Bringing Abstract Academic Integrity and Ethical Concepts into Real-Life Situations
This paper reports the learning analytics on the initial stages of a large-scale, government-funded project which inducts university students in Hong Kong into consideration of academic integrity and ethics through mobile Augmented Reality (AR) learning trails—Trails of Integrity and Ethics (TIEs)—accessed on smart devices. The trails immerse students in collaborative problem solving tasks centred on ethical dilemmas, addressed in real, actual locations where such dilemmas might arise, with contextually appropriate digital advice and information available on hand. Students play out the consequences of their decisions which help reinforce the links between the theoretical concept of academic integrity and ethics and the practical application in everyday contexts. To evaluate the effectiveness of the TIEs, triangulation of different sets of data is adopted and these datasets include user experience surveys, qualitative feedback, clickstream data, and text mining of pre-/post-trail discussion. Thousands of students’ responses and related data gathered are analysed to ascertain the effectiveness of these mobile learning trails in enhancing students’ awareness of AIE issues. The positive learning outcome of the TIEs suggests that this approach can be adopted and applied to a wider scope of the academic curriculum and co-curriculum.
KeywordsAcademic integrity Augmented reality Ethics Learning analytics Learning trail Mobile learning
- Chan, J., Chiu, R., Ng, G., & Kwong, T. (2015). How clickstream tracking helps design mobile learning content. International Journal of Humanities Social Sciences and Education (IJHSSE), 2(7), 95–104.Google Scholar
- Dicheva, D., Dichev, C., Agre, G., & Angelova, G. (2015). Gamification in education: A systematic mapping study. Educational Technology & Society, 18(3), 75–88.Google Scholar
- Gibson, D., & Ifenthaler, D. (2016). Preparing the next generation of education researchers for big data in higher education. In B. K. Daniel (Ed.), Big data and learning analytics in higher education (pp. 29–42). Cham: Springer International Publishing.Google Scholar
- Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher (Education ed.). Austin: The New Media Consortium.Google Scholar
- Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2015). The NMC horizon report: 2015 (Museum ed.). Austin: The New Media Consortium.Google Scholar
- Johnson, L., Adams Becker, S., & Freeman, A. (2013). The NMC horizon report: 2013 (Museum ed.). Austin: The New Media Consortium.Google Scholar
- Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2011). The 2011 horizon report. Austin: The New Media Consortium.Google Scholar
- Kuhlmann, T. (2009, July 14). Build branched e-learning scenarios in three simple steps. Retrieved from https://blogs.articulate.com/rapid-elearning/build-branched-e-learning-scenarios-in-three-simple-steps/.
- Li, P., Kong, S.C., Guo, C., Wong, E., Chan, J. (2015). Enhancing academic integrity online via blended learning and discussion analytics. Proceedings of the eLearning Forum Asia 2015, Singapore.Google Scholar
- Papamitsiou, Z., & Economides, A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.Google Scholar