Using Learning Analytics for Personalised, Data-Informed Feedback and Support: Studies of Impact, Challenges, and Future Directions
A special issue of Education Sciences (ISSN 2227-7102).
Deadline for manuscript submissions: closed (10 September 2023) | Viewed by 4165
Special Issue Editors
Interests: learning analytics and educational data mining; technology enhanced teaching in higher education; student engagement and transition; student feedback literacy; collaborative learning; design thinking; technology acceptance/adoption of information systems
Interests: learning analytics; feedback; self-regulated learning; learner emotions; student engagement; technology adoption; learning design
Special Issue Information
Dear Colleagues,
With the maturity of learning analytics (LA) as a field, many tools and systems exist for data-informed feedback and support. The vast range of these interventions includes fully-automated learning analytics dashboards and recommender systems (Sahin & Ifenthaler, 2010), online tutoring systems (e.g., Heffernan & Heffernan, 2014), web-based portals for tailored support and feedback (e.g., Matz et al., 2021), and semi-automated instructor-in-the-loop systems (e.g., Liu et al., 2017; Pardo et al., 2018), and writing analytics feedback tools (e.g., Knight et al., 2020). However, in comparison with developmental work, research evaluating the effectiveness of learning analytics feedback interventions is lagging (Viberg et al., 2018). Although feedback generally remains a significant factor in improving student learning, its effects are not uniform (Wisniewski, Zierer & Hattie, 2020). Regarding feedback drawing on learning analytics, there is some evidence of the same effect (e.g., Lim et al., 2021), but this is insufficient. More evidence is needed to understand the nuanced impact of data-informed, automated feedback on students with differing motivations, educational backgrounds, prior knowledge, and other learner characteristics. Understanding differences in impact due to learner characteristics is important for designing effective personalised feedback and support systems based on individuals’ learner data.
Over the years, the LA field has made repeated calls for the alignment of learning design and learning analytics (Hernandez-Leo et al., 2019; Lockyer, Heathcote, & Dawson, 2013). Learning analytics tools that began as research projects are finding their way into actual teaching and learning contexts, from project to product (Buckingham Shum, 2022). Wielding such tools in the wild brings a host of challenges, not least of which is knowing how to embed the tool within the context of the teaching and learning environment. Given the importance of embedding learning analytics into learning design, more research is needed to demonstrate how educators can use learning analytics feedback tools in such a way as to promote students’ deep engagement with their personalised feedback.
This Special Issue aims to investigate the key elements of feedback, approaches to data-informed feedback, the importance of developing data-informed feedback literacy among students and teachers, and the impacts, associated challenges, practices, technologies, and future directions. We invite empirical contributions in the research area of personalised, data-informed feedback. These include (but are not limited to) the following:
- Feedback nudges
- Peer feedback
- Video feedback
- Feedback rubrics
- Feedback and learning design
- Feedback and belonging
- Feedback and student engagement
- Feedback and self-regulated learning
- Feedback literacy (emotional response to feedback) of student and teacher
- Challenges in adopting data-informed feedback
- Dialogical feedback
We look forward to receiving your contributions.
References
Buckingham Shum, S. (2022). Embedding learning analytics in a University: Boardroom, Staff Room, Server Room, Classroom. In O. Viberg & A. Gronlund (Eds.), Practicable Learning Analytics. SpringerNature.
Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching. International Journal of Artificial Intelligence in Education, 24(4), 470-497. https://doi.org/10.1007/s40593-014-0024-x.
Hernandez‐Leo, D., Martinez‐Maldonado, R., Pardo, A., Munoz‐Cristobal, J. A., & Rodríguez‐Triana, M. J. (2019). Analytics for learning design: A layered framework and tools. British Journal of Educational Technology, 50(1), 139-152.
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439-1459.
Knight, S., Shibani, A., Abel, S., Gibson, A., Ryan, P., Sutton, N., Wight, R., Lucas, C., Sandor, A., Kitto, K., Liu, M., Mogarka, R. V., & Buckingham Shum, S. (2020). AcaWriter A learning analytics tool for formative feedback on academic writing. Journal of Writing Research, 12(1), 141-186. https://doi.org/https://doi.org/10.17239/jowr-2020.12.01.06.
Lim, L.-A., Gentili, S., Pardo, A., Kovanovic, V., Whitelock-Wainwright, A., Gasevic, D., & Dawson, S. (2021). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 101202. https://doi.org/https://doi.org/10.1016/j.learninstruc.2019.04.003.
Liu, D. Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-driven personalization of student learning support in higher education. In A. Peña-Ayala (Ed.), Learning Analytics: Fundaments, Applications, and Trends, Studies in Systems, Decision and Control, 94 (pp. 143-169). https://doi.org/10.1007/978-3-319-52977-6.
Matz, R. L., Schulz, K. W., Hanley, E. N., Derry, H. A., Hayward, B. T., Koester, B. P., Hayward, C., & McKay, T. (2021). Analyzing the efficacy of Ecoach in supporting gateway course success through tailored support. In LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), April 12–16, 2021, Irvine, CA, USA. (pp. 216-225). ACM. https://doi.org/https://doi.org/10.1145/3448139.3448160.
Pardo, A., Bartimote-Aufflick, K., Buckingham Shum, S., Dawson, S., Gao, J., Gašević, D., ..., & Vigentini, L. (2018). OnTask: Delivering Data-Informed Personalized Learning Support Actions. Journal of Learning Analytics, 5(3), 235-249. https://doi.org/http://dx.doi.org/10.18608/jla.2018.53.15.
Sahin, M., & Ifenthaler, D. (2021). Visualizations and Dashboards for Learning Analytics: A Systematic Literature Review. In M. Sahin & D. Ifenthaler (Eds.), Visualizations and Dashboards for Learning Analytics (pp. 3-22). Springer International Publishing. https://doi.org/10.1007/978-3-030-81222-5_1.
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110. https://doi.org/https://doi.org/10.1016/j.chb.2018.07.027.
Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10, 3087. https://doi.org/10.3389/fpsyg.2019.03087.
Dr. Amara Atif
Dr. Lisa-Angelique Lim
Dr. Caitlin Hayward
Guest Editors
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Keywords
- learning analytics
- personalized
- data-informed feedback
- student engagement
- feedback interventions
