Sink or Skim: Textbook Reading Behaviors of Introductory Accounting Students
Bibliographic record
Abstract
Despite the significant emphasis that most instructors place on textbooks in introductory accounting courses, little research exists to describe how students interact with their textbooks. Using learning journals, 172 undergraduate students provided detailed, real-time accounts of their experiences with 13 chapters of an introductory financial accounting textbook. Using the method of grounded theory, supplemented with quantitative tests of association, this study begins to characterize textbook use from a student perspective. Results indicate that, for students, reading is a motivated behavior, with the specific motives varying across different groups of students and leading to different consequential actions. Academically strong students appear to read with the primary goal of understanding assigned material, as evidenced by their willingness to (1) engage in reading before the related material is covered in class, (2) persist when material becomes difficult, and (3) establish defined action plans that promptly resolve confusion. In contrast, weaker students appear to read with the primary goal of reducing anxiety, by deferring reading and terminating it when comprehension becomes difficult. The findings of this study are used to create instructional guidance that instructors can provide to students and to direct future research by outlining important and interesting questions requiring further investigation.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".