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Record W7133099758

The Development of Persona as a Tool for Online Learning

2022· dissertation· W7133099758 on OpenAlexaff
Teresa Lynn Avery

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPersonaContext (archaeology)Representation (politics)Online learningPlan (archaeology)Virtual learning environmentConstructivist teaching methodsLearning environment
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.366
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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