Facilitating Learning in Online Undergraduate Mathematics and Statistics Courses
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
COVID-19 saw a number of courses and programs in higher education transition to online platforms. While online learning environments provide novel opportunities for pedagogy and technological integration, it is essential that educators feel they are supported with teaching via a new medium. Introductory mathematics and statistics courses have also shifted to virtual learning environments. Given the nature of the content of these courses, they pose an additional layer of complexity to facilitating learning in online spaces. The aim of this chapter is to explore the following questions: 1. What teaching methods do students perceive as effective online pedagogies for teaching mathematics and statistics courses? 2. How does online course design impact learner efficacy in mathematics and statistics courses? The literature revealed the importance of considering instructor presence, learner efficacy, user experience, and structure when designing online learning resources. Implications for teaching math-based courses online are discussed.
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 | 0.002 | 0.003 |
| 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