Are You Making Learning Too Easy? Effects of Grouping Accounting Problems on Students' Learning
Bibliographic record
Abstract
ABSTRACT Prior accounting education research claims learning outcomes are improved by grouping together similar accounting practice problems rather than presenting such problems in an interleaved order. The present study revisits this prior research by asking whether making initial problem solving easier inadvertently leads to less durable longer-term learning. The evidence in the present study confirms that grouping practice problems helps students complete problem-solving practice in less time and with greater accuracy; this performance improvement is evident on a test given immediately after problem-solving practice. However, grouping together similar practice problems significantly reduces longer-term learning, as measured by a delayed test given one week after problem-solving practice. Further, the present study shows the efficient problem-solving experience created through grouping practice problems fools students into thinking they will be able to successfully solve similar problems in the future, and it also misguides them into believing they will need to study less when preparing for an upcoming test involving similar problems. This study raises the possibility that initial instruction is most effective when it does not simplify but rather presents learners with a desirable level of difficulty.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".