Education and technology : a critical study of introduction of computers in Pakistani public schools
Bibliographic record
Abstract
The importance of technology in education cannot be underestimated. There are compelling reasons for developing nations like Pakistan to introduce technology in their educational systems. Nevertheless the approach and methods used in introducing technology in schools are premised on an economic ideology and based on a techno-centric curriculum that leads to new forms of dependency by keeping individuals from controlling the decisions that significantly shape their lives. Introduction of technology does not automatically guarantee enhanced learning or effective teaching. Technology in education should be used as a tool to increase communication, create awareness, break down existing hierarchies, develop new styles of creating knowledge, and make schooling and education more inclusive. Mere technical use of computers in education does nothing to empower students. The techno-centric introduction of technology in Pakistani public schools is likely to produce inequality. A number of practices in Pakistan's educational and social structure will have to change for the potential of technology to be fully achieved. A shift is needed from 'learning about the computers' to 'using computers in learning', from 'acquisition of limited skills' to 'construction of knowledge', from 'teacher-dependency' to 'independent inquiry' and from 'teacher-centered' to 'student-centered' teaching methods. However, such a change can only take place within a critical framework of education. The critical model based on integrated curriculum treats the computer not as an isolated subject but as a tool that helps learners enhance their critical thinking skills and seek various alternatives to solve problems. Thus, it is important for educational policy-makers to realize that any effort at introducing technology in the educational realm requires theoretical discussion and a societal dialogue to arrive at a framework for technology's place in socio-educational contexts. Pakistan needs to develop and introduce educational technology to seek solutions for its unique economic, social, cultural and human and social development requirements based on its present level of development and evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".