Interactive Online Tutorial Assistance for a First Programming Course
Bibliographic record
Abstract
Web-based instruction shows great promise toward enriching the student learning experience. One particular area of interest is providing tutorial material and practice problems online so that classroom lecture time can be better utilized. However, the time and cost to develop full tutoring systems can be prohibitive. The project presented in this paper shows that low-cost online modules can be developed to complement existing course delivery methods. The key to the design is limiting the type of tutoring and focusing on instructional challenges involving the repetition of concepts that are introduced in the course lectures. For introductory programming courses, these challenges primarily involve the difficulties inherent in learning the syntax of a particular programming language and gaining sufficient familiarity with programming concepts, such as loops, conditional statements, and simple algorithms. The set of online modules was developed to reduce the need for repetition of these concepts during lectures. Thus, students benefit as they can gain knowledge and comprehension of these concepts at their own pace as they actively engage the tutorials and self-check exercises. The modules were used as an enhancement for an introductory programming course taught in C++ to first-year university students, some of whom had little or no programming experience. Feedback from students and instructors shows that the modules were useful and aided student learning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".