Abstract
The advances in technology continually push at the boundary of what is possible in online learning environments. Digital learning generates data at scale that can be analyzed to gain new insights into the learning process, which in turn sparks further changes in technology. Adaptive instructional systems have been growing in type and complexity, which also increases the scope of the technology and the teams who work to develop them. In this time of intense exploration and innovation in learning science and technology, it is also imperative to put in place a system of ethics to center this innovative spirit on the intended user: the learner. I propose that learning engineering provides a purpose to advocate for the best interests of the learner as the Learning Engineering Process is carried out. This practitioner purpose can help the learning engineer develop an ethical voice and engage in a dialogic ethic as new technology is being developed. I use a case study on the development and improvement of adaptive activities to illustrate both the Learning Engineering Process as well as how this process supports an ethical practice. In this paper I situate learning engineering in an ethical framework and provide a contextual example to spark discussion on the role of ethics in an increasingly complex learning ecosystem.
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Notes
- 1.
Acrobatiq originated as a start-up from Carnegie Mellon University’s Open Learning Initiative in 2013. Acrobatiq was acquired by VitalSource Technologies in 2018.
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Acknowledgements
I would like to thank Erin Czerwinski for her mentorship in learning engineering when I began in the role, Bill Jerome and Benny Johnson for their continued enthusiasm for learning engineering, and Jim Goodell and Aaron Kessler for their feedback on the idea for this paper. I would also express my gratitude to the ICICLE group for providing a community of practice and for the continued development of the field.
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Van Campenhout, R. (2021). Learning Engineering as an Ethical Framework. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Design and Evaluation. HCII 2021. Lecture Notes in Computer Science(), vol 12792. Springer, Cham. https://doi.org/10.1007/978-3-030-77857-6_7
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