Qualitative Evaluation of the Java Intelligent Tutoring System
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
In an effort to support the growing trend of the Java programming language and to promote web-based personalized education, the Java Intelligent Tutoring System (JITS) was designed and developed. This tutoring system is unique in a number of ways. Most Intelligent Tutoring Systems require the teacher to author problems with corresponding solutions. JITS, on the other hand, requires the teacher to only supply the problem and problem specification. JITS is designed to "intelligently" examine the student's submitted code and determines appropriate feedback based on a number of factors such as JITS' cognitive model of the student, the student's skill level, and problem details. JITS is intended to be used by beginner programming students in their first year of College or University. This paper discusses the important aspects of the design and development of JITS, the qualitative methods and procedures, and findings. Research was conducted at the Sheridan Institute of Technology and Advanced Learning, Ontario, Canada.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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