The Paradigm Recursion: Is It More Accessible When Introduced in Middle School?
Bibliographic record
Abstract
Recursion is a programming paradigm as well as a problem solving strategy thought to be very challenging to grasp for university students. This article outlines a pilot study, which expands the age range of students exposed to the concept of recursion in computer science through instruction in a series of interesting and engaging activities. In this study, a small number of students (n = 9) aged 11 to 13 years, were presented with a new and unique recursion curriculum involving hands-on experiences over a seven-week period at the University of Victoria, Canada. The curriculum was comprised of a series of progressively challenging recursion activities—roughly based upon the ideas of ‘Computer Science Unplugged’ (Bell, Witten, & Fellows, 2009)—and included programming applications with MicroWorlds EX, a programming language based on LOGO. Through this engagement, an increased number of students recognized and understood the concepts covered. We hypothesize that through experiences for youth with activities such as those outlined here, the number of students who understand fundamental computer science applications and who might potentially pursue computer science in post-secondary education will increase. We hypothesis further that through an earlier encounter of “challenging” concepts the learning and understanding of those will become easier at the university level. In this paper, the curriculum, classroom experiences, preliminary, largely descriptive and qualitative results and next steps in the research are discussed.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".