What self-regulation can tell us about analytics

Learners who see technologies as being beneficial to learning are likely to use them less, while those with poor strategies for learning will use them more. This may appear counter intuitive and challenges common perceptions used to determine the success of learning technologies, but essentially insists on quality rather than quantity of interaction which makes sense. The findings behind this emerge from the literature on self-regulated learning and cognitive engagement, which highlight several non-linear relationships between perception, learning strategies and the use of tools. In this article I will summarise three pieces of research and discuss their implications for common metrics used to evaluate learning technologies.

Winne and Hadwin (1998) argue that studying can be distinguished from learning and ‘compels students to engage in complex bundles of goal-directed cognitive and motivational processes’ (pg. 278). Self-regulated studying is demonstrated to be more effective than normal studying, where self-regulation involves a four stage process: (1) definition of task; (2) goal setting and planning; (3) enactment; and (4) adaptation. The stages are recursive – products and evaluations from one stage feeds into the next – and weakly sequenced – the stages can overlap, skip, or go back – and while these are expected to leave traces in the forms of products the recursive nature of the system make this complex to research. Davidson and Sternberg (1998) argue that learners, or experts, who apply effective metacognitive strategies spend more time in the planning stage and as a result less time in the enactment stage – they are also likely to make more effective evaluations and adaptations because of standards identified during planning. Less skilled problem solvers spend more time in the enactment stage due to reduced planning that is often caused by insufficient domain or metacognitive knowledge. Unless the novice learner quits the task due to frustration it is likely that overall expert problem solvers are likely to spend less time on the problem and achieve greater results. 

Approaches to study and teaching (Richardson) - Winne & Hadwin SRL

Winne and Hadwin, 2008

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Docs, zaps, and rows – starting the #PhD

Been a few years in the making but I’ve started as a part-time PhD student at University of Melbourne researching engagement profiles and learner analytics. Before I get into a study period I like to get set up with a group of tools and a workflow that let me know I’m in study mode, and hopefully aid the process at the same. Embarking on a 6-year project has led me to infer I need a little bit more structure than in my previous studies – for example using reading logs to remember what I’ve read rather than rely on my brain, which I need to keep free for research or ordering coffee.

I had a simple set of criteria for the tools:

  1. Should be cloud-based (or syncable) – I don’t always use the same laptop
  2. Mobile app preferred particularly for notes and ideas which I may have on the go
  3. Free wherever possible
  4. Very little setup or admin.

The first 2 years in my case are a literature review leading up to confirmation so I’ve devised the following workflow across a range of different platforms:

Blank Flowchart - New Page (1)

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