Double, Double Toil and Trouble: Using Interactive Qualitative Analysis to Understand Non-Major Accounting Students’ Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the implementation of the methodology, Interactive Qualitative Analysis (IQA) (Northcutt & McCoy, 2004) during the COVID-19 pandemic, to understand how non-major accounting students learn Accounting 101 in a threshold concepts-inspired tutorial programme. Even though IQA is a predominantly qualitative method, it incorporates quantitative data with qualitative data systematically. These data collection and data analysis procedures are a means of aiding participants in a focus group to describe their experiences with a phenomenon, to name these experiences and to then describe the relationships between these named experiences. The objective of the IQA methodology is to create a picture, a Systems Influence Diagram (SID), representative of the mind map of the focus group participants, with regard to the phenomenon outlined in the issue statements. A summary of theoretical codes used to capture the relationships between affinities named, an Inter Relationship Diagram (IRD), is used to draw the SID. IQA requires the researcher to document each step of the research process, whilst acting as a facilitator by teaching the participants the IQA process on how to generate and analyse the data that they have generated, thereby minimising the researcher influence. This study provides qualitative research conducted in the fields of education and accounting, with a qualitative methodological approach, being Interactive Qualitative Analysis (IQA).
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it