Effects of AI Scaffolding on ZPD in MOOC Instructional RPGs | SpringerLink
Skip to main content
Springer Nature Link
Log in

Effects of AI Scaffolding on ZPD in MOOC Instructional RPGs

  • Conference paper
  • First Online:
Innovative Technologies and Learning (ICITL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12555))

Included in the following conference series:

  • 2397 Accesses

Abstract

This study conducts development, application, and testing of artificial intelligence (AI) tools to both monitor learner ZPD and choose appropriate scaffolding for students in a large class, while students learn- by-doing through a Role Playing Game (RPG). Machine learning algorithms are developed and integrated into the cloud-based activity at both the individual and group level. Decision trees are developed that decide a range of scaffolding to supply individual learners and groups. Finally, data are tested across control and test groups. Research results show that learners in both the blended and fully online modalities accurately recall mere-exposure scaffolding (MES). Not only do learners recall seeing the MES, over the eight RPG rounds, they also accurately recall the main pedagogical message contained in the MES. Learners receiving MES in an online mode demonstrate more behaviors associating with the core pedagogical MES message content comparing to those in a blended mode. Fully online learners more frequently check their group’s online RPG statistics and status information while also taking more time to prepare group attributes for a new RPG round.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bornstein, R.F.: Exposure and affect: overview and meta-analysis of research, 1968–1987. Psychol. Bull. 106, 265 (1989)

    Article  Google Scholar 

  2. Montoya, R.M., Horton, R.S., Vevea, J.L., Citkowicz, M., Lauber, E.A.: A re-examination of the mere exposure effect: the influence of repeated exposure on recognition, familiarity, and liking. Psychol. Bull. 143, 459 (2017)

    Article  Google Scholar 

  3. Shu, H., Gu, X.: Determining the differences between online and face-to-face student–group interactions in a blended learning course. Internet High. Educ. 39, 13–21 (2018)

    Article  Google Scholar 

  4. Owston, R., York, D.N.: The nagging question when designing blended courses: does the proportion of time devoted to online activities matter? Internet High. Educ. 36, 22–32 (2018)

    Article  Google Scholar 

  5. Adekola, J., Dale, V.H., Gardiner, K.: Development of an institutional framework to guide transitions into enhanced blended learning in higher education. Res. Learn. Technol. 25, 1973–1997 (2017)

    Article  Google Scholar 

  6. Park, Y., Yu, J.H., Jo, I.H.: Clustering blended learning courses by online behavior data: a case study in a Korean higher education institute. Internet High. Educ. 29, 1–11 (2016)

    Article  Google Scholar 

  7. Roby, T., Ashe, S., Singh, N., Clark, C.: Shaping the online experience: How administrators can influence student and instructor perceptions through policy and practice. Internet High. Educ. 17, 29–37 (2013)

    Article  Google Scholar 

  8. Wingo, N.P., Peters, G.B., Ivankova, N.V., Gurley, D.K.: Benefits and challenges of teaching nursing online: exploring perspectives of different stakeholders. J. Nurs. Educ. 55, 433–440 (2016)

    Article  Google Scholar 

  9. Boling, E.C., Hough, M., Krinsky, H., Saleem, H., Stevens, M.: Cutting the distance in distance education: perspectives on what promotes positive, online learning experiences. Internet High. Educ. 15, 118–126 (2012). https://doi.org/10.1016/j.iheduc.2011.11.006

    Article  Google Scholar 

  10. Spector, J.M.: What makes good online instruction good?: new opportunities and old barriers. In: Visser, J., Visser-Valfrey, M. (eds.) Learners in a Changing Learning Landscape, pp. 251–266. Springer, Dordrecht (2008)

    Chapter  Google Scholar 

  11. Zajonc, R.B.: Attitudinal effects of mere exposure. J. Pers. Soc. Psychol. 9, 1 (1968)

    Article  Google Scholar 

  12. Hausner, H., Olsen, A., Møller, P.: Mere exposure and flavour–flavour learning increase 2–3 year-old children’s acceptance of a novel vegetable. Appetite 58, 1152–1159 (2012)

    Article  Google Scholar 

  13. Carr, E.W., Brady, T.F., Winkielman, P.: Are you smiling, or have I seen you before? Familiarity makes faces look happier. Psychol. Sci. 28, 1087–1102 (2017)

    Article  Google Scholar 

  14. Cooke, L.: The importance of exposure for healthy eating in childhood: a review. J. Hum. Nutr. Diet. 20, 294–301 (2007)

    Article  Google Scholar 

  15. Schaffhauser, D.: 06/11/15: Research: 6 in 10 Millennials Have “Low” Technology Skills. https://campustechnology.com/articles/2015/06/11/report-6-of-10-millennials-have-low-technology-skills.aspx. Accessed 15 Jul 2019

  16. Kouchaki, M., Smith-Crowe, K., Brief, A.P., Sousa, C.: Seeing green: mere exposure to money triggers a business decision frame and unethical outcomes. Organ. Behav. Hum. Decis. Process. 121, 53–61 (2013)

    Article  Google Scholar 

  17. Hollingsworth, P.L.: The combined effect of mere exposure, counter attitudinal advocacy, and art criticism methodology on upper elementary and junior high students’ affect toward art works. Stud. Art Educ. 24, 101–110 (1983)

    Article  Google Scholar 

  18. Järvelä, S., et al.: Socially shared regulation of learning in CSCL: understanding and prompting individual-and group-level shared regulatory activities. Int. J. Comput. Support. Collab. Learn. 11, 263–280 (2016). https://doi.org/10.1007/s11412-016-9238-2

    Article  Google Scholar 

  19. De Laat, M., Lally, V., Lipponen, L., Simons, R.-J.: Investigating patterns of interaction in networked learning and computer-supported collaborative learning: a role for social network analysis. Int. J. Comput. Support. Collab. Learn. 2, 87–103 (2007). https://doi.org/10.1007/s11412-007-9006-4

    Article  Google Scholar 

  20. Järvelä, S., Järvenoja, H., Malmberg, J.: Capturing the dynamic and cyclical nature of regulation: methodological progress in understanding socially shared regulation in learning. Int. J. Comput. Support. Collab. Learn. 14(4), 425–441 (2019). https://doi.org/10.1007/s11412-019-09313-2

    Article  Google Scholar 

  21. Cleary, T.J., Zimmerman, B.J.: Self-regulation differences during athletic practice by experts, non-experts, and novices. J. Appl. Sport Psychol. 13, 185–206 (2001)

    Article  Google Scholar 

  22. Kitsantas, A., Zimmerman, B.J.: Comparing self-regulatory processes among novice, non-expert, and expert volleyball players: a microanalytic study. J. Appl. Sport Psychol. 14, 91–105 (2002)

    Article  Google Scholar 

  23. Leggett, H., Sandars, J., Roberts, T.: Twelve tips on how to provide self-regulated learning (SRL) enhanced feedback on clinical performance. Med. Teacher 41, 147–151 (2019)

    Article  Google Scholar 

  24. Panadero, E.: A review of self-regulated learning: six models and four directions for research. Front. Psychol. 8, 422 (2017)

    Article  Google Scholar 

  25. Zimmerman, B.J.: Attaining self-regulation: a social cognitive perspective. In: Boekaerts, M., Pintrich, P.R., Zeidner, M. (eds.) Handbook of Self-Regulation, pp. 13–40. Academic Press, San Diego (2000)

    Chapter  Google Scholar 

  26. Chaiklin, S.: The zone of proximal development in Vygotsky’s analysis of learning and instruction. In: Kozulin, A., Gindis, B., Ageyev, V.S., Miller, S.M. (eds.) Vygotsky’s Educational Theory in Cultural Context, pp. 39–64. Cambridge University Press, Cambridge (2003)

    Chapter  Google Scholar 

  27. Bowman, S.L., Schrier, K.: Players and their characters in role-playing games. In: Zagal, J.P., Deterding, S. (eds.) Role-Playing Game Studies: A Transmedia Approach, pp. 395–410. Routledge (2018)

    Google Scholar 

  28. Bowman, S.L.: The Functions of Role-Playing Games: How Participants Create Community, Solve Problems and Explore Identity. McFarland & Company Inc. Publishers, Jefferson (2010)

    Google Scholar 

  29. Lankoski, P., Björk, S.: Game research methods: an overview. Lulu.com (2015)

    Google Scholar 

  30. Choi, B., Lee, I., Choi, D., Kim, J.: Collaborate and share: an experimental study of the effects of task and reward interdependencies in online games. CyberPsychol. Behav. 10, 591–595 (2007)

    Article  Google Scholar 

  31. Keller, J., Suzuki, K.: Learner motivation and e-learning design: a multinationally validated process. J. Educ. Media 29, 229–239 (2004)

    Article  Google Scholar 

  32. Kim, C., Keller, J.M.: Effects of motivational and volitional email messages (MVEM) with personal messages on undergraduate students’ motivation, study habits and achievement. Br. J. Educ. Technol. 39, 36–51 (2008)

    Google Scholar 

  33. Hernández-García, Á., Acquila-Natale, E., Chaparro-Peláez, J., Conde, M.Á.: Predicting teamwork group assessment using log data-based learning analytics. Comput. Hum. Behav. 89, 373–384 (2018)

    Article  Google Scholar 

  34. Lerche, T., Kiel, E.: Predicting student achievement in learning management systems by log data analysis. Comput. Hum. Behav. 89, 367–372 (2018)

    Article  Google Scholar 

  35. Emmerich, K., Bogacheva, N., Bockholt, M., Wendel, V.: Operationalization and measurement of evaluation constructs. In: Dörner, R., Göbel, S., Kickmeier-Rust, M., Masuch, M., Zweig, K. (eds.) Entertainment Computing and Serious Games. LNCS, vol. 9970, pp. 306–331. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46152-6_13

    Chapter  Google Scholar 

  36. Zhou, M., Winne, P.H.: Modeling academic achievement by self-reported versus traced goal orientation. Learn. Instruct. 22, 413–419 (2012)

    Article  Google Scholar 

  37. Broadbent, J., Poon, W.: Self-regulated learning strategies & academic achievement in online higher education learning environments: a systematic review. Internet and High. Educ. 27, 1–13 (2015)

    Article  Google Scholar 

  38. Nistor, N., Hernández-Garcíac, Á.: What types of data are used in learning analytics? An overview of six cases. Comput. Hum. Behav. 89, 335–338 (2018)

    Article  Google Scholar 

  39. R Core Team: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2013)

    Google Scholar 

  40. Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJ. Complex Syst. 1695, 1–9 (2006)

    Google Scholar 

  41. Coleman, J.S.: Social capital in the creation of human capital. Am. J. Sociol. 94, S95–S120 (1988)

    Article  Google Scholar 

Download references

Acknowledgment

The authors are grateful to the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. MOST 108-2511-H-240-001 -.

Author information

Authors and Affiliations

  1. Marketing Department, National Chung Hsing University, Taichung, Taiwan

    Clyde A. Warden

  2. Business Administration Department, Overseas Chinese University, Taichung, Taiwan

    Judy F. Chen

Authors
  1. Clyde A. Warden
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Judy F. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Judy F. Chen .

Editor information

Editors and Affiliations

  1. National Taichung University of Science and Technology, Taichung City, Taiwan

    Tien-Chi Huang

  2. National Yunlin University of Science and Technology, Douliou, Taiwan

    Ting-Ting Wu

  3. University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    João Barroso

  4. Oslo Metropolitan University, Oslo, Norway

    Frode Eika Sandnes

  5. University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Martins

  6. National Cheng Kung University, Tainan City, Taiwan

    Yueh-Min Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Warden, C.A., Chen, J.F. (2020). Effects of AI Scaffolding on ZPD in MOOC Instructional RPGs. In: Huang, TC., Wu, TT., Barroso, J., Sandnes, F.E., Martins, P., Huang, YM. (eds) Innovative Technologies and Learning. ICITL 2020. Lecture Notes in Computer Science(), vol 12555. Springer, Cham. https://doi.org/10.1007/978-3-030-63885-6_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63885-6_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63884-9

  • Online ISBN: 978-3-030-63885-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation