Evaluation of an undergraduate physics programme of Indira Gandhi National Open University: A case study
Bibliographic record
Abstract
The undergraduate science programme was launched at the Indira Gandhi National Open University (IGNOU) in 1991-92 with an enrolment of 1,210 students. The programme was well received, and enrolments increased over the years. However, the success rates have not kept pace with enrolment. In this paper, the authors report the results of an evaluation of the undergraduate Physics programme at IGNOU. The evaluation, the first of its type for this programme, adapted the major tenets of the CIPP model. The findings are based on the responses from a randomly chosen sample of 509 learners across India. The methods employed for the study include records, document, and database analysis, surveys, and case studies. Although the University has enhanced access to higher science education, the attrition rate is high (73%), and the success rate is low. The authors recommend that the University review and reorient its strategies for providing good quality, learner-centred higher education in science subjects. The programme should address the concerns of the learners about the effectiveness of the student support systems, the difficulty level, and the learner-friendliness of study materials with the goal of achieving long-term sustainability while maintaining parity with the conventional system. The need for improving the presentation of the courses and simplifying the mathematical details is emphasised.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".