Quantitative Courses in Higher Education: A Comparison Between Asynchronous and Synchronous Distance Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quantitative courses in higher education have always been difficult and demanding because they involve complex principles and procedures and students are required to deal with complicated problems. With the outbreak of the COVID-19 crisis and the need to move immediately to distance learning, the challenge involved in undertaking such courses has become even more significant. Recent studies point to different findings regarding the effect of asynchronous and synchronous learning on student performance so it is not entirely clear which method is preferable. The present study addresses the above ambiguity with a focus on quantitative courses, known for their special complexity. The study examined which method is preferable and why, based on the learners’ point of view. Two main issues were examined: the contribution of each method to the learning process and whether one of them is significantly superior to the other and can even replace it exclusively. The research was based on two samples of students  who enrolled in eight quantitative courses at two colleges. The courses were given during the year 2020-2021 using combined teaching formats: Asynchronous and synchronous distance learning. Learners were asked to answer an online questionnaire that assessed the characteristics and advantages/disadvantages of both methods. The findings show that students distinctly prefer asynchronous learning over synchronous. The main reasons for this are the ability to repeat lessons indefinitely, time savings, flexible learning anywhere, anytime and at the appropriate pace as well as the ability to better understand the material.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it