“Can you hear me? Wait, are you listening to me?” An evaluation of the use of audio-journals as a data-collection method in interpretive inquiry and the implications for classroom assessment.
Bibliographic record
Abstract
My hermeneutic inquiry of grade eleven students’ experiences of classroom talk in English Language Arts came out of my concern about the ways that formal testing scenarios sometimes disadvantage students who need some more time to gain mastery in writing and comprehension skills. There is increasing discussion in educational research about equity in teaching contexts, and not just equality. The assumption influencing my research, based on my teaching experience, was that classroom talk offers students some kind of equity. Classroom talk can sometimes scaffold students who are not confident in written expression. When students have opportunities to gain competence in oral expression and sort out understanding of subject matter through dialogic contexts, their learning can be enhanced. Research on dialogic learning supports this, but we still have much to understand regarding students’ views on dialogic contexts. My emphasis was on student voice, literal and metaphorical, so I proposed to use audio-journals as a way of listening closely to what students had to say about their experiences. This paper evaluates the effectiveness of this method of data collection, considers the advantages and shortcomings, and suggests ways in which this methodological tool can have practical application in general classroom assessments.
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 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 | 0.275 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".