Memview: A Pedagogically-Motivated Visual Debugger
Bibliographic record
Abstract
Novice programmers often have difficulty understanding the interactions between the objects in their programs. Many studies have shown that visual representations of computer memory can aid students' comprehension. One such representation, developed by Gries and Gries, divides computer memory into three areas: one for the call stack, one for static objects allocated on the heap ("static space"), and one for normal heap objects ("object space"). Memview, an extension to the DrJava IDE developed at Rice University, is a dynamic, interactive display of computer memory based on this model. Its simple three-pane representation shows novices the life cycle of objects, and helps them understand three key concepts: the notion of an "address" in memory, how storing an address creates a reference from one object to another, and the differences between the heap, the stack, and static space. User tests conducted during the summer of 2004 demonstrated that Memview facilitated faster completion of common introductory programming problems. Since then, Memview has been used in an introductory programming course to illustrate basic data structures such as linked lists. We are presently refining the tool based on further feedback from students and instructors
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".