Abstract
The unfolding empowerment of instructors as game designers with approachable and widely available tools such as Scratch, Minecraft, or Unreal Engine shifted the perspective on game-based assessment (GBA). An increasing number of instructors are capable of creating games themselves, subsequently gaining access to the mechanics and the embedded data. Thus, detailed information regarding each individual player becomes accessible. This gains importance, as this approach might amplify the availability of desperately needed process data within the fields of instructional psychology and game-based learning research. However, this approach is still in its infancy, and future users and researchers need guidance regarding the gathering and the interpretation of insights created by complex process data within GBA. This becomes particularly important with the increasing use of physiological data in learning as well as assessment scenarios. The ubiquitous availability of sensors acquiring physiological data allows for new and noninvasive ways of acquiring objective real-time data that can provide deeper insights into emotional and cognitive states of players. As the technology becomes less expensive and increasingly novice-friendly, more applications are emerging, ranging from GBA within sport applications to scientific experiments that attempt to connect physiological measures to psychological concepts. This chapter highlights the potentials of process, as well as physiological data, but also the problems that can arise in this context. Finally, this chapter aims to provide a new perspective on the emerging trend of using such data within GBA.
Steve Nebel and Manuel Ninaus shared first-authorship.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Admiraal, W., Huizenga, J., Akkerman, S., & Ten Dam, G. (2011). The concept of flow in collaborative game-based learning. Computers in Human Behavior, 27(3), 1185–1194. https://doi.org/10.1016/j.chb.2010.12.013
Alexander, P. A. (2018). Past as prologue: Educational psychology’s legacy and progeny. Journal of Educational Psychology, 110(2), 147–162. https://doi.org/10.1037/edu0000200
Allison, B. Z., & Polich, J. (2008). Workload assessment of computer gaming using a single-stimulus event-related potential paradigm. Biological Psychology, 77(3), 277–283. https://doi.org/10.1016/j.biopsycho.2007.10.014
Aviezer, H., Trope, Y., & Todorov, A. (2012). Body cues, not facial expressions, discriminate between intense positive and negative emotions. Science, 338(6111), 1225–1229. https://doi.org/10.1126/science.1224313
Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., & Landis, R. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (Vol. 28, pp. 427–449). New York, NY: Springer. https://doi.org/10.1007/978-1-4419-5546-3_28
Baumgartner, T., Speck, D., Wettstein, D., Masnari, O., Beeli, G., & Jäncke, L. (2008). Feeling present in arousing virtual reality worlds: Prefrontal brain regions differentially orchestrate presence experience in adults and children. Frontiers in Human Neuroscience, 2, 1–12. https://doi.org/10.3389/neuro.09.008.2008
Baumgartner, T., Valko, L., Esslen, M., & Jäncke, L. (2006). Neural correlate of spatial presence in an arousing and noninteractive virtual reality: An EEG and psychophysiology study. Cyberpsychology & Behavior, 9(1), 30–45. https://doi.org/10.1089/cpb.2006.9.30
Berta, R., Bellotti, F., De Gloria, A., Pranantha, D., & Schatten, C. (2013). Electroencephalogram and physiological signal analysis for assessing flow in games. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 164–175. https://doi.org/10.1109/TCIAIG.2013.2260340
Bower, M., & Sturman, D. (2015). What are the educational affordances of wearable technologies? Computers & Education, 88, 343–353. https://doi.org/10.1016/j.compedu.2015.07.013
Box, G. E. P., & Tiao, G. C. (2011). Bayesian inference in statistical analysis. New York, NY: Wiley.
Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., … 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. https://doi.org/10.1016/j.compedu.2015.11.003
Calvo-Morata, A., Alonso-Fernández, C., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2018). Making understandable game learning analytics for teachers. In G. Hancke, M. Spaniol, K. Osathanunkul, S. Unankard, & R. Klamma (Eds.), Advances in Web-Based Learning – ICWL 2018 (pp. 112–121). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-96565-9_11
Charles, S. T., Reynolds, C. A., & Gatz, M. (2001). Age-related differences and change in positive and negative affect over 23 years. Journal of Personality and Social Psychology, 80(1), 136–151. https://doi.org/10.1037/0022-3514.80.1.136
Charles University, Czech Academy of Sciences. (2017). Attentat 1942 [Computer Software]. Prague, Czechoslovakia: Author.
Cohn, J. F., Ambadar, Z., & Ekman, P. (2007). Observer-based measurement of facial expression with the Facial Action Coding System. In J. A. Coan & J. J. B. Allen (Eds.), Handbook of emotion elicitation and assessment. Series in affective science (pp. 203–221). New York, NY: Oxford University Press.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper Perennial.
Dasgupta, S., Clements, S. M., Idlbi, A. Y., Willis-Ford, C., & Resnick, M. (2015). Extending Scratch: New pathways into programming. In 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 165–169). Atlanta, GA: IEEE. https://doi.org/10.1109/VLHCC.2015.7357212
Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a delicate issue a checklist for trusted learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16 (pp. 89–98). New York, NY: ACM. https://doi.org/10.1145/2883851.2883893
Epic Games. (2017). Fortnite [Computer Software]. Cary, NC: Author.
Epic Games. (2018). Unreal Engine (Version 4) [Computer Software]. Cary, NC: Author.
Eysink, T. H., de Jong, T., Berthold, K., Kolloffel, B., Opfermann, M., & Wouters, P. (2009). Learner performance in multimedia learning arrangements: An analysis across instructional approaches. American Educational Research Journal, 46(4), 1107–1149. https://doi.org/10.3102/0002831209340235
Ferrari, M., & Quaresima, V. (2012). A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage, 63(2), 921–935. https://doi.org/10.1016/j.neuroimage.2012.03.049
Freeman, J., Avons, S. E., Pearson, D. E., & IJsselsteijn, W. A. (1999). Effects of sensory information and prior experience on direct subjective ratings of presence. Presence: Teleoperators & Virtual Environments, 8(1), 1–13. https://doi.org/10.1162/105474699566017
GameAnalytics. (2016). GameAnalytics [Computer Software]. Copenhagen, Denmark: Author.
Girouard, A., Solovey, E. T., Hirshfield, L. M., Chauncey, K., Jacob, R. J. K., Sassaroli, A., … Jacob, R. J. K. (2009). Distinguishing difficulty levels with non-invasive brain activity measurements. In T. Gross, J. Gulliksen, P. Kotzé, L. Oestreicher, & P. Palanque (Eds.), Human-Computer Interaction – INTERACT 2009 (pp. 440–452). Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-642-03655-2_50
GitHub. (2018). GitHub [Computer Software]. San Francisco, CA: Author.
Granic, I., Lobel, A., & Engels, R. C. (2014). The benefits of playing video games. American Psychologist, 69(1), 66–78. https://doi.org/10.1037/a0034857
Greipl, S., Ninaus, M., Bauer, D., Kiili, K., & Moeller, K. (2018). A fun-accuracy trade-off in game-based learning. In M. Gentile, M. Allegra, & H. Söbke (Eds.), International Conference on Games and Learning Alliance – Lecture Notes in Computer Science (pp. 167–177). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-11548-7_16
Guo, G., & Dyer, C. R. (2005). Learning from examples in the small sample case: Face expression recognition. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 35(3), 477–488. https://doi.org/10.1109/TSMCB.2005.846658
Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179. https://doi.org/10.1016/j.chb.2015.07.045
Hattahara, S., Fujii, N., Nagae, S., Kazai, K., & Katayose, H. (2008). Brain activity during playing video game correlates with player level. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (pp. 360–363). New York, NY: ACM. https://doi.org/10.1145/1501750.1501835
Howard-Jones, P., & Jay, T. (2016). Reward, learning and games. Current Opinion in Behavioral Sciences, 10, 65–72. https://doi.org/10.1016/j.cobeha.2016.04.015
Kalyuga, S., & Singh, A. M. (2016). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review, 28(4), 831–852. https://doi.org/10.1007/s10648-015-9352-0
Kang, J., Liu, M., & Qu, W. (2017). Using gameplay data to examine learning behavior patterns in a serious game. Computers in Human Behavior, 72, 757–770. https://doi.org/10.1016/j.chb.2016.09.062
Kiili, K., Lindstedt, A., & Ninaus, M. (2018). Exploring characteristics of students’ emotions, flow and motivation in a math game competition. In J. Koivisto & J. Hamari (Eds.), Proceedings of the 2nd International GamiFIN Conference (pp. 20–29). Pori, Finland: CEUR Workshop Proceedings.
Kivikangas, J. M. (2006). Psychophysiology of flow experience: An explorative study (Master’s thesis). Retrieved from http://urn.fi/URN:NBN:fi-fe20061271
Klasen, M., Weber, R., Kircher, T. T. J., Mathiak, K. A., & Mathiak, K. (2012). Neural contributions to flow experience during video game playing. Social Cognitive and Affective Neuroscience, 7(4), 485–495. https://doi.org/10.1093/scan/nsr021
Klinkenberg, S., Straatemeier, M., & van der Maas, H. L. (2011). Computer adaptive practice of maths ability using a new item response model for on the fly ability and difficulty estimation. Computers & Education, 57(2), 1813–1824. https://doi.org/10.1016/j.compedu.2011.02.003
Kober, S. E., & Neuper, C. (2012). Using auditory event-related EEG potentials to assess presence in virtual reality. International Journal of Human-Computer Studies, 70(9), 577–587. https://doi.org/10.1016/j.ijhcs.2012.03.004
Lamnek, S., & Krell, C. (2016) Qualitative Sozialforschung [Qualitative social research]. Weinheim, Germany: Beltz Verlagsgruppe.
Littlewort, G. C., Bartlett, M. S., Salamanca, L. P., & Reilly, J. (2011). Automated measurement of children’s facial expressions during problem solving tasks. Face and Gesture, 2011, 30–35. https://doi.org/10.1109/FG.2011.5771418
Liu, Y., Kosmadoudi, Z., Sung, R. C. W., Lim, T., Louchart, S., & Ritchie, J. (2010). Capture user emotions during computer- aided design. In Proceedings of the Integrated Design and Manufacturing in Mechanical Engineering (IDMME) and Virtual Conference (pp. 2–4).
Lomas, J. D., Koedinger, K., Patel, N., Shodhan, S., Poonwala, N., & Forlizzi, J. L. (2017). Is difficulty overrated? The effects of choice, novelty and suspense on intrinsic motivation in educational games. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1028–1039). New York, NY: ACM. https://doi.org/10.1145/3025453.3025638
Maloney, J., Peppler, K., Kafai, Y. B., Resnick, M., & Rusk, N. (2008). Programming by choice: Urban youth learning programming with scratch. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (pp. 367–371). Portland, OR: ACM. https://doi.org/10.1145/1352135.1352260
Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education, 10(4), 1–15. https://doi.org/10.1145/1868358.1868363
Mandryk, R. L., & Atkins, M. S. (2007). A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International Journal of Human-Computer Studies, 65(4), 329–347. https://doi.org/10.1016/j.ijhcs.2006.11.011
Mandryk, R. L., Atkins, M. S., & Inkpen, K. M. (2006). A continuous and objective evaluation of emotional experience with interactive play environments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems CHI 06 (pp. 1027–1036). New York, NY: ACM. https://doi.org/10.1145/1124772.1124926
Marklund, B. B., Backlund, P., & Johannesson, M. (2013). Children’s collaboration in emergent game environments. In Proceedings of the 8th International Conference on the Foundations of Digital Games (pp. 306–313). New York, NY: ACM.
Massachusetts Institute of Technology. (2012). MIT App Inventor [Computer Software]. Cambridge, MA: Author.
Mayer, R. E. (2014). Computer games for learning: An evidence-based approach. Cambridge, MA: MIT.
Mayer, R. E. (2015). On the need for research evidence to guide the design of computer games for learning. Educational Psychologist, 50(4), 349–353. https://doi.org/10.1080/00461520.2015.1133307
Mayer, R. E. (2018). Educational psychology’s past and future contributions to the science of learning, science of instruction, and science of assessment. Journal of Educational Psychology, 110(2), 174–179. https://doi.org/10.1037/edu0000195
Microsoft. (2009). Kodu [Computer Software]. Redmond, WA: Author.
Mionix. (2018). Naos QG [Apparatus and Software]. Växjö, Sweden: Author.
Mojang. (2018). MinecraftEdu [Computer Software]. Stockholm, Sweden: Author.
Nacke, L., & Lindley, C. A. (2008). Flow and immersion in first-person shooters. In Proceedings of the 2008 Conference on Future Play Research, Play, Share - Future Play ’08 (pp. 81–88). New York, NY: ACM. https://doi.org/10.1145/1496984.1496998
Nacke, L. E., Grimshaw, M. N., & Lindley, C. A. (2010). More than a feeling: Measurement of sonic user experience and psychophysiology in a first-person shooter game. Interacting with Computers, 22(5), 336–343. https://doi.org/10.1016/j.intcom.2010.04.005
Nebel, S. (2017). Investigating the mechanisms of competition within educational video games - Die Mechanismen des Wettbewerbs in digitalen Lernspielen (Doctoral dissertation). https://doi.org/10.13140/RG.2.2.28445.41440.
Nebel, S., Beege, M., Schneider, S., & Rey, G. D. (2016). The higher the score, the higher the learning outcome? Heterogeneous impacts of leaderboards and choice within educational videogames. Computers in Human Behavior, 65, 391–401. https://doi.org/10.1016/j.chb.2016.08.042
Nebel, S., Schneider, S., Beege, M., & Rey, G. D. (2017). Leaderboards within educational videogames: The impact of difficulty, effort and gameplay. Computers & Education, 113, 28–41.
Nebel, S., Schneider, S., & Rey, G. D. (2016). Mining learning and crafting scientific experiments: A literature review on the use of Minecraft in education and research. Journal of Educational Technology & Society, 19(2), 355–366.
Nebel, S., Schneider, S., Schledjewski, J., & Rey, G. D. (2017). Goal-setting in educational video games: Comparing goal-setting theory and the goal-free effect. Simulation & Gaming, 48(1), 98–130. https://doi.org/10.1177/1046878116680869
Ninaus, M., Kober, S. E., Friedrich, E. V. C., Dunwell, I., De Freitas, S., Arnab, S., … Neuper, C. (2014). Neurophysiological methods for monitoring brain activity in serious games and virtual environments: A review. International Journal of Technology Enhanced Learning, 6(1), 78–103. https://doi.org/10.1504/IJTEL.2014.060022
Ninaus, M., Kober, S. E., Friedrich, E. V. C., Neuper, C., & Wood, G. (2014). The potential use of neurophysiological signals for learning analytics. In 2014 6th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES) (pp. 1–5). Valletta, Malta: IEEE. https://doi.org/10.1109/VS-Games.2014.7012169
Ninaus, M., Moeller, K., McMullen, J., & Kiili, K. (2017). Acceptance of game-based learning and intrinsic motivation as predictors for learning success and flow experience. International Journal of Serious Games, 4(3), 15–30. https://doi.org/10.17083/ijsg.v4i3.176
Ninja Theory. (2017). Hellblade: Senua’s Sacrifice [Computer Software]. Cambridge, UK: Author.
Nourbakhsh, N., Chen, F., Wang, Y., & Calvo, R. A. (2017). Detecting users’ cognitive load by galvanic skin response with affective interference. ACM Transactions on Interactive Intelligent Systems, 7(3), 1–20. https://doi.org/10.1145/2960413
Novak, E., & Johnson, T. E. (2012). Assessment of student’s emotions in game-based learning. In D. Ifenthaler, D. Eseryel, & X. Ge (Eds.), Assessment in game-based learning (pp. 379–399). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-3546-4_19
Nyamsuren, E., Van der Vegt, W., & Westera, W. (2017). Automated adaptation and assessment in serious games: A portable tool for supporting learning. In M. Winands, H. van den Herik, & W. Kosters (Eds.), Advances in computer games (pp. 201–212). Cham, Switzerland: Springer.
Paas, F., & Sweller, J. (2014). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 27–42). Cambridge, MA: Cambridge University Press.
Peifer, C. (2012). Psychophysiological correlates of flow-experience. In S. Engeser (Ed.), Advances in flow research (pp. 139–164). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2359-1
Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. A. Calvo & S. K. D’Mello (Eds.), New perspectives on affect and learning technologies (pp. 23–39). New York, NY: Springer. https://doi.org/10.1007/978-1-4419-9625-1
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/10.1016/j.cedpsych.2010.10.002
Pellouchoud, E., Smith, M. E., McEvoy, L., & Gevins, A. (1999). Mental effort-related EEG modulation during video-game play: Comparison between juvenile subjects with epilepsy and normal control subjects. Epilepsia, 40(s4), 38–43. https://doi.org/10.1111/j.1528-1157.1999.tb00905.x
Perez-Colado, I., Alonso-Fernandez, C., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2018). Game learning analytics is not informagic! In 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 1729–1737). Tenerife, Spain: IEEE. https://doi.org/10.1109/EDUCON.2018.8363443
Perttula, A., Kiili, K., Lindstedt, A., & Tuomi, P. (2017). Flow experience in game based learning – A systematic literature review. International Journal of Serious Games, 4(1). https://doi.org/10.17083/ijsg.v4i1.151
Plass, J. L., & Kaplan, U. (2016). Emotional design in digital media for learning. In S. Tettegah & M. Gartmeier (Eds.), Emotions, technology, design, and learning (pp. 131–161). New York, NY: Elsevier. https://doi.org/10.1016/B978-0-12-801856-9.00007-4
Pugnetti, L., Mendozzi, L., Barbieri, E., Rose, F. D., Attree, E. A., & Barberi, E. (1996). Nervous system correlates of virtual reality experience. In P. M. Sharkey (Ed.), Proceedings of the First European Conference on Disability, Virtual Reality and Associated Technology (pp. 239–246). Maidenhead, UK: The University of Reading.
Ratcliffe, D. (2017). ComputercraftEdu [Computer software]. Cambridge, UK: Author.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage.
Rey, G. D., & Wender, K. F. (2011). Neuronale Netze: eine Einführung in die Grundlagen, Anwendungen und Datenauswertung. Bern, Switzerland: Huber.
Salminen, M., & Ravaja, N. (2007). Oscillatory brain responses evoked by video game events: The case of super monkey ball 2. Cyberpsychology & Behavior, 10(3), 330–338. https://doi.org/10.1089/cpb.2006.9947
Salminen, M., & Ravaja, N. (2008). Increased oscillatory theta activation evoked by violent digital game events. Neuroscience Letters, 435(1), 69–72. https://doi.org/10.1016/j.neulet.2008.02.009
Schneider, J., Börner, D., Van Rosmalen, P., & Specht, M. (2015). Augmenting the senses: A review on sensor-based learning support. Sensors, 15(2), 4097–4133. https://doi.org/10.3390/s150204097
Schneider, S., Nebel, S., & Rey, G. D. (2016). Decorative pictures and emotional design in multimedia learning. Learning and Instruction, 44, 65–73. https://doi.org/10.1016/j.learninstruc.2016.03.002
Selvaraj, J., Murugappan, M., Wan, K., & Yaacob, S. (2013). Classification of emotional states from electrocardiogram signals: A non-linear approach based on hurst. Biomedical Engineering Online, 12(1), 44. https://doi.org/10.1186/1475-925X-12-44
Shute, V., & Wang, L. (2016). Assessing and supporting hard-to-measure constructs in video games. In A. A. Rupp & J. P. Leighton (Eds.), The Wiley handbook of cognition and assessment (pp. 535–562). Hoboken, NJ: Wiley.
Shute, V. J., & Ventura, M. (2013). Stealth assessment: Measuring and supporting learning in video games. Cambridge, MA: MIT.
Smith, S. P., Blackmore, K., & Nesbitt, K. (2015). A meta-analysis of data collection in serious games research. In C. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious games analytics (pp. 31–55). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-05834-4_2
Solovey, E., Schermerhorn, P., Scheutz, M., Sassaroli, A., Fantini, S., & Jacob, R. (2012). Brainput: Enhancing interactive systems with streaming fnirs brain input. In Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems - CHI ’12 (pp. 2193–2202). New York, NY: ACM. https://doi.org/10.1145/2207676.2208372
Strait, M., Canning, C., & Scheutz, M. (2013). Limitations of NIRS-based BCI for realistic applications in human-computer interaction. In Proceedings of the Fifth International Brain-Computer Interface Meeting (pp. 2–3). Graz, Austria: Graz University of Technology Publishing House. https://doi.org/10.3217/978-3-85125-260-6-2
Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. (2017). Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island. Computers in Human Behavior, 76, 641–655. https://doi.org/10.1016/j.chb.2017.01.038
Thauros-Clan. (2016). Brain computer interface plugin [Computer Software]. Author.
Valve Corporation. (2012). Counter Strike: Global Offensive [Computer Software]. Bellevue, WA: Author.
Vorderer, P., Wirth, W., Gouveia, F. R., Biocca, F., Saari, T., Jäncke, F., … Jäncke, P. (2004). MEC Spatial Presence Questionnaire (MECSPQ): Short documentation and instructions for application. Report to the European Community, Project Presence: MEC (IST-2001-37661). Retrieved from https://www.researchgate.net/publication/318531435_MEC_spatial_presence_questionnaire_MEC-SPQ_Short_documentation_and_instructions_for_application
Vorderer, P., Wirth, W., Saari, T., Gouveia, F. R., Biocca, F., Jäncke, F., … Jäncke, P. (2003). Constructing presence: Towards a two-level model of the formation of Spatial Presence. Unpublished report to the European Community, Project Presence: MEC (IST-2001-37661). Hannover, Munich, Helsinki, Porto, Zurich.
Wirzberger, M., Herms, R., Esmaeili Bijarsari, S., Rey, G. D., & Eibl, M. (2017). Influences of cognitive load on learning performance, speech and physiological parameters in a dual-task setting. In Poster session presented at the meeting of the 20th Conference of the European Society for Cognitive Psychology, Potsdam, Germany.
Wise, R. A. (2004). Dopamine, learning and motivation. Nature Reviews Neuroscience, 5(6), 483–494. https://doi.org/10.1038/nrn1406
Witte, M., Ninaus, M., Kober, S. E. S. E., Neuper, C., & Wood, G. (2015). Neuronal correlates of cognitive control during gaming revealed by near-infrared spectroscopy. PLoS One, 10(8), e0134816. https://doi.org/10.1371/journal.pone.0134816
Wu, C. H., Huang, Y. M., & Hwang, J. P. (2016). Review of affective computing in education/learning: Trends and challenges. British Journal of Educational Technology, 47(6), 1304–1323. https://doi.org/10.1111/bjet.12324
Xiao, X., & Wang, J. (2016). Context and cognitive state triggered interventions for mobile MOOC learning. In ICMI ’16: Proceedings of the 18th ACM International Conference on Multimodal Interaction (pp. 378–385). New York, NY: ACM. https://doi.org/10.1145/2993148.2993177
Xue, S., Wu, M., Kolen, J., Aghdaie, N., & Zaman, K. A. (2017). Dynamic difficulty adjustment for maximized engagement in digital games. In WWW ’17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion (pp. 465–471). Geneva, Switzerland: International World Wide Web Conferences Steering Committee and Republic and Canton of Geneva. https://doi.org/10.1145/3041021.3054170
Acknowledgments
The current research was funded by the Leibniz-Competition Fund (SAW-2016-IWM-3) and the Leibniz-WissenschaftsCampus “Cognitive Interfaces” (MWK-WCT TP12) supporting Manuel Ninaus.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nebel, S., Ninaus, M. (2019). New Perspectives on Game-Based Assessment with Process Data and Physiological Signals. In: Ifenthaler, D., Kim, Y.J. (eds) Game-Based Assessment Revisited. Advances in Game-Based Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-15569-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-15569-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15568-1
Online ISBN: 978-3-030-15569-8
eBook Packages: EducationEducation (R0)