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Development and Implementation of Adaptive Learning to Engage Learners in Engineering Technology


Adaptive learning uses computers to provide personalized learning pathways for students. This project explores the use of an adaptive learning module implemented in a sophomore level course for civil engineering technology and construction management students with an instructional focus on “Pumps.” The research goal of this case study is to examine student learning and behavioral engagement when an adaptive learning module is introduced. The adaptive learning module was designed to engage students in personalized instruction and was used as a supplement to the instructor’s in-class lectures on the topic. The researchers gathered and analyzed 42 students’ learning data on learning, performance, and user pathways on the adaptive learning platform Smart Sparrow. In total, 81% of students demonstrated mastery across all modules by successfully answering all assessment questions. Furthermore, 65% interacted with at least one adaptive learning module due to assessment, and 24% had more than one interaction, suggesting students were able to efficiently resolve uncertainty within the lesson. Additionally, correct responses for students viewing adaptive content were associated with increased time spent reviewing adaptive content, demonstrating the usefulness of an adaptive learning program. Student responses to a follow-up survey reflect an overall positive experience and also highlight opportunities to improve the module in future iterations.

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  • Alli, N., Rajan, R., & Ratliff, G. (2016). How personalized learning unlocks student success. EDUCAUSE Review, 51(2), 12-21. Retrieved from

  • Bradbury, A. E., Taub, M., and Azevedo, R (2017). The effects of autonomy on emotions and learning in game-based learning environments. Retrieved from

  • Branch, R. (2010). Instructional design: the ADDIE approach. Berlin: Springer.

    Google Scholar 

  • Calvert, S. L., Strong, B. L., & Gallagher, L. (2005). Control as an engagement feature for young children’s attention to and learning of computer content. American Behavioral Scientist, 48(5), 578–589.

    Article  Google Scholar 

  • Chan, A.T., Chan, S.Y., Cao, J. (2001). SAC: a self-paced and adaptive courseware system. In: Proceedings IEEE International Conference on Advanced Learning Technologies, 2001. pp. 78–81. IEEE (2001).

  • Clark, R. M., & Kaw, A. (2020). Adaptive learning in a numerical methods course for engineering: evaluation in blended and flipped classrooms. Computer Applications in Engineering Education, 28(1), 62–79.

    Article  Google Scholar 

  • Cordova, D. I., & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: beneficial effects of contextualization, personalization, and choice. Journal of Educational\ Psychology, 88(4), 715.

    Article  Google Scholar 

  • Dahlstrom, E., Brooks, D. C., & Bichsel, J. (2014). The current ecosystem of learning management systems in higher education: student, faculty, and IT perspectives (p. 3). Research report. Louisville, CO: ECAR, September 2014. Retrieved from EDUCAUSE. CC by-nc-nd.

  • Educause Horizon Report (2020). Teaching and learning edition. Retrieved from

  • Flowerday, T., & Schraw, G. (2003). Effect of choice on cognitive and affective engagement. The Journal of Educational Research, 96(4), 207–215.

    Article  Google Scholar 

  • Hattie, J. (2008). Visible learning: a synthesis of over 800 meta-analyses relating to achievement. Abingdon: Routledge.

    Book  Google Scholar 

  • Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.

    Article  Google Scholar 

  • Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2(2–3), 107–124.

    Article  Google Scholar 

  • Jackson, G. T., & McNamara, D. S. (2013). Motivation and performance in a game-based intelligent tutoring system. Journal of Educational Psychology, 105(4), 1036–1049.

    Article  Google Scholar 

  • Kaw, A., Clark, R., Delgado, E., & Abate, N. (2019). Analyzing the use of adaptive learning in a flipped classroom for preclass learning. Computer Applications in Engineering Education, 27(3), 663–678.

    Article  Google Scholar 

  • Land, S. M. (2000). Cognitive requirements for learning with open-ended learning environments. Educational Technology Research and Development, 48(3), 61–78.

    Article  Google Scholar 

  • Lowendahl, J. M., Thayer, T. L. B., & Morgan, G. (2016). Top 10 strategic technologies impacting higher education in 2016. Research Note G00294732, 15.

  • Markant, D., & Gureckis, T. M. (2014). Is it better to select or to receive? Learning via active and passive hypothesis testing. Journal of Experimental Psychology: General, 143(1), 94–122.

    Article  Google Scholar 

  • Markant, D., Ruggeri, A., Gureckis, T. M., & Xu, F. (2016). Enhanced memory as a common effect of active learning. Mind, Brain, and Education, 10(3), 142–152.

    Article  Google Scholar 

  • Markant, D., DuBrow, S., Davachi, L., & Gureckis, T. M. (2014). Deconstructing the effect of self-directed study on episodic memory. Memory & Cognition, 42(8), 1211–1224.

    Article  Google Scholar 

  • Mettler, E., Massey, C. M., Kellman, P.J. (2011). Improving adaptive learning technology through use of response times. Edited by Carlson, L., Hoelscher, C., & Shipley, T.F. for Expanding the Space of Cognitive Science Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, Boston, Massachusetts.

  • Morrison, G. R., Ross, S. M., Kemp, J. E., & Kalman, H. (2013). Designing effective instruction (7th ed.). Hoboken, NJ: J. Wiley & Sons.

    Google Scholar 

  • NAE Grand Challenges for Engineering (2020). 14 grand challenges for engineering in the 21st century. Retrieved from

  • Nakic, J., Granic, A., & Glavinic, V. (2015). Anatomy of Student Models in Adaptive Learning Systems: A Systematic Literature Review of Individual Differences from 2001 to 2013. Journal of Educational Computing Research, 51(4), 459–489.

    Article  Google Scholar 

  • Nedungadi, P., & Raman, R. (2010, August). Effectiveness of adaptive learning with interactive animations and simulations. In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE) (Vol. 6, pp. V6-40). IEEE.

  • Newmann, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). The significance and sources of student engagement. In F. Newmann (Ed.), Student engagement and achievement in American secondary schools (pp. 11–39). New York: Teachers College Press.

    Google Scholar 

  • Park, O. C., & Lee, J. (2003). Adaptive instructional systems. Educational Technology Research and Development, 25, 651–684.

    Google Scholar 

  • Prusty, B. G., & Russell, C. (2011). Engaging students in learning threshold concepts in engineering mechanics: adaptive eLearning tutorials. Paper presented at the 17th International Conference on Engineering Education (ICEE).

  • Prusty, G.B., Russell, C., Ford, R., Ben-Naim, D., Ho, S., Vrcelj, Z., Marcus, N., McCarthy, T., Goldfinch, T., Ojeda, R., Gardner, A., Molyneaux, T., & Hadgraft, R. (2011). Adaptive tutorials to target Threshold Concepts in Mechanics - a community of practice approach. In Proceedings of the 22nd Australasian Association for Engineering Education Conference (pp. 305-311), Freemantle, WA, Australia.

  • Sabourin, J., Shores, L., Mott, B., & Lester, J. (2012). Predicting student self-regulation strategies in game-based learning environments. In Intelligent Tutoring Systems (pp. 141–150). Berlin: Springer.

    Chapter  Google Scholar 

  • Sawyer, R., Smith, A., Rowe, J., Azevedo, R., & Lester, J. (2017). Is more agency better? The impact of student agency on game-based learning. In International Conference on Artificial Intelligence in Education (pp. 335–346). Berlin: Springer.

    Chapter  Google Scholar 

  • Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational Psychology Review, 19(3), 285–307.

    Article  Google Scholar 

  • Šimko, M., Barla, M., Bieliková, M. (2010): ALEF: a framework for adaptive web-based learning 2.0. In: Reynolds, N., Turcsányi-Szabó, M. (eds.) KCKS 2010. Springer, Berlin, Heidelberg. IAICT, 324, 367–378.

  • Snow, E. L., Allen, L. K., Jacovina, M. E., & McNamara, D. S. (2015). Does agency matter?: Exploring the impact of controlled behaviors within a game-based environment. Computers & Education, 82, 378–392.

    Article  Google Scholar 

  • Specht, M., Kravcik, M., Klemke, R., Pesin, L., & Hüttenhain, R. (2002, May). Adaptive learning environment for teaching and learning in WINDS. In International conference on adaptive hypermedia and adaptive web-based systems (pp. 572-575). Springer, Berlin, Heidelberg.

  • Tabbers, H. K., & de Koeijer, B. (2010). Learner control in animated multimedia instructions. Instructional Science, 38(5), 441–453.

    Article  Google Scholar 

  • Wang, T., Wang, K., & Huang, Y. (2008). Using a style-based ant colony system for adaptive learning. Expert Systems with Applications, 34(4), 2449–2464.

    Article  Google Scholar 

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Correspondence to Carl D. Westine.

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Barclay, N., Westine, C.D., Claris, A. et al. Development and Implementation of Adaptive Learning to Engage Learners in Engineering Technology. J Form Des Learn 4, 107–118 (2020).

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  • Adaptive learning
  • Engineering technology
  • Higher education
  • Engagement