Double, Double Toil and Trouble: Using Interactive Qualitative Analysis to Understand Non-Major Accounting Students’ Learning
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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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 | 0.050 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".