Symposium 5: Variations in the Experience of Phenomenographic Research
Bibliographic record
Abstract
Phenomenography originated in the field of Education in the 1970s. At this time, a series of studies were designed to understand why some students appeared to learn more deeply and easily than others (Marton, 1994, Marton & Säljö, 1976). The researchers gathered the different conceptualizations described by the research participants, analyzed their similarities and differences, and noticed that what emerged was a qualitatively limited number of ways of conceptualizing phenomenon. Further, they discovered that these conceptualizations were structurally and referentially related and that these relationships could be mapped hierarchically forming what became known as outcome spaces (Dahlin, 2007). In general, phenomenography aims to find the “variation and the architecture of this variation in terms of different aspects that define the phenomena” (Marton & Booth, 1997, p. 117). Since those early days, phenomenographic methodology has been used in a variety of ways, sometimes combining it with of secondary methods. Hasselgren and Beach list (1997) identify five different types of phenomenography: experimental, discursive (pure), naturalistic, hermeneutic, and phenomenological. The aim of this symposium is to discuss the variation in ways that phenomenography can be applied to research in networked learning. A secondary goal is to open a discussion on the issues and challenges presented by this methodology. The four authors of the papers for this symposium have all taken a discursive (pure) phenomenological approach to their research. What this means is that rather than taking an experimental approach in which learning outcomes were analyzed and measured, as in the experimental approach, the researchers examine conceptions outside of active intervention. That is, the researchers examine how learners conceptualize phenomena occurring in the general learning environment.
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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 | 0.125 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".