1. Verma, V.: Content Agnostic Game Based Stealth Assessment. PhD thesis, Arizona State University (2021)

   Google Scholar 

2. Shute, V., Spector, J.M.: Scorm 2.0 white paper: stealth assessment in virtual worlds. Unpublished manuscript (2008)

   Google Scholar 

3. Kim, Y.J., Shute, V.: Opportunities and challenges in assessing and supporting creativity in video games. In: Video Games and Creativity, pp. 99–117. Elsevier, Amsterdam (2015)

   Google Scholar 

4. Shute, V.: Stealth assessment in computer-based games to support learning. Comput. Games Instruct.55(2), 503–524 (2011)

   Google Scholar 

5. Ventura, M., Shute, V., Small, M.: Assessing persistence in educational games. Des. Recommendations Adapt. Intell. Tutoring Syst. Learner Model.2(2014), 93–101 (2014)

   Google Scholar 

6. Rotherham, A.J., Willingham, D.T.: 21st-century’ skills. Am. Educ.17(1), 17–20 (2010)

   Google Scholar 

7. Mislevy, R.J., Almond, R.G., Lukas, J.F.: A brief introduction to evidence-centered design. ETS Res. Rep. Ser.2003(1), i–29 (2003)

   Google Scholar 

8. Malone, T.W., Lepper, M.R.: Making learning fun: a taxonomy of intrinsic motivations for learning. In: Aptitude, Learning, and Instruction, pp. 223–254. Routledge, Milton Park (2021)

   Google Scholar 

9. Baron, T., Heath, C., Amresh, A.: Towards a context agnostic platform for design and assessment of educational games. In: European Conference on Games Based Learning, p. 34. Academic Conferences International Limited, Reading (2016)

   Google Scholar 

10. Baron, T.: An Architecture for Designing Content Agnostic Game Mechanics for Educational Burst Games. PhD thesis, Arizona State University (2017)

    Google Scholar 

11. Shute, V.J., Kim, Y.J.: Formative and stealth assessment. In: Handbook of Research on Educational Communications and Technology, pp. 311–321. Springer, Berlin (2014)

    Google Scholar 

12. Verma, V., Baron, T., Bansal, A., Amresh, A.: Emerging practices in game-based assessment. In: Game-Based Assessment Revisited, pp. 327–346. Springer, Berlin (2019)

    Google Scholar 

13. Pardos, Z.A., Heffernan, N.T.: Modeling individualization in a bayesian networks implementation of knowledge tracing. In: International Conference on User Modeling, Adaptation, and Personalization, pp. 255–266. Springer, Berlin (2010)

    Google Scholar 

14. Chin, J., Dukes, R., Gamson, W.: Assessment in simulation and gaming: a review of the last 40 years. Simul. Gaming40(4), 553–568 (2009)

    Article  Google Scholar 

15. Shute, V., Masduki, I., Donmez, O., Dennen, V.P., Kim, Y.-J., Jeong, A.C., Wang, C.-Y.: Modeling, assessing, and supporting key competencies within game environments. In: Computer-Based Diagnostics and Systematic Analysis of Knowledge, pp. 281–309. Springer, Berlin (2010)

    Google Scholar 

16. Shute, V., Wang, L.: Measuring problem solving skills in portal 2. In: E-Learning Systems, Environments and Approaches, pp. 11–24. Springer, Berlin (2015)

    Google Scholar 

17. Mayer, I., van Dierendonck, D., Van Ruijven, T., Wenzler, I.: Stealth assessment of teams in a digital game environment. In: International Conference on Games and Learning Alliance, pp. 224–235. Springer, Berlin (2013)

    Google Scholar 

18. Shute, V., Kim, Y.J.: Does playing the world of goo facilitate learning. In: Design Research on Learning and Thinking in Educational Settings: Enhancing Intellectual Growth and Functioning, pp. 359–387 (2011)

    Google Scholar 

19. Chen, J.: Flow in games (and everything else). Commun. ACM50(4), 31–34 (2007)

    Article  Google Scholar 

20. Crisp, G.T., Assessment in next generation learning spaces. In: The Future of Learning and Teaching in Next Generation Learning Spaces. Emerald Group Publishing Limited, Bingley (2014)

    Book  Google Scholar 

21. Shute, V., Ventura, M., Small, M., Goldberg, B.: Modeling student competencies in video games using stealth assessment. Des. Recommendations Intell. Tutoring Syst.1, 141–152 (2013)

    Google Scholar 

22. Papesh, M.H., Goldinger, S.D.: Memory in motion: movement dynamics reveal memory strength. Psychonomic Bull. Rev.19(5), 906–913 (2012)

    Article  Google Scholar 

23. Yamauchi, T., Xiao, K.: Reading emotion from mouse cursor motions: affective computing approach. Cognit. Sci.42(3), 771–819 (2018)

    Article  Google Scholar 

24. Freeman, J.B., Ambady, N.: Motions of the hand expose the partial and parallel activation of stereotypes. Psychol. Sci.20(10), 1183–1188 (2009)

    Article  Google Scholar 

25. Rheem, H., Verma, V., Becker, D.V.: Use of mouse-tracking method to measure cognitive load. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 62, pp. 1982–1986. SAGE Publications Sage CA, Los Angeles (2018)

    Google Scholar 

26. Faulkenberry, T.J., Testing a direct mapping versus competition account of response dynamics in number comparison. J. Cognit. Psychol.28(7), 825–842 (2016)

    Article  Google Scholar 

27. Element: mousemove event (2022)

    Google Scholar 

28. Mouseevent.pagex (2022)

    Google Scholar 

29. Unity3d (2022)

    Google Scholar 

30. Unity3d: Input.mouseposition (2022)

    Google Scholar 

31. Unreal engine: get mouse position (2022)

    Google Scholar 

32. Visage Technologies (2022)

    Google Scholar 

33. Affectiva (2022)

    Google Scholar 

34. Ekman, P., Friesen, W.V.: Facial Action Coding System: A Technique for the Measurement of Facial Movement, Consulting Psychologists Press. Palo Alto, Santa Clara (1978)

    Google Scholar 

35. iMotions Inc. Affectiva channel explained (2018).https://help.imotions.com/hc/en-us/articles/360011728719-Affectiva-channel-explained. Accessed 07 Aug 2022

36. Verma, V., Rheem, H., Amresh, A., Craig, S.D., Bansal, A.: Predicting real-time affective states by modeling facial emotions captured during educational video game play. In: International Conference on Games and Learning Alliance, pp. 447–452. Springer, Berlin (2020)

    Google Scholar 

37. BayesServer. Dynamic bayesian networks – an introduction (2022)

    Google Scholar 

38. Verma, V., Amresh, A., Craig, S.D., Bansal, A.: Validity of a content agnostic game based stealth assessment. In: International Conference on Games and Learning Alliance, pp. 121–130. Springer, Berlin (2021)

    Google Scholar 

39. BayesServer. Dynamic bayesian networksc# api in bayes server (2022)

    Google Scholar 

40. Scheuer, O., McLaren, B.M.: Educational data mining. In: Encyclopedia of the Sciences of Learning, pp. 1075–1079 (2012)

    Google Scholar 

41. Baker, R.S.J.D., Gowda, S., Wixon, M., Kalka, J., Wagner, A., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., Rossi, L.: Sensor-free automated detection of affect in a cognitive tutor for algebra. In: Educational Data Mining 2012 (2012)

    Google Scholar 

42. D’Mello, S.K., Graesser, A.: Mining bodily patterns of affective experience during learning. In: Educational data mining 2010 (2010)

    Google Scholar 

43. Mislevy, R.J., Oranje, A., Bauer, M.I., von Davier, A.A., Hao, J.: Psychometric Considerations in Game-Based Assessment. GlassLabGames (2014)

    Google Scholar 

44. Shaffer, D.W., Squire, K.R., Halverson, R., Gee, J.P.: Video games and the future of learning. Phi delta kappan87(2), 105–111 (2005)

    Article  Google Scholar 

45. Typing of the dead, the description (2022)

    Google Scholar 

46. Verma, V., Craig, S.D., Levy, R., Bansal, A., Amresh, A.: Domain knowledge and adaptive serious games: exploring the relationship of learner ability and affect adaptability. J. Educ. Comput. Res.60(2), 406–432 (2022)

    Article  Google Scholar 

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