Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasingly, students learn to use computer-based tools to support their coursework in science, engineering, technology, and mathematics (STEM) courses. A common educational strategy is to introduce the new tool along with new course material, having students learn the tool and the course material simultaneously. We looked at how two simultaneous learning goals affect students’ learning of a computer algebra system (CAS), a tool increasingly used in STEM education. The study described involved teaching a group of students some basic CAS commands through one of two sets of instructional materials that used different contextual examples, based on either familiar or unfamiliar mathematics, which theoretically, based on predictions of cognitive load theory, imposed different levels of cognitive load. The students’ learning of the CAS concepts was tested and their workload during learning and testing was measured. We showed that the students in the familiar math case performed better on a test of CAS concepts and that they reported a lower workload when completing the test. This result was consistent with the predictions of cognitive load theory. The results of this study point to the potential importance of managing the cognitive load of instructional material when training students in the use of advanced educational software systems.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it