Bibliographic record
Abstract
The purpose of this thesis was to explore if learner personas could be used as a tool for learner-centred online learning. Personas can be a powerful design tool when applied to online learning design and could assist instructors in envisioning and planning for distinct “archetypes” of online learners and is one way that can be used to characterize the needs, goals, experience and accessibility requirements of learners (Lilley et al., 2012; Pyper et al., 2011; Rogers et al., 2011; Ward & Parr, 2010). The study intended to create a compelling and approachable representation of data based on representative data from observations made while doing a comprehensive case study. Personas can be tools or a broader lens that instructors can use to extend or complement their teaching, adding to the constructivist and collective knowledge of the group, ideally interrogating ideas within the online environment (Dalley-Hewer et al., 2012; Garrison et al., 2001; Scardamalia & Bereiter, 2005). Accordingly, a course developed as part of a COI should provide a space for “meaningful discourse and [to] develop personal and lasting understandings of course topics” (Garrison et al., 1999; Oztok et al., 2014; Rourke & Kanuka, 2009). This study found that using distinct personas––as a way to envision the learner’s individual journeys and engagement patterns––can guide the instructor’s thoughts about how the learners’ themselves add to their own personal understanding. The personas also offered a way for instructors to dialogue with colleagues in context about similar learners across other course offerings, providing a practical way for instructors and designers to look at their courses from the perspective of the learner (Jonassen et al., 1995; Swan et al., 2009a; Ward, 2010). The study used log-file data from threaded conversations, student participatory behaviours, and student reflective journals, along with instructor interviews and insight from a comprehensive literature review. This contributed to the development of five specific personas, created from specific facts as general archetypes. The next steps in this study included how these findings about one course offering can be translated into future studies and tool development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".