
Advances in learning analytics focused on understanding and shaping the learning environment would seem to have a natural fit with regard to text based online learning transactions? In this regard, Terry Anderson (2017) has suggested that analytics can support group discussion through monitoring the activities and progress of participants. However, the key in my mind is to recognize the role of a coherent theoretical approach. Learning analytics must be driven by legitimate educational dynamics. We must be sure to measure what is important to the goals of a learning experience. This is why it is essential to have a meaningful and defensible theoretical framework that can guide us to the important dynamics of deep and meaningful learning. Only then can learning analytics provide powerful information in shaping a worthwhile learning experience. In this regard, learning analytics can be extremely useful in assessing the efficacy and efficiency of a complex community of learners. More specifically, learning analytics can be extremely beneficial in revealing the personal and collaborative complexities of a community of inquiry.
In terms of the Community of Inquiry framework (CoI), analytics can provide invaluable diagnostic information with regard to the constructive progression of the inquiry process shaped by social and teaching presence. Diagnostic analytics have considerable practical potential to assess leadership (teaching presence) and a trusting environment (social presence) to ensure the successful development of collaborative inquiry (cognitive presence). Analytics can greatly inform teaching presence by achieving a balance of the presences and making decisions with regard to the progression of the inquiry process. Learning analytics in support of creating a CoI has the powerful potential to place participants in charge of their learning through shared metacognitive awareness. Considering the dynamic complexity of a community of inquiry, learning analytics could be an invaluable tool for the efficiency and effectiveness of collaborative approaches to thinking and learning.
Insight into the use of learning analytics to inform a community of inquiry has been provided by recent studies. This exploratory research using automated content analysis focused on cognitive presence manifested through the phases of inquiry. First, a study by Waters et al. (2015) concluded that the structural classification of cognitive presence showed promise in automating the detection of inquiry in online discussions. A similar study by an expanded group of researchers from the previous study concluded that this approach showed great potential and could be used “to provide a real-time overview of the progress for a group of students and to point out the students for which progress estimates are uncertain” (Kovanovic, 2016, p. 23).
Learning analytics has enormous potential to help educators understand the characteristics of critical discourse as well as identify areas after the fact that could be improved through redesign. That said, as stated previously, it seems to me that learning analytics is dependent upon a clear framework of teaching and learning such as that provided by the CoI framework. A recent report (West et al., 2018) explored the possible use of learning analytics and the CoI framework to develop metrics to improve outcomes. They concluded that there “were very few data points that arose... that could not be mapped to the CoI” (West et al., 2018, p. 26). These preliminary results indicate the potential of merging learning analytics with the CoI framework.
When we first began developing the CoI framework we recognized that coding transcripts was impractical in terms of providing timely guidance to a community of inquiry. While automated coding was not realistic at the time, I had a belief that one day this would be a possibility. I can safely say that the time has arrived where learning analytics can provide practical diagnostics with regard to the dynamics of a community of inquiry. Hopefully other researchers will join the challenge to use learning analytic assessments to understand the dynamics of a community of inquiry in real time. In conclusion, I strongly believe learning analytics is one of the more exciting lines of innovative research that can have a practical influence on the effectiveness of communities of inquiry.
Kovanovic, V.,Joksimovic, S. Waters, Z., Gasevic, D., Kitto, K., Hatala, M., & Siemens, G. (2016). Towards automated content analysis of discussion transcripts: A cognitive presence case. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, UK, April. Retrieved January 29, 2018 from: http://vitomir.kovanovic.info/public/papers/Kovanovic et al. - 2016 - Towards Automated Content Analysis of Discussion Transcripts.pdf
Waters, Z., Kovanovic, V., Kitto, K., & Gasevic, D. (2015). Structure matters: Adoption of structured classification approach in the context of cognitive presence classification. Paper presented to 11th Asia Information Retrieval Societies Conference, Brisbane, QLD, Australia, December. Retrieved January 29, 2018 from: https://www.researchgate.net/publication/283463324
West, D., Luzeckyj, A., Searle, B., Toohey, D., & Price, R. (2018). The Use of Learning Analytics to Support Improvements in Teaching Practice. Innovative Research Universities. Melbourne, Australia. Retrieved May 17 from: https://www.researchgate.net/publication/324979461
Professor Emeritus, University of Calgary
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