Designing a Student-Centric Computer Science Curriculum: Enabling Flexibility and Personalised Learning in Secondary Schools
Bibliographic record
Abstract
This study analyses the Computer Science (CS) curricula for grades 9 and 10 in Nepal, emphasising students’ interests and needs within the social context. The study applied the mixed-methods research design. The quantitative data were collected from questionnaire surveys with students and teachers. The qualitative information was collected from curriculum designers, textbook authors, school principals, teachers and students from the Kathmandu valley, Nepal. Random and purposive sampling techniques were used to collect data from the respondents and participants. Data and information were analysed using both quantitative and thematic techniques. The study revealed significant opportunities to enhance the CS curricula by integrating interdisciplinary concepts supported by pragmatic examples. It also highlighted that improving the CS curricula for grades 9 and 10 is a top priority for both schools and students. The study proposes a student-centric CS curriculum development framework that balances between foundational concepts of CS and students' interests. This framework would address the diverse needs of students, considering their physical and mental abilities and interests in their learning. It also suggests that CS students should be included in the curriculum development committee so that they can provide practical feedback and suggestions in the CS curriculum revision process.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".