Enhancing Students' Performance in Computer Science Through Tailored Instruction Based on their Programming Background
Bibliographic record
Abstract
Computer science including data analytics is a widely popular field, boasting promising career opportunities in the future. Proficiency in programming stands as a fundamental requirement for success in this domain. However, students entering MSc programs in data analytics often possess varying levels of programming background, which can impact their performance in assignments. Recognising and addressing these differences through tailored instruction can improve students’ outcomes. This paper explores the importance of considering students' programming backgrounds in the data analytics field and highlights strategies to enhance their performance based on prior knowledge. This study was carried out on two different modules in two different pathways. We have chosen two distinct cohorts and pathways to ensure unbiased conclusions in our study. The initial research was applied to the Database and Programming Fundamentals module for an MSc data analytics cohort, and then we utilized a Deep Learning module for final year computer science undergraduates as a validation cohort. As a conclusion, this study successfully demonstrated a significant increase in student assignment performance through the implementation of tailored instruction based on students' programming backgrounds. Despite receiving positive student feedback and observing excellent and improved performances, it is crucial to acknowledge instances of unsatisfactory student performance as well. Both studies were conducted by the School of Electronics, Electrical Engineering, and Computer Science (EEECS) at Queen's University Belfast (QUB) during the academic year 2021/2022.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".