Enhancing Students’ Accuracy in Tests and Understanding of the Main Ideas of Reading Materials by Retrieval Cues in the Textbook
Bibliographic record
Abstract
Since understanding reading assignments is important to succeeding in school, improving the way that text is arranged in books would be an efficient way to help students better understand the material and perform well on tests. In this study, we asked students to read two original and two rearranged historical passages, in which rephrased questions from the test of the passage were embedded in the reading. Those restated questions were embedded into the corresponding sections in the text where the answers for the questions were located. After the reading, they were tested on their understanding of the material with multiple choice questions. All participants took an identical test. According to the results, both groups’ accuracy was higher for the questions of the two rearranged passages in each group. In other words, the textbook makers should consider putting retrieval cues for the test into the textbook in order to make the textbooks more readable. Thus, the students’ retrieval of the concept and their performance on the test will be enhanced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".