The Role of Abstraction in Introductory Programming
Bibliographic record
Abstract
First year computer science (CS) courses have mean failure rates as high as 30.3% [13]. In an attempt to identify and mitigate potential contributing factors to this problem, this study aims to investigate how the understanding of abstraction impacts students’ programming ability and subsequent success in a first-year data structures course. Specifically, we employ the use of videos to explicitly introduce the concept of abstraction and assess understanding through quizzes directly related to concrete programming exercises. Our work is motivated and guided by related work on abstract thinking as it relates to the skillset of a computer scientist, in addition to existing work on the introduction of abstraction as a learning outcome in computer science education. We measure the students’ understanding of abstraction through a series of short weekly quizzes tightly tied to graded programming exercises. Through our analysis we identify specific topics in the introductory CS course that present abstraction difficulties for students, and suggest potential reasons that these topics are particularly challenging. We also evaluate the students’ learning experience when taught abstraction explicitly, discussing both successes and areas in need of improvement. Finally, we recommend introducing abstraction into the early CS curriculum as an explicit learning outcome and treating the topic as a persistent theme throughout courses in order to support students’ understanding of foundational programming.
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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 | 0.013 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".