As the previous post indicates, I’m having a closer look at decision-making and uncertainty with regard to learning analytics. The following is really just some thinking out loud so apologies in advance for any errors or misinterpretations. Note that in this post I’m thinking about learning analytics through a learning and teaching lens.
It has been said that people based their decisions on their internal representations of the context and not the context as sensed (Zachary, Rosoff, Miller, & Read, 2013). These internal representations of the context are richer, more stylized, incorporate multiple levels of abstraction, and take on a structure that enables rapid retrieval of relevant decision-making heuristics and procedures (Zachary et al., 2013). This is known as recognition-primed decision-making (RPD).
According to Zachary et al. (2013) there are four context awareness levels:
- Perception. What is there.
- Comprehension. What does it mean.
- Projection. How might it evolve.
- Sense-making. How does it make sense.
This has some alignment with the situation awareness levels as described by Endsley that I’ve talked about in an earlier post (Endsley, 2001):
- Level 1. Perception of the elements in the environment
- Level 2. Comprehension of the current situation
- Level 3. Projection of the future status
Zachary et al. (2013) say that the situation awareness theory and RPD work best in contexts that involve well-defined problem-solving in bounded problem domains, such as piloting aircraft and controlling complex mechanical systems. With regard to learning analytics, I’m seeing this as how it can contribute to operational, perhaps, real-time contexts inside the course, during the term.
They also talk about narrative reasoning where the observer/participant constructs, analyses and explains complex situations through a narrative (story telling) process (Zachary et al., 2013). They go on to say that people almost universally use story narratives to represent, reason about, and make sense of contexts involving multiple interacting agents, using motivations and goals to explain both observed and possible future actions (Zachary et al., 2013). With regard to learning analytics, I’m seeing this as how it can contribute to the retrospective understanding and sharing of what transpired within the operational contexts.
The message here for me is that learning analytics should aim to contribute to both operational/real-time components, and the reflective/retrospective components, as they are not mutually exclusive. This gets very interesting from an information systems and complexity science perspective when we start to think about affordances for distributed cognition and disintermediation.
Endsley, M. R. (2001). Designing for situation awareness in complex systems. Paper presented at the Proceedings of the Second International Workshop on symbiosis of humans, artifacts and environment.
Zachary, W., Rosoff, A., Miller, L. C., & Read, S. J. (2013). Context as a Cognitive Process: An Integrative Framework for Supporting Decision Making. Paper presented at the STIDS.