A theoretical framework for researching Learning Analytics
Underpinning an educator’s instructional choices, either consciously or subconsciously, is a belief about how students learn. These beliefs alternately inform and are informed by an understanding of learning theory of which the predominant perspectives are those of behaviourism, cognitivism and constructivism (Ertmer & Newby, 2016). Imported into the digital age, the question invariably arises as to whether these theories continue to be pertinent. While some commentators clearly think so; “There are really no models of e-learning per se — only e-enhancements of models of learning” (Mayes & de Freitas, p.4), others, notably Siemens (2005), have gone so far as to claim “underlying conditions have altered so significantly, that further modification is no longer sensible. An entirely new approach is needed” (p. 4).
Rather than align my conception of e-learning with solely one theory of learning, I agree with Dyke’s (2006) position that there is “an inherent risk of e-learning adopting an almost positivistic perspective that claims to apply particular theoretical positions to designing for learning; in other words a naïve over-prescription of the domain.” (p.97). Valid applications of behaviourist, cognitivist and constructivist theory in the digital domain are well documented and will continue to find an expression (Ertmer & Newby, 2016). When it comes to situating my own study, the overarching question is not so much “which is better?” but “which fits?” From this perspective, context is key and learning outcomes drive the choice and application of learning theory.
In the context of my own study, namely professional development around the use of Learning Analytics (LA), Siemens’ theory of connectivism appears to be an appropriate choice. For each connectivist principle listed, I have given an application in the field of LA.
- Learning and knowledge rests in diversity of opinions. In my research design, I intend to work with a focus group of educators who will analyse LA generated data. As a group, the educators will draw conclusions and plan appropriate interventions. It is anticipated that the diversity of interpretations and perspectives will bring a richness to the discussion.
- Learning is a process of connecting specialized nodes or information sources. While the starting point of connectivism is the individual (as with behaviourism, cognitivism and constructivism), the individual is part of a personal learning network (PLN) which in turn, has organisational links. These relationships are reciprocal in that the conclusions reached by one individual/group can be fed back to another in order to effect systemic change.
- Learning may reside in non-human appliances. Here the connection is self evident. LA generates analytics, generally presented as a dashboard incorporating visualizations, learning recommendations, prompts, rating possibilities and self-assessments (Schumacher & Ifenthaler, 2018). This information takes on value when connected with another node such as the learner, other educators and/or school management.
- Capacity to know more is more critical than what is currently known. The potential of LA to answer some of teaching’s important, yet tricky questions is enormous. Questions such as “which teaching approaches appear to be most effective for this cohort”? “How do engagement levels vary between subjects?
- Nurturing and maintaining connections is needed to facilitate continual learning. In order for teachers to derive maximum benefit from their PLN, they need to be skilled in the art of professional conversations. As teachers are unlikely to learn or contribute if they feel threatened, participants need to feel respected, be able to learn and be motivated to change their practice (Timperley, 2015).
- Ability to see connections between fields, ideas, and concepts is a core skill. My proposed methodology will involve considering student achievement and engagement across a specific cohort. Rather than analyse data at the level of individual subjects, I am interested in seeing what interventions teachers propose based on the analysis of data from an entire year level.
- Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities. By presenting just-in-time, actionable insights (Schumacher & Ifenthaler, 2018), LA offers accurate and up-to-date knowledge. Many software applications have the added ability to compare and contrast student performance over extended periods of time.
- Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision. The easy accessibility of LA means that teachers can regularly revisit data in order to reflect on the success or otherwise of planned interventions. Regular analysis through the lens of professional conversations will allow teachers to continually fine-tune their instruction.
References
Dyke, M., Conole, G.,, Ravenscroft, A. & de Freitas, S. (2006) Learning theory and its application to e-learning. Contemporary Perspectives in E-Learning Research: Themes, Methods and Impact on Practice. Retrieved from https://www-taylorfrancis-com.ezproxy.auckland.ac.nz/books/e/9780203966266
Ertmer, P. & Newby, T. (2016). Learning Theory and Technology: A reciprocal relationship. The Wiley Handbook of Learning Technology. Retrieved from https://onlinelibrary.wiley.com/doi/book/10.1002/9781118736494
Mayes, T. and de Freitas, S. (2004). Review of e-learning theories, frameworks and models. JISC e-Learning Models Desk Study, 1, 1–43. Retrieved from
Schumacher, C. & Ifenthaler, D. (2018) Features students really expect from learning analytics. Computers in Human Behaviour, 78, 397–407. Doi: 10.1016/j.chb.2017.06.030
Siemens, G. (2005). Connectivism: A Learning Theory for the Digital Age. Retrieved from: https://www.learningnetwork.ac.nz
Timperley, H. (2015) Professional Conversations and Improvement-Focused Feedback: A Review of the Research Literature and the Impact on Practice and Student Outcomes. Retrieved from https://www.aitsl.edu.au
