Symposium 5: Perceptions of guest lecturers' impact on online learning communities
Bibliographic record
Abstract
A phenomenographic approach to case study is presented as a proposed methodology to researching guest speakers’ impact in networked learning communities. This work-in-progress paper outlines the proposed use of case study and phenomenography in exploring learners’ experience of guest speakers’ impact on students’ in online learning communities in higher education (HE). The rationale for this chosen methodology is outlined, as well as its epistemological and ontological underpinnings. The inclusion of guest speakers in higher education courses, such that they share experience with and learn with students and instructors through synchronous or asynchronous communication, is an area little studied to date. Little is known about guest speakers’ impact on learning beyond a few documented benefits afforded by guest speakers in face-to-face learning environments. For example, guest speakers bridge theory and practice (praxis) through experiences they share with the class. Data collected from semi-structured interviews in each case will be formulated into outcome spaces. This multiple case study will generate outcomes spaces that depict the categories of description as provided by participants. Following Åkerlind’s (2008) method of focusing on student experience, the outcome space will be representative of participants’ variation of experience, from students’ perspectives. The project’s issues and challenges, as understood to-date, are outlined. Proposed next steps are identified. This paper presents an alternate approach to the use of phenomenography in researching learning in a student-centred phenomenographic approach to case study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".