Teaching and Learning with AI-Generated Courseware: Lessons from the Classroom

Kersten T. Schroeder, Martha Hubertz, Rachel Van Campenhout, Benny G. Johnson

Abstract


While research in the learning sciences has spurred advancements in educational technology, the implementation of those learning resources in natural learning contexts advances teaching and learning. In this paper, two faculty members at the University of Central Florida used courseware generated with artificial intelligence as the primary learning resource for their students. The selection and enhancement of this courseware is contextualized for each course. Instructor implementation practices over multiple semesters are described and related to resulting student engagement and exam scores. Finally, benefits of the adaptive courseware are discussed not only for student outcomes, but the qualitative changes faculty identified and the impact that iterative changes in teaching practice had on instructors as well as students.

 


Keywords


Teaching and Learning; Engagement; Learning Outcomes; Courseware; Learn by Doing

Full Text:

PDF

References


Anderson, L.W. (Ed.), Krathwohl, D.R. (Ed.), Airasian, P.W., Cruikshank, K.A., May-er, R.E., Pintrich, P.R., Raths, J., & Wittrock, M.C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s Taxonomy of Educational Objectives (Complete edition). New York: Longman.

Andrew, D. M., & Bird, C. (1938). A comparison of two new-type questions: recall and recognition. Journal of Educational Psychology, 29(3), 175–193. https://doi.org/10.1037/h0062394

Arnold, K. E., & Pistilli, M. D. (2012) Course Signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. New York, NY: ACM, pp. 267–270.

Baker, R. S. (2016). Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614. https://doi.org/10.1007/s40593- 016-0105-0

Baker, S., & Inventado, P. S. (2016). Educational data mining and learning analytics: Potentials and possibilities for online education. In G. Veletsianos (Ed.), Emergence and Innovation in Digital Learning (83–98). https://doi.org/10.15215/aupress/9781771991490.01

Black, P., & William, D. (2010). Inside the black box: raising standards through classroom assessment. Phi Delta Kappan, 92(1), 81–90. https://doi.org/10.1177/003172171009200119

Bosson, J. K., Vendello, J. A., & Buckner, C. V. (2018). The psychology of sex and gender (1st ed.). Thousand Oaks, California: SAGE Publications.

Carvalho, P. F., Mclaughlin, E. A., & Koedinger, K. R. (2017). Is there an explicit learning bias? Students beliefs, behaviors and learning outcomes. Proceedings of the 39th Annual Conference of the Cognitive Science Society (Eds. Gunzelmann, G. et al.), 204–209. https://cogsci.mindmodeling.org/2017/papers/0050/index.html

Dittel, J. S., Jerome, B., Brown, N., Benton, R., Van Campenhout, R., Kimball, M. M., Profitko, C., & Johnson, B. G. (2019). SmartStart: Artificial Intelligence Technology for Automated Textbook-to-Courseware Transformation, Version 1.0. Raleigh, NC: VitalSource Technologies.

Dunlosky, J., Rawson, K., Marsh, E., Nathan, M., & Willingham, D. (2013). Improving students’ learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-58. https://doi.org/10.1177/1529100612453266

Ertmer. P.A., & Newby, T. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly. 24(3), 55–76. https://doi.org/10.1002/piq

Fiorella, L., & Mayer, R. E. (2016). Eight Ways to Promote Generative Learning. Educational Psychology Review, 717–741. https://doi.org/10.1007/s10648-015-9348-9

Fitzpatrick, L., & McConnell, C. (2008). Student reading strategies and textbook use: an inquiry into economics and accounting courses. Research in Higher Education Journal, 1–10.

Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education. Educause. https://library.educause.edu/-/media/files/library/2005/12/ers0508w-pdf.pdf

Hubertz, M. & Van Campenhout, R. (2022). Teaching and Iterative Improvement: The Impact of Instructor Implementation of Courseware on Student Outcomes. The IAFOR International Conference on Education – Hawaii 2022 Official Conference Proceedings. ISSN: 2189-1036, pp. 201–210. Honolulu, Hawaii. https://doi.org/10.22492/issn.2189-1036.2022.19

Jerome, B., Van Campenhout, R., & Johnson, B. G. (2021). Automatic Question Generation and the SmartStart Application. Learning at Scale. https://doi.org/10.1145/3430895.3460878

Johnson, B. G., Dittel, J. S., Van Campenhout, R., & Jerome, B. (2022). Discrimination of automatically generated questions used as formative practice. Proceedings of the Ninth ACM Conference on Learning@Scale (pp. 325-329). https://doi.org/10.1145/3491140.3528323

Kessler, A., Boston, M., & Stein, M. K. (2019). Exploring how teachers support students’ mathematical learning in computer-directed learning environments. Information and Learning Science, 121(1–2), 52–78. https://doi.org/10.1108/ILS-07-2019-0075

Koedinger, K., Kim, J., Jia, J., McLaughlin, E., & Bier, N. (2015, March). Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. Proceedings of the Second ACM Conference on Learning@Scale (pp. 111-120). https://doi.org/10.1145/2724660.2724681

Koedinger, K. R., McLaughlin, E. A., Jia, J. Z., & Bier, N. L. (2016, April). Is the doer effect a causal relationship? How can we tell and why it's important. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 388-397). Edinburgh, United Kingdom. http://dx.doi.org/10.1145/2883851.2883957

Kurdi, G., Leo, J., Parsia, B., Sattler, U., & Al-Emari, S. (2020). A Systematic Review of Automatic Question Generation for Educational Purposes. International Journal of Artificial Intelligence in Education, 30, 121–204. https://doi.org/10.1007/s40593-019-00186-y

Lovett, M., Meyer, O., & Thille, C. (2008). The Open Learning Initiative: Measuring the

effectiveness of the OLI statistics course in accelerating student learning. Journal of

Interactive Media in Education, (1), 1-16. http://doi.org/10.5334/2008-14

Margulieux, L. E., McCracken, W. M., & Catrambone, R. (2015). Mixing in-class and online learning: Content meta-analysis of outcomes for hybrid, blended, and flipped courses. In O. Lindwall, P. Hakkinen, T. Koschmann, P. Tchounikine, & S. Ludvigsen (Eds.) Exploring the Material Conditions of Learning: The Computer Supported Collaborative Learning (CSCL) Conference (pp. 220-227), 2. Gothenburg, Sweden: The International Society of the Learning Sciences.

O’Donnell, C. L. (2008). Defining, Conceptualizing, and Measuring Fidelity of Implementation and Its Relationship to Outcomes in K-12 Curriculum Intervention. (2008). Review of Educational Research. 78(1). Pp. 33–84. https://doi.org/10.3102/0034654307313793

Piaget, J. (1926). The language and thought of the child. London: Kegan Paul, Trench, Trubner and Company.

Ritter, S., Fancsali, S., Yudelson, M., Rus, V., & Berman, S. (2016). Toward intelligent instructional handoffs between humans and machines. Workshop on Machine Learning for Education, The Thirtieth Conference on Neural Information Processing Systems (NIPS), Barcelona.

Schroeder, K., Hubertz, M., Johnson, B. G., & Van Campenhout, R. (2021). Courseware at Scale: Using Artificial Intelligence to Create Learning by Doing from Textbooks. Presented at OLC Accelerate, Washington D.C., Oct. 6 2021.

Singer, S.R. & Bonvillian, W.B. (2013). Two Revolutions in Learning. Science 22, Vol. 339 no. 6126, p.1359. https://doi.org/10.1126/science.1237223

Sullivan, P. O., Adams, S., & Ed, M. (2020). Adaptive Courseware Implementation Guide. https://solve.everylearnereverywhere.org/toolsforimplementation/88Urs1I6xJydwcxydkYq

Swanson, M., Reguera, G., Schaechter, M., & Neidhardt, F. (2016). Microbe (2nd ed.). Washington, DC: ASM Press

Van Campenhout, R., Dittel, J. S., Jerome, B., & Johnson, B. G. (2021). Transforming textbooks into learning by doing environments: an evaluation of textbook-based automatic question generation. Third Workshop on Intelligent Textbooks at the 22nd International Conference on Artificial Intelligence in Education. CEUR Workshop Proceedings, ISSN 1613-0073 (pp. 1–12). http://ceur-ws.org/Vol-2895/paper06.pdf

Van Campenhout, R., Jerome, B., & Johnson, B. G. (2020). The impact of adaptive activities in Acrobatiq courseware: Investigating the efficacy of formative adaptive activities on learning estimates and summative assessment scores. In: Sottilare R., Schwarz J. (eds) Adaptive Instructional Systems. HCII 2020. LNCS, vol 12214. Springer. pp 543–554. https://doi.org/10.1007/978-3-030-50788-6_40

Van Campenhout, R. Johnson, B. G., & Olsen, J. A. (2021). The doer effect: Replicating findings that doing causes learning. Proceedings of eLmL 2021: The Thirteenth International Conference on Mobile, Hybrid, and On-line Learning. ISSN 2308-4367 (pp. 1–6). https://www.thinkmind.org/index.php?view=article&articleid=elml_2021_1_10_58001

Van Campenhout, R. Johnson, B. G., & Olsen, J. A. (in press). The doer effect: Replication and comparison of correlational and causal analyses of Learning. International Journal on Advances in Systems and Measurements.

Van Campenhout, R. & Kessler, A. (2022). Developing Instructor Training for Diverse & Scaled Contexts: A Learning Engineering Challenge. Proceedings of eLmL 2022: The Fourteenth International Conference on Mobile, Hybrid, and On-line Learning. ISSN: 2308-4367, pp. 29–34. https://www.thinkmind.org/index.php?view=article&articleid=elml_2022_2_20_58006

Van Campenhout, R. & Kimball, M. (2021). At the intersection of technology and teaching: The critical role of educators in implementing technology solutions. IICE 2021: The 6th IAFOR International Conference on Education. ISSN 2189-1036, pp. 151–161. https://doi.org/10.22492/issn.2189-1036.2021.11




DOI: http://dx.doi.org/10.24059/olj.v26i3.3370



Copyright (c) 2022 Kersten Schroeder, Martha Hubertz, Rachel Van Campenhout, Benny G. Johnson

License URL: https://creativecommons.org/licenses/by/4.0/