Student Perceptions of Learning Technologies in Introductory Accounting Courses
Bibliographic record
Abstract
The past two decades have seen a dramatic increase in the development and use of various classroom technologies purported to enhance student learning. Accompanying this has been a large volume of studies aimed at assessing the effectiveness of these technologies from a variety of disciplines. This study contributes to this discourse and the accounting education literature in particular by examining student perceptions of the effectiveness of multiple technologies used in an introductory management accounting course. Perceptions of the effectiveness of a traditional textbook were also collected. Students were then asked to compare their experience in this course to that of the prerequisite introductory financial accounting course where no learning technologies were used. This study shows that students perceive practice problems and problem-based lectures to be the most effective learning activities whether they are employed using technology or not. Two learning technologies to be particularly effective – online practice problems/quizzes and video lectures- but all learning technologies tended to support superficial rather than deep learning approaches.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".