Using Hypertext in Instructional Material: Helping Students Link Accounting Concept Knowledge to Case Applications
Bibliographic record
Abstract
We study whether the linking facility that is enabled in hypertext can enhance students' ability to link accounting concepts to practical case situations. We expected that by linking accounting concepts to related case facts in instructional material, hypertext will allow students to more readily understand how accounting concepts apply to practical case situations. We predicted that this understanding would be apparent when students later attempt to apply accounting concepts to new accounting cases. To test our predictions, we conducted a laboratory experiment in which accounting students studied instructional materials that showed how concepts involving asset characteristics are used to determine whether expenditures are appropriately classified as assets (or expenses). The experimental design comprised three learning conditions: the student-generated condition in which the instructional materials required students to generate hypertext links between concepts and cases, the instructor-provided condition in which the instructional materials required students to explore hypertext links already inserted between concepts and cases, and the control condition in which students studied the same instructional materials without generating or being provided hypertext links. After spending equal amounts of time working with the concepts and cases in the instructional material, all participants analyzed new test cases by identifying appropriate links between specific case facts and the applicable concepts (i.e., asset characteristics). Our analyses focus on these subsequent applications of concepts to test cases. Results revealed that the application of accounting concepts to test cases was greater in the instructor-provided condition than the control condition. Results also indicated that the application of accounting concepts to test cases was greater in the student-generated condition than the instructor-provided condition, but only when students were able to generate appropriate links when learning with hypertext. When inappropriate hypertext links were generated, the application of accounting concepts to test cases in the student-generated condition was similar to that in the control condition.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".