An interactive computer-based tutorial for MATLAB
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
The paper describes the implementation of an interactive computer based tutorial for MATLAB. Students are engaged in learning new concepts and syntax with video, audio, and interactive exercises. The interactive exercises, which are a distinguishing feature of the tutorial, use a specially designed exercise window which has a background software interface to MATLAB. The learner is challenged with problems in the exercise window immediately after covering new concepts. Hints, example solutions, multiple choice quizzes and test problems, requiring the use of proper MATLAB structure and syntax, add to the learning experience. Student input has played an important role in the development of this tutorial. Student feedback has led to useful improvements, which were integrated into the tutorial. Student evaluation results, which are presented in the paper, indicate great promise for this approach to teaching MATLAB and, by extension, other programming languages. The paper also describes various difficulties and problems encountered in developing this computer based tutorial, which may provide some useful guidelines for others who are considering computer based instruction. Note that an Internet site, www.m-tutor.usask.ca is available, where the reader can obtain more information on the tutorial.
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 | 0.000 | 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 it