A Comprehensive Overview of Education during Three COVID-19 Pandemic Periods: Impact on Engineering Students in Sri Lanka
Bibliographic record
Abstract
The COVID-19 pandemic has impacted the education system in Sri Lanka, similar to many countries in the world. As a result, the mode of education shifted from conventional face-to-face classes to online mode. The main objective of this study is to provide a comprehensive overview of the changes to the educational system due to the COVID-19 pandemic among engineering undergraduates of Sri Lanka over three identified pandemic periods. Quantitative descriptive analysis was used together with chi-square statistics to answer the research questions using the data collected through a google survey from engineering undergraduates in Sri Lanka. According to the results, students’ attendance in online classes has improved over time compared to the initial pandemic period. Nearly 50% of students’ family income has been impacted, either stopped or reduced due to the pandemic. Most students have issues regarding computing devices, internet connectivity, and the home environment. According to the chi-square statistics results, few of these issues had a statistically significant relationship between the family income; lower the income, higher the negative impact on students. More than half of the students felt isolated when studying at home during the pandemic. Still, more than 50% of students agreed that lecturers were well prepared to guide and deliver lessons remotely. The overall recommendations of the study are implementing workshops, training on new technologies, awareness programs for educational stakeholders, providing incentives to purchase digital devices, and improving internet connectivity to improve the new standard education system of Sri Lanka.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".