A Teaching Assistant for Microelectronic Circuits Problems
Bibliographic record
Abstract
Ever since their introduction, personal computers have been used as a tool for education. An Intelligent Tutoring System (ITS) is one such tool that can provide automated feedback to students when solving problems. This paper discusses the design and implementation of an ITS to aid instructors of microelectronic circuits, a topic often taught to undergraduate electrical and computer engineering students. The proposed ITS allows an instructor to create and add problems related to metal-oxide-semiconductor (MOS) transistors, and to simulate the underlying circuits using the commonly available LTspice tool. Students are then able to load that problem and enter equations to solve it, while the ITS provides immediate feedback and hints, as applicable. Currently, the proposed ITS can handle MOS transistors at DC, including Ids equations for triode and saturation regions of operation. The intent is to extend the capabilities of the proposed ITS to handle different circuit elements such as diodes and bipolar transistors, and to facilitate open-ended design problems.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.188 | 0.058 |
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".