Abstract
Prior research has shown that students learn from Intelligent Tutoring Systems (ITS). However, students’ attention may drift or become disengaged with the task over extended amounts of instruction. To remedy this problem, researchers have examined the impact of game-like features (e.g., a narrative) in digital learning environments on motivation and learning. Some of this research has concluded that the game-like features decrease learning because the features take away resources from the primary task of learning subject-matter content. However, these experiments have involved short-term interventions of less than an hour. Two experiments using college students examined the impact of adding game-like features to the ITS AutoTutor in an intervention that lasted 4 h. In one study, a game-like version was compared to a text-only version and a “do nothing” control. In another study, a game-like version was compared to a nongame version that had similar interfaces. Unlike prior research that has shown that narratives decrease learning in digitally-based learning environments, the game-like features, which included a narrative, had little impact on learning from the ITS. Reasons for the discrepancies are discussed.
Similar content being viewed by others
References
Adams, D. M., & Clark, D. B. (2014). Integrating self-explanation functionality into a complex game environment: Keeping gaming in motion. Computers & Education, 73, 149–159.
Adams, D. M., Mayer, R. E., MacNamara, A., Koenig, A., & Wainess, R. (2012). Narrative games for learning: Testing the discovery and narrative hypotheses. Journal of Educational Psychology, 104, 235–249.
Baker, R. S. J., Corbett, A. T., Koedinger, K. R., Evenson, S., Roll, Il, Wagner, A. Z., et al. (2006). Adapting to when students game an Intelligent Tutoring System. In M. Ikeda, K. D. Ashley, & T. W. Chan (Eds.), Proceedings of Intelligent Tutoring Systems 8th international conference ITS 2011, May 2011 (pp. 392–401). Berlin: Springer.
Baker, R. S. J. D., D’Mello, S. K., Rodrigo, M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human–Computer Studies, 68, 223–241.
Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology-Enhanced Learning, 5, 123–152.
Blizzard Entertainment. (2004). World of warcraft. http://www.google.com/?gws_rd=ssl#q=world+of+warcraft&stick=H4sIAAAAAAAAAONgFuLQz9U3MMyqsFTiBLGMDFPKKrREwjJTUvPdE3NTfROzU4tCwEwAMaI3Ni0AAAA.
ChanMin, K., & Pekrun, R. (2013). Emotions and motivation in learning and performance. Handbook of research on educational communications and technology (pp. 65–75). New York: Springer.
D’Mello, S., & Graesser, A. C. (2012). Emotions during learning with AutoTutor. In P. J. Durlach & A. Lesgold (Eds.), Adaptive technologies for training and education. Cambridge: Cambridge University Press.
Dzikovska, M. O., Steinhauser, N., Farrow, E., Moore, J. D., & Campbell, G. E. (2014). BEETLE II: Deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics. International Journal of Artificial Intelligence in Education, 24, 284–332.
Forsyth. (2014). Predicting learning: A fine-grained analysis of learning in a serious game. Unpublished doctoral dissertation. The University of Memphis.
Gee, J. P. (2009). Deep learning properties of good video games: How far can they go? In U. Ritterfeld, M. Cody, & P. Vorderer (Eds.), Serious games: Mechanisms and effects (pp. 67–82). New York: Routledge.
Gee, J. P. (2013). Games for learning. Educational Horizons, 91, 17–20.
Graesser, A. C. (2016). Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26, 124–132.
Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005). AutoTutor: An Intelligent Tutoring System with mixed-initiative dialogue. IEEE Transactions in Education, 48, 612–618.
Graesser, A. C., Conley, M. W., & Olney, A. (2012a). Intelligent Tutoring Systems. In S. Graham & K. Harris (Eds.), APA handbook of educational psychology. Washington, DC: American Psychological Association.
Graesser, A. C., & D’Mello, S. (2012). Emotions during the learning of difficult material. In B. Ross (Ed.), The psychology of learning and motivation (Vol. 57, pp. 183–225). Amsterdam: Elsevier.
Graesser, A. C., D’Mello, S. K., Hu, X., Cai, Z., Olney, A., & Morgan, B. (2012b). AutoTutor. In P. M. McCarthy & C. Boonthum (Eds.), Applied natural language processing and content analysis: Identification, investigation and resolution (pp. 169–187). Hershey, PA: IGI Global.
Graesser, A. C., D’Mello, S. K., Craig, S. D., Witherspoon, A. M., Sullins, J., McDaniel, B., et al. (2008). The relationship between affective states and dialogue patterns during interactions with AutoTutor. Journal of Interactive Learning Research, 19(2), 293–312.
Graesser, A. C., Hu, X., Nye, B., & Sottilare, R. (2016). Intelligent Tutoring Systems, serious games, and the Generalized Intelligent Framework for Tutoring (GIFT). In H. F. O’Neil, E. L. Baker, & R. S. Perez (Eds.), Using games and simulation for teaching and assessment (pp. 58–79). Abingdon: Routledge.
Graesser, A. C., Li, H., & Forsyth, C. (2014). Learning by communicating in natural language with conversational agents. Current Directions in Psychological Science, 23, 374–380.
Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H. H., Ventura, M., Olney, A. M., et al. (2004). AutoTutor: A tutor with dialogue in natural language. Behavioral, Research Methods, Instruction & Computers, 36, 180–193.
Graesser, A. C., & Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31, 104–137.
Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology, 9, 495–522.
Halpern, D. F., Millis, K., Graesser, A. C., Butler, H., Forsyth, C., & Cai, Z. (2012). Operation ARA: A computerized learning game that teaches critical thinking and scientific reasoning. Thinking Skills and Creativity, 7, 93–100.
Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90, 414–434.
Institute for Education Sciences. (2009). Science 2009: National assessment of educational progress at grades 4, 8, and 12. http://nces.ed.gov/nationsreportcard/pdf/main2009/2011451.pdf.
Jackson, G. T., & McNamara, D. S. (2013). Motivation and performance in a game-based Intelligent Tutoring System. Journal of Educational Psychology, 105, 1036–1049.
Johnson, L. W., & Valente, A. (2008). Tactical language and culture training systems: Using artificial intelligence to teach foreign languages and cultures. In M. Goker & K. Haigh (Eds.), Proceedings of the twentieth conference on innovative applications of artificial intelligence (pp. 1632–1639). Menlo Park, CA: AAAI Press.
Koening, A. D. (2008). Exploring effective educational video game design: The interplay between narrative and game-schema construction (Unpublished doctoral dissertation). Arizona State University.
Landers, R. N. (2014). Developing a theory of gamified learning: Linking serious games and gamification of learning. Simulation & Gaming, 45(6), 752–768.
Landers, R. N., & Landers, A. K. (2014). An empirical test of the theory of gamified learning: The effect of leaderboards on time-on-task and academic performance. Simulation & Gaming, 45(6), 769–785.
Lane, H. C., Noren, D., Auerbach, D., Birch, M., & Swartout, W. (2011). Intelligent tutoring goes to the museum in the big city: A pedagogical agent for informal science education. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial intelligence in education: 15th International conference (pp. 155–162). Heidelberg: Springer.
Lepper, M. R., & Henderlong, J. (2000). Turning “play” into “work” and “work” into “play”: 25 years of research on intrinsic versus extrinsic motivation. In C. Sansone & J. M. Harackiewicz (Eds.), Intrinsic and extrinsic motivation: The search for optimal motivation and performance (pp. 257–307). San Diego, CA: Academic Press.
McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38, 1–30.
McNamara, D. S., Jackson, G. T., & Graesser, A. C. (2010). Intelligent tutoring and games (ITaG). In Y. K. Baek (Ed.), Gaming for classroom-based learning: Digital role-playing as a motivator of study (pp. 44–65). Hershey, PA: IGI Global.
McQuiggan, S. W., Robinson, J., & Lester, J. (2010). Affective transitions in narrative-centered learning environments. Educational Technology & Society, 13, 40–53.
McQuiggan, S. W., Rowe, J. P., Lee, S., & Lester, J. C. (2008). Story-based learning: The impact of narrative on learning experiences and outcomes. Intelligent Tutoring Systems: Lecture Notes in Computer Science, 5091, 530–539.
Millis, K., Graesser, A., & Halpern, D. (2014). Operation ARA: A serious game that combines intelligent tutoring and learning principles to teach science. In V. Benassi, C. E. Overson, & C. M. Hakala, (Eds.) Applying the science of learning in education: Infusing psychological science into the curriculum. Retrieved from the Society for the Teaching of Psychology web site: http://teachpsych.org/ebooks/asle2014/index.php.
National Science Foundation. (2012). National Science Board’s Science and Engineering Indicators 2012. http://www.nsf.gov/statistics/seind12/c7/c7h.htm.
Nye, B. D., Graesser, A. C., & Hu, X. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24, 427–469.
Olney, A., D’Mello, S. K., Person, N., Cade, W., Hays, P., Williams, C., et al. (2012). Guru: A computer tutor that models expert human tutors. In S. Cerri, W. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Proceedings of Intelligent Tutoring Systems (ITS) 2012 (pp. 256–261). Berlin: Springer.
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.
Richards, J., Stebbins, L., & Moellering, K. (2013). Games for a digital age: K-12 market map and investment analysis. New York: The Joan Ganz Cooney Center at Sesame Workshop.
Ritterfeld, U., Cody, M., & Vorderer, P. (Eds.). (2009). Serious games: Mechanisms and effects. New York and London: Routledge, Taylor & Francis.
Rowe, J., Shores, L. R., Mott, B., & Lester, J. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 121, 115–133.
Rus, V., D’Mello, S., Hu, X., & Graesser, A. C. (2013). Recent advances in intelligent systems with conversational dialogue. AI Magazine, 34, 42–54.
Sabourin, J. L., Rowe, J. P., Mott, B. W., & Lester, J. C. (2013). Considering alternate futures to classify off-task behavior as emotion self-regulation: A supervised learning approach. Journal of Educational Data Mining, 5, 9–38.
Sanchez, C. A., & Wiley, J. (2006). An examination of the seductive details effect in terms of working memory capacity. Memory & Cognition, 34, 344–355.
Shaffer, D. W. (2007). How computer games help children learn. New York, NY: Palgrave.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.
Sweller, J. (1999). Instructional design in technical Areas. Camberwell, VIC: Australian Council for Educational Research.
VanLehn, K. (2011). The relative effectiveness of human tutoring, Intelligent Tutoring Systems, and other tutoring systems. Educational Psychologist, 46, 197–221.
VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, P. A. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 30, 1–60.
Vogel, J. J., Vogel, D. S., Cannon-Bowers, J., Bowers, C. A., Muse, K., & Wright, M. (2006). Computer gaming and interactive simulations for learning: A meta-analysis. Journal of Educational Computing Research, 34, 229–243.
Vygotsky, L. S. (1978). Mind in society: Development of higher psychological processes (p. 86). Cambridge: Harvard College.
Wang, H., Shen, C., & Ritterfeld, U. (2009). Enjoyment of digital games: What makes them “seriously” fun? In U. Ritterfeld, M. Cody, & P. Vorderer (Eds.), Serious games: Mechanisms and effects (pp. 25–47). New York and London: Routledge, Taylor & Francis.
Woolf, B. P. (2009). Building Intelligent Tutoring Systems. Burlington, MA: Morgan Kaufman.
Wouters, P., van Nimwegen, C., van der Spek, E. D., & van Oostendorp, H. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology, 105, 249–265.
Acknowledgments
The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305B070349 to Northern Illinois University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Millis, K., Forsyth, C., Wallace, P. et al. The Impact of Game-Like Features on Learning from an Intelligent Tutoring System. Tech Know Learn 22, 1–22 (2017). https://doi.org/10.1007/s10758-016-9289-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10758-016-9289-5