Assessing the Effect of Intelligent Learning Platforms on Academic Performance of University Students in Bangladesh
Bibliographic record
Abstract
This study was primarily aimed at evaluating how Intelligent Learning Platforms (ILPs) can influence or affect academic performance (AP) of university students in Bangladesh, in terms of the principal characteristics of US, PA, FB, and ENG. The purpose of the study was to determine the effect of these characteristics of the platforms in relation to the learning outcomes of students in a developing country environment. Quantitative research design was used, and a structured questionnaire was used to collect primary data consisting of 219 valid responses. To test the hypothesized relationships, data was analyzed with SmartPLS 4 using PLS-SEM. The results suggest that the US, PA, FB, and ENG have a positive effect on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A P$</tex>, which confirms the importance of intuitive, adaptive and responsive ILPs in post-secondary education. In practice, the findings are useful to university administrators, instructional designers and policymakers to create successful digital learning environments. Socially, the effectiveness of ILP can be improved to enhance the general quality and access to learning in the Bangladeshi universities. The research provides empirical data in a little studied area but confined to cross-sectional data on a convenience sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".