Use of Digital Technological Tools among Undergraduate English Language Learners in Pakistan
Bibliographic record
Abstract
Technology has become an important source for enhancing the knowledge of the students. Apart from the non-academic purposes, the use of technology for the academic purposes also has greater impact on the process of learning specifically on tertiary education. Therefore, it has become essential for higher education institutions to focus on the available opportunities for integrating technology in the academic setting. The developing countries like Pakistan, however; are facing some major challenges in technology integration due to the unavailability of sufficient financial resources. Hence, this study explores the use of digital technological tools at undergraduate level in one of the public sector universities of Pakistan. The study also examines the impact of the medium of instruction and respective discipline of the tertiary level students on the use of technology. For this purpose, an online survey was conducted from 200 undergraduate students studying in four different disciplines in the university. The findings revealed that the majority of the students at the undergraduate level have accessibility to smartphones, laptops or desktop computers in the university but only a few students use these available technological tools for learning purposes. Smartphones were determined to be the most easily available technological tool while the students generally do not prefer carrying their laptops to the university. Besides this, the students also reported having limited technological knowledge and skills for the digital tools to be used for educational and learning purposes. However, a greater percentage of the students were willing to participate in training sessions for learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".