The Surprising Foil to Online Education: Why Students Won’t Give Up Paper Textbooks
Bibliographic record
Abstract
Purpose of the Study. Digital resources are an integral part of online education. Although advocates of digitized information believe that millennial students will embrace the paperless classroom, this is not proving to be the case. This research addresses gaps in our understanding of student resistance to giving up paper-based learning resources by examining attributes of the paper textbook that are perceived as necessary for knowledge transfer and that are not present in digital information modalities. Method/Design and Sample. Phase 1 used focus groups to identify the content of items that were incorporated into a quantitative instrument in phase 2. A sample of 386 undergraduate students taking marketing courses at a Canadian urban university completed the online survey. We then used Confirmatory Factor Analysis to test the factors linked to resistance to discontinuing paper textbooks. Results. Students’ resistance to giving up the paper textbook positively relates to the way in which the paper textbook facilitate learning and study processes, is permanent and under the students’ control during and after the course is finished. The fluid and dynamic nature of digital content compared to the more consistent and predictable nature of information on paper appears to be a barrier to the acquisition of knowledge for the purpose of assessment. Value to Marketing Educators. This study provides insights into the underlying reasons for student resistance to discontinuing paper-based learning resources, and benefits marketing educators and developers of educational content by outlining ways to improve student learning success.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".