Lifting the hood of the computer: program animation with the Teaching Machine
Bibliographic record
Abstract
The teaching of computer programming concepts is hampered by the difficulty students have in visualizing the dynamic processes that are controlled by the static texts of computer programs. This is no surprise, as the students have never actually seen these processes. To reveal what is happening "under the hood" of the computer, we have developed a new tool for program animation: the Teaching Machine. It shows an abstraction that captures some of the ways high-level programmers think of machines, by modeling aspects of both the underlying processor and the compiler. As a program executes, the Teaching Machine can show the flow of control through the source code, the evaluation of expressions, and the changing values of data objects in the memory. The Teaching Machine allows considerable flexibility. Views that are not relevant to an example can be hidden. Execution steps can be as large as a complete subroutine call or as small as a single arithmetic operation. Memory can be viewed in any of four different formats, including a box and arrow representation, which allows automatic animation of algorithms on data structures such as linked lists and trees. We have used the Teaching Machine in a number of ways: as an animated blackboard for an instructor to use in the classroom; as an application that students can use to investigate either canned examples or their own programs; as an component in a Web tutorial; and as the centrepiece of a series of tutorial videos. The Teaching Machine has been used in a first course on programming, a second course on programming, and a course on data structures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".