Bibliographic record
Abstract
Keywords: education technology, OER, data analytics, business education, faculty support This study examined an initiative at the researcher's institution to provide an online, open source data analytics curriculum for courses at the university level. Semistructured interviews were conducted with seven professors of accounting at universities in the US and Canada to explore their experiences while adopting this open educational resource to teach data analytics skills and tools. Transcripts were analyzed using Interpretive Phenomenological Analysis (IPA) to identify themes. Three major themes emerged from the analysis: instructor responses to a changing professional field for which they were preparing students; adapting approaches to instruction to meet changing demand; and navigating the university's institutional environment and the marketplace of data analytics tool providers to promote diffusion of new teaching practices. These findings were considered within the context of existing literature on education technology and Diffusion of Innovations Theory (IDT) to identify opportunities for next practices and to support adoption across departments and institutions. Findings of this study suggested formalizing market research to better understand workplace demands, identifying early adopters of next practices and elevating their work, tying academic innovation to education research, and deploying existing administrative personnel to support technology adoption.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".