman, P. Dawson, R. Ajjawi, J. Tai, and D. Boud, Eds. Vol. 7. Springer, Cham, Switzerland, 263–288.
Kim, Y. J. and Ifenthaler, D. 2019. Game-based assessment: The past ten years and moving forward. In Game-Based Assessment Revisited, D. Ifenthaler and Y. J. Kim, Eds. Springer, Cham, Switzerland, 3–11.
Kirsch, I., Lynn, S. J., Vigorito, M., and Miller, R. R. 2004. The role of cognition in classical and operant conditioning. Journal of clinical psychology 60, 4, 369–392.
Klema, V. and Laub, A. 1980. The singular value decomposition: Its computation and some applications. IEEE Transactions on automatic control 25, 2, 164–176.
Kuznetsov, V. S., Moiseienko, M. V., Moiseienko, N. V., Rostalny, B. A., and Kiv, A. E. 2020. Using unity to teach game development. In Proceedings of the 1st Symposium on Advances in Educational Technology (AET 2020). Vol. 2. Science and Technology Publications, Lda, Online, 506–515.
Laffey, J., Griffin, J., Sigoloff, J., Sadler, T., Goggins, S., Womack, A., Wulff, E., and Lander, S. 2019. Mission hydrosci: Meeting learning standards through gameplay. In Proceedings of The International Conference on Computer-Supported Collaborative Learning (CSCL), G. N. Kristine Lund, Ed. International Society of the Learning Sciences (ISLS), Lyon, France, 1017–1020.
Laffey, J. M., Sadler, T. D., Goggins, S. P., Griffin, J., and Babiuch, R. N. 2019. Mission hydrosci: Distance learning through game-based 3d virtual learning environments. In Virtual Reality in education: Breakthroughs in research and practice, I. R. M. A. (IRMA), Ed. IGI Global, Lyon, France, 623–643.
Lee, D. D. and Seung, H. S. 1999. Learning the parts of objects by non-negative matrix factorization. nature 401, 6755, 788–791.
Leonardou, A., Rigou, M., and Garofalakis, J. 2020. Techniques to motivate learner improvement in game-based assessment. information 11, 4, 176.
Lin, H.-C. K., Lin, Y.-H., Wang, T.-H., Su, L.-K., and Huang, Y.-M. 2021. Effects of incorporating augmented reality into a board game for high school students’ learning motivation and acceptance in health education. Sustainability 13, 6, 3333.
Lin, P.-H. and Chen, S.-Y. 2020. Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access 8, 45689–45699.
Liu, T. and Israel, M. 2022. Uncovering students’ problem-solving processes in game-based learning environments. Computers & Education 182, 104462.
Loh, C. S., Sheng, Y., and Ifenthaler, D. 2015. Serious games analytics: Theoretical framework. In Serious games analytics: Methodologies for performance measurement, assessment, and improvement, D. I. Christian Sebastian Loh, Yanyan Sheng, Ed. Springer Cham, Switzerland, 3–29.
López, C. and Tucker, C. 2018. Toward personalized adaptive gamification: a machine learning model for predicting performance. IEEE transactions on Games 12, 2, 155–168.
Lu, W., Griffin, J., Sadler, T. D., Laffey, J., and Goggins, S. P. 2023. Serious game analytics by design: Feature generation and selection using game telemetry and game metrics–toward predictive model construction. Journal of Learning Analytics 10, 1, 168–188.
Lu, Z., Chiu, M. M., Cui, Y., Mao, W., and Lei, H. 2023. Effects of game-based learning on students’ computational thinking: A meta-analysis. Journal of Educational Computing Research 61, 1, 235–256.
Luckin, R. and Cukurova, M. 2019. Designing educational technologies in the age of ai: A learning sciences-driven approach. British Journal of Educational Technology 50, 6, 2824–2838.
Lundberg, S. and Lee, S.-I. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus, Eds. Curran Associates Inc. 57 Morehouse LaneRed Hook, NY, United States, Long Beach, California, USA, 1–10.
Martinez-Garza, M. M. and Clark, D. B. 2017. Investigating epistemic stances in game play with data mining. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 9, 3, 1–40.
McGaghie, W. C. and Harris, I. B. 2018. Learning theory foundations of simulation-based mastery learning. Simulation in Healthcare 13, 3S, S15–S20.
Miladinovic, I., Schefer-Wenzl, S., and Bajic-Kerndl, K. 2023. Impact of a mobile serious game on learning outcomes for complex problem solving skills. In 2023 IEEE Global Engineering Education Conference (EDUCON). IEEE, IEEE, Salmiya, Kuwait, 1–7.
Min, W., Frankosky, M. H., Mott, B. W., Rowe, J. P., Smith, A., Wiebe, E., Boyer, K. E., and Lester, J. C. 2019. Deepstealth: Game-based learning stealth assessment with deep neural networks. IEEE Transactions on Learning Technologies 13, 2, 312–325.
Mislevy, R. J., Almond, R. G., and Lukas, J. F. 2003. A brief introduction to evidence-centered design. ETS Research Report Series 2003, 1, i–29.
Moore, G. R. and Shute, V. J. 2017. Improving learning through stealth assessment of conscientiousness. In Handbook on digital learning for K-12 schools, T. H. Ann Marcus-Quinn, Ed. Springer, Online, 355–368.
Mouri, K., Okubo, F., Shimada, A., and Ogata, H. 2016. Bayesian network for predicting students’ final grade using e-book logs in university education. In 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT). IEEE, IEEE, University of Macau, Macao, China, 85–89.
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., and Ghazi Saeedi, M. 2021. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments 29, 1, 142–163.
Mulwa, C., Lawless, S., Sharp, M., Arnedillo-Sanchez, I., and Wade, V. 2010. Adaptive educational hypermedia systems in technology enhanced learning: a literature review. In Proceedings of the 2010 ACM Conference on Information Technology Education, M. Stinson, N. Cregger, K. Baker, and R. Friedman, Eds. Association for Computing Machinery, New York, NY, United States, University of Maryland, College Park, Maryland, USA, 73–84.
Natekin, A. and Knoll, A. 2013. Gradient boosting machines, a tutorial. Frontiers in neurorobotics 7, 21.
National Research Council. 2013. Monitoring progress toward successful K-12 STEM education: A nation advancing? National Academies Press.
Naul, E. and Liu, M. 2020. Why story matters: A review of narrative in serious games. Journal of Educational Computing Research 58, 3, 687–707.
Nguyen, H. A., Hou, X., Stamper, J., and McLaren, B. M. 2020. Moving beyond test scores: Analyzing the effectiveness of a digital learning game through learning analytics. In Proceedings of the Thirteenth International Conference on Educational Data Mining (EDM 2020), A. Rafferty and J. R. Whitehill, Eds. International Educational Data Mining Society, Online, 487–495.
Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E. 2015. A review of relational machine learning for knowledge graphs. Proceedings of the IEEE 104, 1, 11–33.
Nie, H., Xiao, H. M., and Shang, J. J. 2014. A critical analysis of the studies on fostering creativity through game-based learning. In Hybrid Learning. Theory and Practice: 7th International Conference, ICHL 2014, Shanghai, China, August 8-10, 2014. Proceedings 7, S. K. S. Cheung, J. Fong, L. F. Kwok, J. Zhang, and R. Kwan, Eds. Springer, Cham, Shanghai, China, 278–287.
Nieland, T., Fehrenbach, A., Marowsky, M., and Burfeind, M. 2021. The teacher-centered perspective on digital game-based learning: Quantitative and qualitative evaluation methods from diverse disciplines. In Game-Based Learning across the Disciplines, D. I. Carmela Aprea, Ed. Springer, Switzerland, 341–362.
Oren, M., Pedersen, S., and Butler-Purry, K. L. 2020. Teaching digital circuit design with a 3-d video game: The impact of using in-game tools on students’ performance. IEEE Transactions on Education 64, 1, 24–31.
Osborne, S. P., Radnor, Z., and Nasi, G. 2013. A new theory for public service management? toward a (public) service-dominant approach. The American Review of Public Administration 43, 2, 135–158.
Peng, T., Luo, Y., and Liu, Y. 2022. Ai-based equipment optimization of the design on intelligent education curriculum system. In Wireless Communications and Mobile Computing, S. Rani, Ed. Vol. 2022. Hindawi, Baffins Lane, Chichester, West Sussex PO19 1UD, United Kingdom, 1–13.
Peterson, L. E. 2009. K-nearest neighbor. Scholarpedia 4, 2, 1883.
Pivec, M., Dziabenko, O., and Schinnerl, I. 2003. Aspects of game-based learning. In Proceedings of the 3rd International Conference on Knowledge Management, Graz, Austria, K. T. Hermann Maurer, Ed. Vol. 304. Springer Verlag Heidelberg, Graz, Austria, 216–225.
Plass, J. L., Homer, B. D., MacNamara, A., Ober, T., Rose, M. C., Pawar, S., Hovey, C. M., and Olsen, A. 2020. Emotional design for digital games for learning: The effect of expression, color, shape, and dimensionality on the affective quality of game characters. Learning and instruction 70, 101194.
Prensky, M. 2001. Digital natives, digital immigrants part 2: Do they really think differently? On the Horizon 1, 1, 1–9.
Rahimi, S., Shute, V., Kuba, R., Dai, C.-P., Yang, X., Smith, G., and Fernández, C. A. 2021. The use and effects of incentive systems on learning and performance in educational games. Computers & Education 165, 104135.
Reeves, T., Romine, W., Laffey, J., Sadler, T., and Goggins, S. 2020. Distance learning through game-based 3d virtual learning environments: Mission hydro science. evaluation report for mission hydrosci. Grantee Submission 1, 1, 1–31.
Romero, M., Usart, M., and Ott, M. 2015. Can serious games contribute to developing and sustaining 21st century skills? Games and culture 10, 2, 148–177.
Rosé, C. P., McLaughlin, E. A., Liu, R., and Koedinger, K. R. 2019. Explanatory learner models: Why machine learning (alone) is not the answer. British Journal of Educational Technology 50, 6, 2943–2958.
Rowe, E., Asbell-Clarke, J., and Baker, R. S. 2015. Serious games analytics to measure implicit science learning. In Serious games analytics: Methodologies for performance measurement, assessment, and improvement, C. S. Loh, Y. Sheng, and D. Ifenthaler, Eds. Springer, Switzerland, 343–360.
Rowe, E., Asbell-Clarke, J., Baker, R. S., Eagle, M., Hicks, A. G., Barnes, T. M., Brown, R. A., and Edwards, T. 2017. Assessing implicit science learning in digital games. Computers in Human Behavior 76, 617–630.
Rowe, J. P., Shores, L. R., Mott, B. W., and Lester, J. C. 2010. Individual differences in gameplay and learning: a narrative-centered learning perspective. In Proceedings of the Fifth International Conference on the Foundations of Digital Games, Y. P. Ian Horswill, Ed. Association for Computing Machinery, New York, NY, United States, Worcester Polytechnic Institute (WPI) in Worcester, Massachusetts, USA, 171–178.
Sadler, T. D., Nguyen, H., and Lankford, D. 2017. Water systems understandings: a framework for designing instruction and considering what learners know about water. Wiley Interdisciplinary Reviews: Water 4, 1, e1178.
Sakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutaporn, P., Surareungchai, W., Pataranutaporn, P., and Subsoontorn, P. 2018. Kids making ai: Integrating machine learning, gamification, and social context in stem education. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). IEEE, IEEE, Wollongong, Australia, 1005–1010.
Saleh, A., Chen, Y., Hmelo-Silver, C., Glazewski, K., Mott, B., Taylor, R., Rowe, J., and Lester, J. 2019. Supporting collaborative problem solving in a game-based learning environment. In The Proceedings of the International Conference on Computer-Supported Collaborative Learning (CSCL). International Society of the Learning Sciences (ISLS), Lyon, France, 1029–1032.
Sanchez, D. R. and Lee, C. A. 2022. Understanding the challenges of game-based training: Recommendations for moving research forward in game-based learning. In Handbook of Research on the Influence and Effectiveness of Gamification in Education, A. C. Moreira, O. Bernardes, and V. Amorim, Eds. IGI Global, Online, 541–578.
Sasada, T., Liu, Z., Baba, T., Hatano, K., and Kimura, Y. 2020. A resampling method for imbalanced datasets considering noise and overlap. Procedia Computer Science 176, 420–429.
Schölkopf, B., Smola, A., and Müller, K.-R. 1997. Kernel principal component analysis. In Proceedings of the International Conference on Artificial Neural Networks, W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, Eds. Springer, Alghero, Sardinia, Italy, 583–588.
Serhan, B., Said, B., Cheniti, L., and El Khayat, G. 2019. Personalization in serious games for assessment. In The 12th Annual International Conference of Education, Research and Innovation(ICERI2019) Proceedings. IATED, IATED, Seville, Spain, 4845–4852.
Serrano-Laguna, Á., Martínez-Ortiz, I., Haag, J., Regan, D., Johnson, A., and Fernández-Manjón, B. 2017. Applying standards to systematize learning analytics in serious games. Computer Standards & Interfaces 50, 116–123.
Shaffer, D. W. 2006. Epistemic frames for epistemic games. Computers & education 46, 3, 223–234.
Sharma, K., Papamitsiou, Z., and Giannakos, M. 2019. Building pipelines for educational data using ai and multimodal analytics: A “grey-box” approach. British Journal of Educational Technology 50, 6, 3004–3031.
Shute, V., Ke, F., and Wang, L. 2017. Assessment and adaptation in games. In Instructional Techniques to Facilitate Learning and Motivation of Serious Games, H. v. O. Pieter Wouters, Ed. Springer Cham, Switzerland, 59–78.
Shute, V., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C.-P., Kuba, R., Liu, Z., Yang, X., and Sun, C. 2021. Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. Journal of Computer Assisted Learning 37, 1, 127–141.
Shute, V. and Ventura, M. 2013. Stealth assessment: Measuring and supporting learning in video games. The MIT Press, Online.
Shute, V. J. 2011. Stealth assessment in computer-based games to support learning. Computer games and instruction 55, 2, 503–524.
Shute, V. J. and Kim, Y. J. 2014. Formative and stealth assessment. In Handbook of Research on Educational Communications and Technology, J. M. Spector, M. D. Merrill, J. Elen, and M. J. Bishop, Eds. Springer New York, NY, New York, NY, 311–321.
Shute, V. J. and Rahimi, S. 2021. Stealth assessment of creativity in a physics video game. Computers in Human Behavior 116, 106647.
Shute, V. J., Ventura, M., Bauer, M., and Zapata-Rivera, D. 2009. Melding the power of serious games and embedded assessment to monitor and foster learning. Serious games: Mechanisms and effects 2, 295–321.
Shute, V. J., Wang, L., Greiff, S., Zhao, W., and Moore, G. 2016. Measuring problem solving skills via stealth assessment in an engaging video game. Computers in Human Behavior 63, 106–117.
Slavin, R. E. 1987. Mastery learning reconsidered. Review of educational research 57, 2, 175–213.
Smith, G., Shute, V., and Muenzenberger, A. 2019. Designing and validating a stealth assessment for calculus competencies. Journal of Applied Testing Technology 20, S1, 52–59.
Soflano, M., Connolly, T. M., and Hainey, T. 2015. An application of adaptive games-based learning based on learning style to teach sql. Computers & Education 86, 192–211.
Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., and Postma, E. 2006. Adaptive game ai with dynamic scripting. Machine Learning 63, 217–248.
Staddon, J. E. and Cerutti, D. T. 2003. Operant conditioning. Annual review of psychology 54, 1, 115–144.
Steinemann, A. 2003. Implementing sustainable development through problem-based learning: Pedagogy and practice. Journal of Professional Issues in Engineering Education and Practice 129, 4, 216–224.
Steinmaurer, A., Sackl, M., and Gütl, C. 2021. Engagement in in-game questionnaires-perspectives from users and experts. In 2021 7th International Conference of the Immersive Learning Research Network (iLRN). IEEE, IEEE, University of Westminster, London, UK, 1–7.
Streicher, A. and Smeddinck, J. D. 2016. Personalized and adaptive serious games. In Entertainment Computing and Serious Games: International GI-Dagstuhl Seminar 15283, Dagstuhl Castle, Germany, July 5-10, 2015, Revised Selected Papers, R. Dörner, S. Göbel, M. Kickmeier-Rust, M. Masuch, and K. Zweig, Eds. Springer Cham, Springer Cham, Dagstuhl Castle, Germany, 332–377.
Sun, C.-T., Wang, D.-Y., and Chan, H.-L. 2011. How digital scaffolds in games direct problem-solving behaviors. Computers & Education 57, 3, 2118–2125.
Sunarya, P. A. 2022. Machine learning and artificial intelligence as educational games. International Transactions on Artificial Intelligence 1, 1, 129–138.
Sweller, J. 1994. Cognitive load theory, learning difficulty, and instructional design. In Learning and Instruction, Elsevier, Ed. Vol. 4. Elsevier, 295–312.
Sweller, J. 2011. Cognitive load theory. In Psychology of Learning and Motivation. Vol. 55. Elsevier, Online, 37–76.
Tadayon, M. and Pottie, G. J. 2020. Predicting student performance in an educational game using a hidden markov model. IEEE Transactions on Education 63, 4, 299–304.
Tharwat, A. 2016. Linear vs. quadratic discriminant analysis classifier: a tutorial. International Journal of Applied Pattern Recognition 3, 2, 145–180.
Tumenayu, O. O., Shabalina, O., Kamaev, V., and Davtyan, A. 2014. Using agent-based technologies to enhance learning in educational games. In Proceedings of the International Conference e-Learning 2014. Multi Conference on Computer Science and Information Systems (Lisbon, Portugal, July 15-19, 2014), M. Baptista Nunes and M. McPherson, Eds. International Association for Development of the Information Society, Lisbon, Portugal, 149–155.
Verleysen, M. and François, D. 2005. The curse of dimensionality in data mining and time series prediction. In Computational Intelligence and Bioinspired Systems. 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Proceedings, F. S. Joan Cabestany, Alberto Prieto, Ed. Springer Berlin, Heidelberg, Springer Berlin, Heidelberg, Vilanova i la Geltrú, Barcelona, Spain, 758–770.
Wadoux, A. M.-C. and Molnar, C. 2022. Beyond prediction: methods for interpreting complex models of soil variation. Geoderma 422, 115953.
Wang, A., Ramaswamy, V. V., and Russakovsky, O. 2022. Towards intersectionality in machine learning: Including more identities, handling underrepresentation, and performing evaluation. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, United States, COEX exhibition center in Seoul, South Korea, 336–349.
Wang, L.-H., Chen, B., Hwang, G.-J., Guan, J.-Q., and Wang, Y.-Q. 2022. Effects of digital game-based stem education on students’ learning achievement: a meta-analysis. International Journal of STEM Education 9, 1, 1–13.
Wang, Y., Yao, H., and Zhao, S. 2016. Auto-encoder based dimensionality reduction. Neurocomputing 184, 232–242.
Wattenberg, M., Viégas, F., and Johnson, I. 2016. How to use t-sne effectively. Distill 1, 10, e2.
Wu, W.-H., Hsiao, H.-C., Wu, P.-L., Lin, C.-H., and Huang, S.-H. 2012. Investigating the learning-theory foundations of game-based learning: a meta-analysis. Journal of Computer Assisted Learning 28, 3, 265–279.
Wu, X., Chen, J., Yu, F., Yao, M., and Luo, J. 2019. Joint learning of multiple latent domains and deep representations for domain adaptation. IEEE transactions on cybernetics 51, 5, 2676–2687.
Yang, C. C., Chen, I. Y., and Ogata, H. 2021. Toward precision education. Educational Technology & Society 24, 1, 152–163.
Yang, D., Zargar, E., Adams, A. M., Day, S. L., and Connor, C. M. 2021. Using interactive e-book user log variables to track reading processes and predict digital learning outcomes. Assessment for Effective Intervention 46, 4, 292–303.
Yang, K.-H. 2017. Learning behavior and achievement analysis of a digital game-based learning approach integrating mastery learning theory and different feedback models. Interactive Learning Environments 25, 2, 235–248.
Yang, Y.-T. C. 2012. Building virtual cities, inspiring intelligent citizens: Digital games for developing students’ problem solving and learning motivation. Computers & Education 59, 2, 365–377.
Ying, C., Qi-Guang, M., Jia-Chen, L., and Lin, G. 2013. Advance and prospects of adaboost algorithm. Acta Automatica Sinica 39, 6, 745–758.
Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., and Saeed, J. 2020. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. Journal of Applied Science and Technology Trends 1, 1, 56–70.
Zhu, J. and Ontañón, S. 2020. Player-centered ai for automatic game personalization: Open problems. In Proceedings of the 15th International Conference on the Foundations of Digital Games, G. N. Yannakakis, A. Liapis, P. Kyburz, V. Volz, F. Khosmood, and P. Lopes, Eds. Association for Computing Machinery, New York, NY, United States, Online, 1–8.
Abdi, H. and Williams, L. J. 2010. Principal component analysis. Wiley interdisciplinary reviews: computational statistics 2, 4, 433–459.
Afyouni, I., Murad, A., and Einea, A. 2020. Adaptive rehabilitation bots in serious games. Sensors 20, 24, 7037.
Akpan, B. 2020. Classical and operant conditioning—ivan pavlov; burrhus skinner. In Science education in theory and practice: an introductory guide to learning theory, B. Akpan, Ed. Springer, Cham, 71–84.
Akram, B., Min, W., Wiebe, E., Mott, B., Boyer, K. E., and Lester, J. 2018. Improving stealth assessment in game-based learning with lstm-based analytics. In Proceedings of International Conference on Educational Data Mining, M. Y. Kristy Elizabeth Boyer, Ed. International Educational Data Mining Society, Buffalo, NY, 208–218.
Alam, A. 2021. Should robots replace teachers? mobilisation of ai and learning analytics in education. In 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3). IEEE, IEEE, Mumbai, India, 1–12.
Altmann, A., Toloşi, L., Sander, O., and Lengauer, T. 2010. Permutation importance: a corrected feature importance measure. Bioinformatics 26, 10, 1340–1347.
Aluthman, E. S. 2016. The effect of using automated essay evaluation on esl undergraduate students’ writing skill. International Journal of English Linguistics 6, 5, 54–67.
Annetta, L. A., Murray, M. R., Laird, S. G., Bohr, S. C., and Park, J. C. 2006. Serious games: Incorporating video games in the classroom. Educause quarterly 29, 3, 16–22.
Apley, D. W. and Zhu, J. 2020. Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society Series B: Statistical Methodology 82, 4, 1059–1086.
Assiri, A. S., Nazir, S., and Velastin, S. A. 2020. Breast tumor classification using an ensemble machine learning method. Journal of Imaging 6, 6, 39.
Ayesha, S., Hanif, M. K., and Talib, R. 2020. Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion 59, 44–58.
Bada, S. O. and Olusegun, S. 2015. Constructivism learning theory: A paradigm for teaching and learning. Journal of Research & Method in Education 5, 6, 66–70.
Bahrami, M. R., Bahrami, B., Behboodi, F., and Pourrafie, S. 2023. Teaching the future: The vision of ai/chatgpt in education. In Agents and Multi-Agent Systems: Technologies and Applications, G. Jezic, J. Chen-Burger, R. S. M. Kusek, R. J. Howlett, and L. C. Jain, Eds. Vol. 1. Springer, Singapore, Rome, Italy, 393–402.
Barab, S. A., Gresalfi, M., and Ingram-Goble, A. 2010. Transformational play: Using games to position person, content, and context. Educational researcher 39, 7, 525–536.
Bartholomew, D. J., Knott, M., and Moustaki, I. 2011. Latent variable models and factor analysis: A unified approach. John Wiley & Sons, Online.
Bauer, G. R., Mahendran, M., Walwyn, C., and Shokoohi, M. 2022. Latent variable and clustering methods in intersectionality research: systematic review of methods applications. Social psychiatry and psychiatric epidemiology 57, 1–17.
Belland, B. R., Walker, A. E., and Kim, N. J. 2017. A bayesian network meta-analysis to synthesize the influence of contexts of scaffolding use on cognitive outcomes in stem education. Review of educational research 87, 6, 1042–1081.
Bellotti, F., Kapralos, B., Lee, K., Moreno-Ger, P., and Berta, R. 2013. Assessment in and of serious games: An overview. In Advances in Human-Computer Interaction, A. B. Barreto, Ed. Vol. 2013. Hindawi Limited London, UK, United Kingdom, London, UK, United Kingdom, 1–11.
Biedermann, D., Ciordas-Hertel, G.-P., Winter, M., Mordel, J., and Drachsler, H. 2023. Contextualized logging of on-task and off-task behaviours during learning. Journal of Learning Analytics 10, 2, 115–125.
Bird, K. A., Castleman, B. L., Mabel, Z., and Song, Y. 2021. Bringing transparency to predictive analytics: A systematic comparison of predictive modeling methods in higher education. AERA Open 7, 23328584211037630.
Blikstein, P. and Worsley, M. 2016. Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics 3, 2, 220–238.
Block, J. H. and Burns, R. B. 1976. Mastery learning. Review of research in education 4, 3–49.
Botelho, A. F., Baker, R. S., and Heffernan, N. T. 2019. Machine-learned or expert-engineered features? exploring feature engineering methods in detectors of student behavior and affect. In Proceedings of the Twelfth International Conference on Educational Data Mining, C. Lynch and A. Merceron, Eds. International Educational Data Mining Society., Montréal, Canada, 1–4.
Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., Lim, T., Ninaus, M., Ribeiro, C., and Pereira, J. 2016. An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education 94, 178–192.
Braad, E., Žavcer, G., and Sandovar, A. 2016. Processes and models for serious game design and development. In Entertainment Computing and Serious Games: International GI-Dagstuhl Seminar 15283, Dagstuhl Castle, Germany, July 5-10, 2015, Revised Selected Papers, R. Dörner, S. Göbel, M. Kickmeier-Rust, M. Masuch, and K. Zweig, Eds. Springer, Springer Cham, Online, 92–118.
Breiman, L. 2001. Random forests. Machine learning 45, 5–32.
Caballero-Hernández, J. A., Palomo-Duarte, M., Dodero, J. M., and Gaševic, D. 2024. Supporting skill assessment in learning experiences based on serious games through process mining techniques. In International Journal of Interactive Multimedia and Artificial Intelligence, D. Verdú, Ed. Vol. 8. Universidad Internacional de La Rioja (UNIR) in Spain, Spain, 146–159.
Calixto, I., Rios, M., and Aziz, W. 2019. Latent variable model for multi-modal translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, A. Korhonen, D. Traum, and L. Màrquez, Eds. Association for Computational Linguistics, Florence, Italy, 6392–6405.
Carbonaro, M., Cutumisu, M., Duff, H., Gillis, S., Onuczko, C., Schaeffer, J., Schumacher, A., Siegel, J., Szafron, D., and Waugh, K. 2006. Adapting a commercial role-playing game for educational computer game production. In GameOn North America Conference. Vol. 13. Institute for Operations Research and the Management Sciences (INFORMS), Monterey, CA, USA, 1–8.
Carvalho, M. B., Bellotti, F., Berta, R., De Gloria, A., Sedano, C. I., Hauge, J. B., Hu, J., and Rauterberg, M. 2015. An activity theory-based model for serious games analysis and conceptual design. Computers & education 87, 166–181.
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., and Lopez, A. 2020. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 408, 189–215.
Chaiklin, S. et al. 2003. The zone of proximal development in Vygotsky’s analysis of learning and instruction. Vygotsky’s educational theory in cultural context 1, 2, 39–64.
Champion, C. and Elkan, C. 2017. Visualizing the consequences of evidence in bayesian networks. arXiv preprint arXiv:1707.00791 1, 1, 1–9.
Chen, T. and Guestrin, C. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, B. Krishnapuram, M. Shah, A. Smola, C. Aggarwal, D. Shen, and R. Rastogi, Eds. Association for Computing Machinery, New York, NY, United States, San Francisco, USA, 785–794.
Chopade, P., Edwards, D., Khan, S. M., Andrade, A., and Pu, S. 2019. Cpsx: using ai-machine learning for mapping human-human interaction and measurement of cps teamwork skills. In 2019 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, IEEE, Arlington, VA, USA, 1–6.
Christensen, D. S., Jakobsen, M., and Kraus, M. 2018. The engagement effect of players’ agency over their characters’ motivation. In Interactivity, Game Creation, Design, Learning, and Innovation: 6th International Conference, ArtsIT 2017, and Second International Conference, DLI 2017, Heraklion, Crete, Greece, October 30–31, 2017, Proceedings 6, N. V. Anthony L. Brooks, Eva Brooks, Ed. Springer, Springer, Crete, Greece, 184–193.
Ciolacu, M., Tehrani, A. F., Binder, L., and Svasta, P. M. 2018. Education 4.0-artificial intelligence assisted higher education: early recognition system with machine learning to support students’ success. In 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging(SIITME). IEEE, IEEE, Online, 23–30.
Clark, D. B., Tanner-Smith, E. E., and Killingsworth, S. S. 2016. Digital games, design, and learning: A systematic review and meta-analysis. Review of educational research 86, 1, 79–122.
Conati, C., Porayska-Pomsta, K., and Mavrikis, M. 2018. Ai in education needs interpretable machine learning: Lessons from open learner modelling. In ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden, A. W. Been Kim, Kush R. Varshney, Ed. arXiv, Stockholm, Sweden, 21–27.
Covitt, B. A., Gunckel, K. L., and Anderson, C. W. 2009. Students’ developing understanding of water in environmental systems. The Journal of Environmental Education 40, 3, 37–51.
Darvenkumar, T. and Devi, V. A. 2022. A review on the impact of using e-games for social development and language acquisition in the english classroom. ECS Transactions 107, 1, 15713.
Dewey, J. 1974. John Dewey on education: Selected writings. University of Chicago, Chicago, Illinois, USA.
Dickey, M. D. 2011. Murder on grimm isle: The impact of game narrative design in an educational game-based learning environment. British Journal of Educational Technology 42, 3, 456–469.
Divjak, B. and Tomić, D. 2011. The impact of game-based learning on the achievement of learning goals and motivation for learning mathematics-literature review. Journal of information and organizational sciences 35, 1, 15–30.
Doleck, T., Lemay, D. J., Basnet, R. B., and Bazelais, P. 2020. Predictive analytics in education: a comparison of deep learning frameworks. Education and Information Technologies 25, 1951–1963.
Drey, T., Jansen, P., Fischbach, F., Frommel, J., and Rukzio, E. 2020. Towards progress assessment for adaptive hints in educational virtual reality games. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, S. Barbosa, C. Lampe, C. Appert, D. A. Shamma, S. Drucker, J. Williamson, and K. Yatani, Eds. Association for Computing Machinery, New York, NY, United States, Honolulu, HI, USA, 1–9.
Dyulicheva, Y. Y., Kosova, Y. A., and Uchitel, A. D. 2020. The augmented reality portal and hints usage for assisting individuals with autism spectrum disorder, anxiety and cognitive disorders. In Proceedings of the 3rd International Workshop on Augmented Reality in Education, A. E. K. Oleksandr Yu. Burov, Ed. CEUR Workshop Proceedings, CEUR, Kryvyi Rih, Ukraine, 251–262.
Eliseyev, A. and Aksenova, T. 2019. Personalized adaptive instruction design (paid) for brain–computer interface using reinforcement learning and deep learning: simulated data study. Brain-Computer Interfaces 6, 1-2, 36–48.
Eseryel, D., Ge, X., Ifenthaler, D., and Law, V. 2011. Dynamic modeling as a cognitive regulation scaffold for developing complex problem-solving skills in an educational massively multiplayer online game environment. Journal of Educational Computing Research 45, 3, 265–286.
Eseryel, D., Law, V., Ifenthaler, D., Ge, X., and Miller, R. 2014. An investigation of the interrelationships between motivation, engagement, and complex problem solving in game-based learning. Journal of Educational Technology & Society 17, 1, 42–53.
Estevez, J., Garate, G., Guede, J., and Graña, M. 2019. Using scratch to teach undergraduate students’ skills on artificial intelligence. arXiv preprint arXiv:1904.00296. 1, 1, 1–6.
Frommel, J., Rogers, K., Brich, J., Besserer, D., Bradatsch, L., Ortinau, I., Schabenberger, R., Riemer, V., Schrader, C., and Weber, M. 2015. Integrated questionnaires: Maintaining presence in game environments for self-reported data acquisition. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, A. L. Cox, P. Cairns, R. Bernhaupt, and L. Nacke, Eds. Association for Computing Machinery, New York, NY, United States, Seattle, Washington, USA, 359–368.
Gaikwad, P. 2022. Sustainable learning: Implications from the law of the minimum. Spicer Adventist University Research Articles Journal 1, 1, 18–25.
Gee, J. P. 2003. What video games have to teach us about learning and literacy. Computers in entertainment (CIE) 1, 1, 20–20.
Georgiadis, K., van Lankveld, G., Bahreini, K., and Westera, W. 2019. Learning analytics should analyse the learning: proposing a generic stealth assessment tool. In 2019 IEEE Conference on Games (CoG). IEEE, IEEE, London, United Kingdom, 1–8.
Georgiadis, K., van Lankveld, G., Bahreini, K., and Westera, W. 2020. On the robustness of stealth assessment. IEEE Transactions on Games 13, 2, 180–192.
Göbel, S., de Carvalho Rodrigues, A., Mehm, F., and Steinmetz, R. 2009. Narrative game-based learning objects for story-based digital educational games. narrative 14, 16.
Göbel, S. and Mehm, F. 2013. Personalized, adaptive digital educational games using narrative game-based learning objects. In Serious Games and Virtual Worlds in Education, Professional Development, and Healthcare, W. B. Klaus Bredl, Ed. Informaiton Science Reference, Online, 74–84.
Grey, S., Grey, D., Gordon, N., and Purdy, J. 2017. Using formal game design methods to embed learning outcomes into game mechanics and avoid emergent behaviour. International journal of game-based learning (IJGBL) 7, 3, 63–73.
Gris, G. and Bengtson, C. 2021. Assessment measures in game-based learning research: A systematic review. International Journal of Serious Games 8, 1, 3–26.
Gunckel, K. L., Covitt, B. A., and Anderson, C. W. 2009. Learning a secondary discourse: shifts from force-dynamic to model-based reasoning in understanding water in socioecological systems. In Proceedings of Learning Progressions in Science (LeaPS) Conference, Iowa City, IA, A. W. G. Alicia C. Alonzo, Ed. LeaPS Learning Progressions in Science, Iowa City, IA, USA, 1–19.
Gupta, A., Carpenter, D., Min, W., Rowe, J. P., Azevedo, R., and Lester, J. C. 2021. Multimodal multi-task stealth assessment for reflection-enriched game-based learning. In Proceedings of the First International Workshop on Multimodal Artificial Intelligence in Education (MAIED 2021) At the 22nd International Conference on Artificial Intelligence in Education (AIED 2021), Online, June 14th, 2021, D. D. Mitri, R. M. Maldonado, O. C. Santos, J. Schneider, K. A. M. Sanusi, M. Cukurova, D. Spikol, I. Molenaar, M. N. Giannakos, R. Klemke, and R. Azevedo, Eds. CEUR Workshop Proceedings, vol. 2902. CEUR-WS.org, Online, 93–102.
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., and Hussain, A. 2024. Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation 16, 1, 45–74.
Heine, C. 2020. Towards modeling visualization processes as dynamic bayesian networks. IEEE Transactions on Visualization and Computer Graphics 27, 2, 1000–1010.
Henderson, N., Acosta, H., Min, W., Mott, B., Lord, T., Reichsman, F., Dorsey, C., Wiebe, E., and Lester, J. 2022. Enhancing stealth assessment in game-based learning environments with generative zero-shot learning. In Proceedings of the 15th International Conference on Educational Data Mining, A. Mitrovic and N. Bosch, Eds. International Educational Data Mining Society, Durham, United Kingdom, 171–182.
Henderson, N., Kumaran, V., Min, W., Mott, B., Wu, Z., Boulden, D., Lord, T., Reichsman, F., Dorsey, C., Wiebe, E., et al. 2020. Enhancing student competency models for game-based learning with a hybrid stealth assessment framework. In Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020), A. Rafferty and J. R. Whitehill, Eds. International Educational Data Mining Society, Online, 92–103.
Hooshyar, D., Ahmad, R. B., Yousefi, M., Fathi, M., Horng, S.-J., and Lim, H. 2016. Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills. Computers & Education 94, 18–36.
Hooshyar, D., Malva, L., Yang, Y., Pedaste, M., Wang, M., and Lim, H. 2021. An adaptive educational computer game: Effects on students’ knowledge and learning attitude in computational thinking. Computers in Human Behavior 114, 106575.
Hosmer Jr, D. W., Lemeshow, S., and Sturdivant, R. X. 2013. Applied logistic regression. John Wiley & Sons, Online.
Huang, W.-H. 2011. Evaluating learners’ motivational and cognitive processing in an online game-based learning environment. Computers in Human Behavior 27, 2, 694–704.
Huang, X., Wu, L., and Ye, Y. 2019. A review on dimensionality reduction techniques. International Journal of Pattern Recognition and Artificial Intelligence 33, 10, 1950017.
Hyvärinen, A. and Oja, E. 2000. Independent component analysis: algorithms and applications. Neural networks 13, 4-5, 411–430.
Ifenthaler, D. 2014. Toward automated computer-based visualization and assessment of team-based performance. Journal of Educational Psychology 106, 3, 651.
Ifenthaler, D., Eseryel, D., and Ge, X. 2012. Assessment for game-based learning. Springer, Online.
Ishak, S. A., Hasran, U. A., and Din, R. 2023. Media education through digital games: a review on design and factors influencing learning performance. Education Sciences 13, 2, 102.
Jackson, L. C., O’Mara, J., Moss, J., and Jackson, A. C. 2018. A critical review of the effectiveness of narrative-driven digital educational games. International Journal of Game-Based Learning (IJGBL) 8, 4, 32–49.
Johnson, C. I., Bailey, S. K., and Van Buskirk, W. L. 2017. Designing effective feedback messages in serious games and simulations: A research review. In Instructional Techniques to Facilitate Learning and Motivation of Serious Games, P. Wouters and H. van Oostendorp, Eds. Springer Cham, Switzerland, 119–140.
Kamel, H., Abdulah, D., and Al-Tuwaijari, J. M. 2019. Cancer classification using gaussian naive bayes algorithm. In 2019 International Engineering Conference (IEC). IEEE, IEEE, Erbil, Iraq, 165–170.
Kanal, V., Brady, J., Nambiappan, H., Kyrarini, M., Wylie, G., and Makedon, F. 2020. Towards a serious game based human-robot framework for fatigue assessment. In Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, F. Makedon, Ed. Association for Computing Machinery, New York, NY, United States, Online, 1–6.
Khenissi, M. A., Essalmi, F., and Jemni, M. 2013. Toward the personalization of learning games according to learning styles. In 2013 International Conference on Electrical Engineering and Software Applications. IEEE, IEEE, Hammamet, Tunisia, 1–6.
Kim, Y. and Rosenheck, L. 2020. Reimagining assessment through play: A case study of metarubric. In Re-imagining University Assessment in a Digital World, M. Bearman, P. Dawson, R. Ajjawi, J. Tai, and D. Boud, Eds. Vol. 7. Springer, Cham, Switzerland, 263–288.
Kim, Y. J. and Ifenthaler, D. 2019. Game-based assessment: The past ten years and moving forward. In Game-Based Assessment Revisited, D. Ifenthaler and Y. J. Kim, Eds. Springer, Cham, Switzerland, 3–11.
Kirsch, I., Lynn, S. J., Vigorito, M., and Miller, R. R. 2004. The role of cognition in classical and operant conditioning. Journal of clinical psychology 60, 4, 369–392.
Klema, V. and Laub, A. 1980. The singular value decomposition: Its computation and some applications. IEEE Transactions on automatic control 25, 2, 164–176.
Kuznetsov, V. S., Moiseienko, M. V., Moiseienko, N. V., Rostalny, B. A., and Kiv, A. E. 2020. Using unity to teach game development. In Proceedings of the 1st Symposium on Advances in Educational Technology (AET 2020). Vol. 2. Science and Technology Publications, Lda, Online, 506–515.
Laffey, J., Griffin, J., Sigoloff, J., Sadler, T., Goggins, S., Womack, A., Wulff, E., and Lander, S. 2019. Mission hydrosci: Meeting learning standards through gameplay. In Proceedings of The International Conference on Computer-Supported Collaborative Learning (CSCL), G. N. Kristine Lund, Ed. International Society of the Learning Sciences (ISLS), Lyon, France, 1017–1020.
Laffey, J. M., Sadler, T. D., Goggins, S. P., Griffin, J., and Babiuch, R. N. 2019. Mission hydrosci: Distance learning through game-based 3d virtual learning environments. In Virtual Reality in education: Breakthroughs in research and practice, I. R. M. A. (IRMA), Ed. IGI Global, Lyon, France, 623–643.
Lee, D. D. and Seung, H. S. 1999. Learning the parts of objects by non-negative matrix factorization. nature 401, 6755, 788–791.
Leonardou, A., Rigou, M., and Garofalakis, J. 2020. Techniques to motivate learner improvement in game-based assessment. information 11, 4, 176.
Lin, H.-C. K., Lin, Y.-H., Wang, T.-H., Su, L.-K., and Huang, Y.-M. 2021. Effects of incorporating augmented reality into a board game for high school students’ learning motivation and acceptance in health education. Sustainability 13, 6, 3333.
Lin, P.-H. and Chen, S.-Y. 2020. Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access 8, 45689–45699.
Liu, T. and Israel, M. 2022. Uncovering students’ problem-solving processes in game-based learning environments. Computers & Education 182, 104462.
Loh, C. S., Sheng, Y., and Ifenthaler, D. 2015. Serious games analytics: Theoretical framework. In Serious games analytics: Methodologies for performance measurement, assessment, and improvement, D. I. Christian Sebastian Loh, Yanyan Sheng, Ed. Springer Cham, Switzerland, 3–29.
López, C. and Tucker, C. 2018. Toward personalized adaptive gamification: a machine learning model for predicting performance. IEEE transactions on Games 12, 2, 155–168.
Lu, W., Griffin, J., Sadler, T. D., Laffey, J., and Goggins, S. P. 2023. Serious game analytics by design: Feature generation and selection using game telemetry and game metrics–toward predictive model construction. Journal of Learning Analytics 10, 1, 168–188.
Lu, Z., Chiu, M. M., Cui, Y., Mao, W., and Lei, H. 2023. Effects of game-based learning on students’ computational thinking: A meta-analysis. Journal of Educational Computing Research 61, 1, 235–256.
Luckin, R. and Cukurova, M. 2019. Designing educational technologies in the age of ai: A learning sciences-driven approach. British Journal of Educational Technology 50, 6, 2824–2838.
Lundberg, S. and Lee, S.-I. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, and R. Fergus, Eds. Curran Associates Inc. 57 Morehouse LaneRed Hook, NY, United States, Long Beach, California, USA, 1–10.
Martinez-Garza, M. M. and Clark, D. B. 2017. Investigating epistemic stances in game play with data mining. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS) 9, 3, 1–40.
McGaghie, W. C. and Harris, I. B. 2018. Learning theory foundations of simulation-based mastery learning. Simulation in Healthcare 13, 3S, S15–S20.
Miladinovic, I., Schefer-Wenzl, S., and Bajic-Kerndl, K. 2023. Impact of a mobile serious game on learning outcomes for complex problem solving skills. In 2023 IEEE Global Engineering Education Conference (EDUCON). IEEE, IEEE, Salmiya, Kuwait, 1–7.
Min, W., Frankosky, M. H., Mott, B. W., Rowe, J. P., Smith, A., Wiebe, E., Boyer, K. E., and Lester, J. C. 2019. Deepstealth: Game-based learning stealth assessment with deep neural networks. IEEE Transactions on Learning Technologies 13, 2, 312–325.
Mislevy, R. J., Almond, R. G., and Lukas, J. F. 2003. A brief introduction to evidence-centered design. ETS Research Report Series 2003, 1, i–29.
Moore, G. R. and Shute, V. J. 2017. Improving learning through stealth assessment of conscientiousness. In Handbook on digital learning for K-12 schools, T. H. Ann Marcus-Quinn, Ed. Springer, Online, 355–368.
Mouri, K., Okubo, F., Shimada, A., and Ogata, H. 2016. Bayesian network for predicting students’ final grade using e-book logs in university education. In 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT). IEEE, IEEE, University of Macau, Macao, China, 85–89.
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., and Ghazi Saeedi, M. 2021. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments 29, 1, 142–163.
Mulwa, C., Lawless, S., Sharp, M., Arnedillo-Sanchez, I., and Wade, V. 2010. Adaptive educational hypermedia systems in technology enhanced learning: a literature review. In Proceedings of the 2010 ACM Conference on Information Technology Education, M. Stinson, N. Cregger, K. Baker, and R. Friedman, Eds. Association for Computing Machinery, New York, NY, United States, University of Maryland, College Park, Maryland, USA, 73–84.
Natekin, A. and Knoll, A. 2013. Gradient boosting machines, a tutorial. Frontiers in neurorobotics 7, 21.
National Research Council. 2013. Monitoring progress toward successful K-12 STEM education: A nation advancing? National Academies Press.
Naul, E. and Liu, M. 2020. Why story matters: A review of narrative in serious games. Journal of Educational Computing Research 58, 3, 687–707.
Nguyen, H. A., Hou, X., Stamper, J., and McLaren, B. M. 2020. Moving beyond test scores: Analyzing the effectiveness of a digital learning game through learning analytics. In Proceedings of the Thirteenth International Conference on Educational Data Mining (EDM 2020), A. Rafferty and J. R. Whitehill, Eds. International Educational Data Mining Society, Online, 487–495.
Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E. 2015. A review of relational machine learning for knowledge graphs. Proceedings of the IEEE 104, 1, 11–33.
Nie, H., Xiao, H. M., and Shang, J. J. 2014. A critical analysis of the studies on fostering creativity through game-based learning. In Hybrid Learning. Theory and Practice: 7th International Conference, ICHL 2014, Shanghai, China, August 8-10, 2014. Proceedings 7, S. K. S. Cheung, J. Fong, L. F. Kwok, J. Zhang, and R. Kwan, Eds. Springer, Cham, Shanghai, China, 278–287.
Nieland, T., Fehrenbach, A., Marowsky, M., and Burfeind, M. 2021. The teacher-centered perspective on digital game-based learning: Quantitative and qualitative evaluation methods from diverse disciplines. In Game-Based Learning across the Disciplines, D. I. Carmela Aprea, Ed. Springer, Switzerland, 341–362.
Oren, M., Pedersen, S., and Butler-Purry, K. L. 2020. Teaching digital circuit design with a 3-d video game: The impact of using in-game tools on students’ performance. IEEE Transactions on Education 64, 1, 24–31.
Osborne, S. P., Radnor, Z., and Nasi, G. 2013. A new theory for public service management? toward a (public) service-dominant approach. The American Review of Public Administration 43, 2, 135–158.
Peng, T., Luo, Y., and Liu, Y. 2022. Ai-based equipment optimization of the design on intelligent education curriculum system. In Wireless Communications and Mobile Computing, S. Rani, Ed. Vol. 2022. Hindawi, Baffins Lane, Chichester, West Sussex PO19 1UD, United Kingdom, 1–13.
Peterson, L. E. 2009. K-nearest neighbor. Scholarpedia 4, 2, 1883.
Pivec, M., Dziabenko, O., and Schinnerl, I. 2003. Aspects of game-based learning. In Proceedings of the 3rd International Conference on Knowledge Management, Graz, Austria, K. T. Hermann Maurer, Ed. Vol. 304. Springer Verlag Heidelberg, Graz, Austria, 216–225.
Plass, J. L., Homer, B. D., MacNamara, A., Ober, T., Rose, M. C., Pawar, S., Hovey, C. M., and Olsen, A. 2020. Emotional design for digital games for learning: The effect of expression, color, shape, and dimensionality on the affective quality of game characters. Learning and instruction 70, 101194.
Prensky, M. 2001. Digital natives, digital immigrants part 2: Do they really think differently? On the Horizon 1, 1, 1–9.
Rahimi, S., Shute, V., Kuba, R., Dai, C.-P., Yang, X., Smith, G., and Fernández, C. A. 2021. The use and effects of incentive systems on learning and performance in educational games. Computers & Education 165, 104135.
Reeves, T., Romine, W., Laffey, J., Sadler, T., and Goggins, S. 2020. Distance learning through game-based 3d virtual learning environments: Mission hydro science. evaluation report for mission hydrosci. Grantee Submission 1, 1, 1–31.
Romero, M., Usart, M., and Ott, M. 2015. Can serious games contribute to developing and sustaining 21st century skills? Games and culture 10, 2, 148–177.
Rosé, C. P., McLaughlin, E. A., Liu, R., and Koedinger, K. R. 2019. Explanatory learner models: Why machine learning (alone) is not the answer. British Journal of Educational Technology 50, 6, 2943–2958.
Rowe, E., Asbell-Clarke, J., and Baker, R. S. 2015. Serious games analytics to measure implicit science learning. In Serious games analytics: Methodologies for performance measurement, assessment, and improvement, C. S. Loh, Y. Sheng, and D. Ifenthaler, Eds. Springer, Switzerland, 343–360.
Rowe, E., Asbell-Clarke, J., Baker, R. S., Eagle, M., Hicks, A. G., Barnes, T. M., Brown, R. A., and Edwards, T. 2017. Assessing implicit science learning in digital games. Computers in Human Behavior 76, 617–630.
Rowe, J. P., Shores, L. R., Mott, B. W., and Lester, J. C. 2010. Individual differences in gameplay and learning: a narrative-centered learning perspective. In Proceedings of the Fifth International Conference on the Foundations of Digital Games, Y. P. Ian Horswill, Ed. Association for Computing Machinery, New York, NY, United States, Worcester Polytechnic Institute (WPI) in Worcester, Massachusetts, USA, 171–178.
Sadler, T. D., Nguyen, H., and Lankford, D. 2017. Water systems understandings: a framework for designing instruction and considering what learners know about water. Wiley Interdisciplinary Reviews: Water 4, 1, e1178.
Sakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutaporn, P., Surareungchai, W., Pataranutaporn, P., and Subsoontorn, P. 2018. Kids making ai: Integrating machine learning, gamification, and social context in stem education. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). IEEE, IEEE, Wollongong, Australia, 1005–1010.
Saleh, A., Chen, Y., Hmelo-Silver, C., Glazewski, K., Mott, B., Taylor, R., Rowe, J., and Lester, J. 2019. Supporting collaborative problem solving in a game-based learning environment. In The Proceedings of the International Conference on Computer-Supported Collaborative Learning (CSCL). International Society of the Learning Sciences (ISLS), Lyon, France, 1029–1032.
Sanchez, D. R. and Lee, C. A. 2022. Understanding the challenges of game-based training: Recommendations for moving research forward in game-based learning. In Handbook of Research on the Influence and Effectiveness of Gamification in Education, A. C. Moreira, O. Bernardes, and V. Amorim, Eds. IGI Global, Online, 541–578.
Sasada, T., Liu, Z., Baba, T., Hatano, K., and Kimura, Y. 2020. A resampling method for imbalanced datasets considering noise and overlap. Procedia Computer Science 176, 420–429.
Schölkopf, B., Smola, A., and Müller, K.-R. 1997. Kernel principal component analysis. In Proceedings of the International Conference on Artificial Neural Networks, W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, Eds. Springer, Alghero, Sardinia, Italy, 583–588.
Serhan, B., Said, B., Cheniti, L., and El Khayat, G. 2019. Personalization in serious games for assessment. In The 12th Annual International Conference of Education, Research and Innovation(ICERI2019) Proceedings. IATED, IATED, Seville, Spain, 4845–4852.
Serrano-Laguna, Á., Martínez-Ortiz, I., Haag, J., Regan, D., Johnson, A., and Fernández-Manjón, B. 2017. Applying standards to systematize learning analytics in serious games. Computer Standards & Interfaces 50, 116–123.
Shaffer, D. W. 2006. Epistemic frames for epistemic games. Computers & education 46, 3, 223–234.
Sharma, K., Papamitsiou, Z., and Giannakos, M. 2019. Building pipelines for educational data using ai and multimodal analytics: A “grey-box” approach. British Journal of Educational Technology 50, 6, 3004–3031.
Shute, V., Ke, F., and Wang, L. 2017. Assessment and adaptation in games. In Instructional Techniques to Facilitate Learning and Motivation of Serious Games, H. v. O. Pieter Wouters, Ed. Springer Cham, Switzerland, 59–78.
Shute, V., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C.-P., Kuba, R., Liu, Z., Yang, X., and Sun, C. 2021. Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. Journal of Computer Assisted Learning 37, 1, 127–141.
Shute, V. and Ventura, M. 2013. Stealth assessment: Measuring and supporting learning in video games. The MIT Press, Online.
Shute, V. J. 2011. Stealth assessment in computer-based games to support learning. Computer games and instruction 55, 2, 503–524.
Shute, V. J. and Kim, Y. J. 2014. Formative and stealth assessment. In Handbook of Research on Educational Communications and Technology, J. M. Spector, M. D. Merrill, J. Elen, and M. J. Bishop, Eds. Springer New York, NY, New York, NY, 311–321.
Shute, V. J. and Rahimi, S. 2021. Stealth assessment of creativity in a physics video game. Computers in Human Behavior 116, 106647.
Shute, V. J., Ventura, M., Bauer, M., and Zapata-Rivera, D. 2009. Melding the power of serious games and embedded assessment to monitor and foster learning. Serious games: Mechanisms and effects 2, 295–321.
Shute, V. J., Wang, L., Greiff, S., Zhao, W., and Moore, G. 2016. Measuring problem solving skills via stealth assessment in an engaging video game. Computers in Human Behavior 63, 106–117.
Slavin, R. E. 1987. Mastery learning reconsidered. Review of educational research 57, 2, 175–213.
Smith, G., Shute, V., and Muenzenberger, A. 2019. Designing and validating a stealth assessment for calculus competencies. Journal of Applied Testing Technology 20, S1, 52–59.
Soflano, M., Connolly, T. M., and Hainey, T. 2015. An application of adaptive games-based learning based on learning style to teach sql. Computers & Education 86, 192–211.
Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., and Postma, E. 2006. Adaptive game ai with dynamic scripting. Machine Learning 63, 217–248.
Staddon, J. E. and Cerutti, D. T. 2003. Operant conditioning. Annual review of psychology 54, 1, 115–144.
Steinemann, A. 2003. Implementing sustainable development through problem-based learning: Pedagogy and practice. Journal of Professional Issues in Engineering Education and Practice 129, 4, 216–224.
Steinmaurer, A., Sackl, M., and Gütl, C. 2021. Engagement in in-game questionnaires-perspectives from users and experts. In 2021 7th International Conference of the Immersive Learning Research Network (iLRN). IEEE, IEEE, University of Westminster, London, UK, 1–7.
Streicher, A. and Smeddinck, J. D. 2016. Personalized and adaptive serious games. In Entertainment Computing and Serious Games: International GI-Dagstuhl Seminar 15283, Dagstuhl Castle, Germany, July 5-10, 2015, Revised Selected Papers, R. Dörner, S. Göbel, M. Kickmeier-Rust, M. Masuch, and K. Zweig, Eds. Springer Cham, Springer Cham, Dagstuhl Castle, Germany, 332–377.
Sun, C.-T., Wang, D.-Y., and Chan, H.-L. 2011. How digital scaffolds in games direct problem-solving behaviors. Computers & Education 57, 3, 2118–2125.
Sunarya, P. A. 2022. Machine learning and artificial intelligence as educational games. International Transactions on Artificial Intelligence 1, 1, 129–138.
Sweller, J. 1994. Cognitive load theory, learning difficulty, and instructional design. In Learning and Instruction, Elsevier, Ed. Vol. 4. Elsevier, 295–312.
Sweller, J. 2011. Cognitive load theory. In Psychology of Learning and Motivation. Vol. 55. Elsevier, Online, 37–76.
Tadayon, M. and Pottie, G. J. 2020. Predicting student performance in an educational game using a hidden markov model. IEEE Transactions on Education 63, 4, 299–304.
Tharwat, A. 2016. Linear vs. quadratic discriminant analysis classifier: a tutorial. International Journal of Applied Pattern Recognition 3, 2, 145–180.
Tumenayu, O. O., Shabalina, O., Kamaev, V., and Davtyan, A. 2014. Using agent-based technologies to enhance learning in educational games. In Proceedings of the International Conference e-Learning 2014. Multi Conference on Computer Science and Information Systems (Lisbon, Portugal, July 15-19, 2014), M. Baptista Nunes and M. McPherson, Eds. International Association for Development of the Information Society, Lisbon, Portugal, 149–155.
Verleysen, M. and François, D. 2005. The curse of dimensionality in data mining and time series prediction. In Computational Intelligence and Bioinspired Systems. 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Proceedings, F. S. Joan Cabestany, Alberto Prieto, Ed. Springer Berlin, Heidelberg, Springer Berlin, Heidelberg, Vilanova i la Geltrú, Barcelona, Spain, 758–770.
Wadoux, A. M.-C. and Molnar, C. 2022. Beyond prediction: methods for interpreting complex models of soil variation. Geoderma 422, 115953.
Wang, A., Ramaswamy, V. V., and Russakovsky, O. 2022. Towards intersectionality in machine learning: Including more identities, handling underrepresentation, and performing evaluation. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, United States, COEX exhibition center in Seoul, South Korea, 336–349.
Wang, L.-H., Chen, B., Hwang, G.-J., Guan, J.-Q., and Wang, Y.-Q. 2022. Effects of digital game-based stem education on students’ learning achievement: a meta-analysis. International Journal of STEM Education 9, 1, 1–13.
Wang, Y., Yao, H., and Zhao, S. 2016. Auto-encoder based dimensionality reduction. Neurocomputing 184, 232–242.
Wattenberg, M., Viégas, F., and Johnson, I. 2016. How to use t-sne effectively. Distill 1, 10, e2.
Wu, W.-H., Hsiao, H.-C., Wu, P.-L., Lin, C.-H., and Huang, S.-H. 2012. Investigating the learning-theory foundations of game-based learning: a meta-analysis. Journal of Computer Assisted Learning 28, 3, 265–279.
Wu, X., Chen, J., Yu, F., Yao, M., and Luo, J. 2019. Joint learning of multiple latent domains and deep representations for domain adaptation. IEEE transactions on cybernetics 51, 5, 2676–2687.
Yang, C. C., Chen, I. Y., and Ogata, H. 2021. Toward precision education. Educational Technology & Society 24, 1, 152–163.
Yang, D., Zargar, E., Adams, A. M., Day, S. L., and Connor, C. M. 2021. Using interactive e-book user log variables to track reading processes and predict digital learning outcomes. Assessment for Effective Intervention 46, 4, 292–303.
Yang, K.-H. 2017. Learning behavior and achievement analysis of a digital game-based learning approach integrating mastery learning theory and different feedback models. Interactive Learning Environments 25, 2, 235–248.
Yang, Y.-T. C. 2012. Building virtual cities, inspiring intelligent citizens: Digital games for developing students’ problem solving and learning motivation. Computers & Education 59, 2, 365–377.
Ying, C., Qi-Guang, M., Jia-Chen, L., and Lin, G. 2013. Advance and prospects of adaboost algorithm. Acta Automatica Sinica 39, 6, 745–758.
Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., and Saeed, J. 2020. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. Journal of Applied Science and Technology Trends 1, 1, 56–70.
Zhu, J. and Ontañón, S. 2020. Player-centered ai for automatic game personalization: Open problems. In Proceedings of the 15th International Conference on the Foundations of Digital Games, G. N. Yannakakis, A. Liapis, P. Kyburz, V. Volz, F. Khosmood, and P. Lopes, Eds. Association for Computing Machinery, New York, NY, United States, Online, 1–8.
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
- Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons 4.0 License (Attribution-Noncommercial-No Derivatives 4.0 International), or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
- Noncommercial—other users (including Publisher) may not use this Work for commercial purposes;
- No Derivative Works—other users (including Publisher) may not alter, transform, or build upon this Work,with the understanding that any of the above conditions can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- the Work is the Author’s original work;
- the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- the Work is not pending review or under consideration by another publisher;
- the Work has not previously been published;
- the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- the Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.