Abstract
We present a brief case study of a multi-year learning engineering effort to iteratively redesign the problem-solving experience of students using the “Solving Quadratic Equations” workspace in Carnegie Learning’s MATHia intelligent tutoring system. We consider two design changes, one involving additional scaffolds for the problem-solving task and the next involving a “nudge” for learners to more rapidly and readily engage with these scaffolds and discuss resulting changes in the relative proportion of students who fail to master skills associated with this workspace over the course of two school years.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ritter, S., Anderson, J.R., Koedinger, K.R., Corbett, A.T.: Cognitive tutor: applied research in mathematics education. Psychon. Bull. Rev. 14, 249–255 (2007)
Simon, H.A.: The job of a college president. Educ. Rec. 48(Winter), 68–78 (1967)
Rosé, C.P., McLaughlin, E.A., Liu, R., Koedinger, K.R.: Explanatory learner models: why machine learning (alone) is not the answer. Br. J. Educ. Technol. 50, 2943–2958 (2019)
Fancsali, S.E., Liu, H., Sandbothe, M., Ritter, S.: Targeting design-loop adaptivity. In: Proceedings of the Fourteenth International Conference on Educational Data Mining (2021)
Koedinger, K.R., Corbett, A.T., Perfetti, C.: The knowledge-learning-instruction framework: bridging the science-practice chasm to enhance robust student learning. Cogn. Sci. 36(5), 757–798 (2012)
Aleven, V., McLaughlin, E.A., Glenn, R.A., Koedinger, K.R.: Instruction based on adaptive learning technologies. In: Handbook of Research on Learning and Instruction, 2nd edn. pp. 522–560. Routledge, New York (2017)
Huang, Y., Aleven, V., McLaughlin, E., Koedinger, K.: A general multi-method approach to design-loop adaptivity in intelligent tutoring systems. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 124–129. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_23
Huang, Y., et al.: A general multi-method approach to data-driven redesign of tutoring systems. In: LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), pp. 161–172. ACM, New York (2021)
Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adapt. Interact. 4, 253–278 (1994)
Ritter, S., Murphy, A., Fancsali, S.E., Fitkariwala, V., Patel, N., Lomas, J.D.: UpGrade: an open source tool to support A/B testing in educational software. In: Proceedings of the First Workshop on Educational A/B Testing at Scale (at Learning @ Scale 2020) (2020)
Acknowledgements
This work is sponsored in part by the National Science Foundation under award The Learner Data Institute (Award #1934745). The opinions, findings, and results are solely the authors’ and do not reflect those of the funding agency.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Fancsali, S.E., Pavelko, M., Fisher, J., Wheeler, L., Ritter, S. (2021). Scaffolds and Nudges: A Case Study in Learning Engineering Design Improvements. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_78
Download citation
DOI: https://doi.org/10.1007/978-3-030-78270-2_78
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78269-6
Online ISBN: 978-3-030-78270-2
eBook Packages: Computer ScienceComputer Science (R0)