Are Students Submitting their Mathematics Outputs on Time during the COVID-19 Pandemic? A Statistical Modeling
Bibliographic record
Abstract
During the pandemic, submitting students' outputs in mathematics is seldom on time because of a lack of focus on doing their activities before the deadline. In fact, several causal factors cause the students' submission process of mathematics outputs. Hence, this study focused on investigating the factors of outputs submission on time among students at Visayas State University taking a mathematics course online amid the new normal. The study involved statistical measures to summarize the variables of interest and employed binary logistic regression to model the causal factors affecting the students' submission on time. Results revealed that only 26.15% of the students are submitting their mathematics outputs on time. This means that during the pandemic, several students are having difficulty submitting their outputs on or before the given deadline. The logistic regression model showed that the significant factors that influence the students' submission on time include the availability of laptops (p-value=0.011), money spent on internet load (p-value=0.062), small household size (p-value=0.087), and internet signal strength (p-value=0.020). It is concluded that appropriate gadgets (technology) for online learning are a great help in accomplishing learning tasks on time. Additionally, less distraction at home, enough budget, and a good internet signal can progress their required mathematics activities and sustain an effective learning behavior amid the distance learning process. Hence, students must be supported by the Philippine government in terms of their need for learning tools that are suitable for online learning. Furthermore, teachers must provide attainable learning tasks given the deadline of submission and encourage their students to develop time scheduling management for their mathematics activities.
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 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".