Students’ Learning Support and Perceptions in an Online Mathematics Course in a Business Faculty
Bibliographic record
Abstract
Online courses are growing in higher education, resulting from an increased access to information and communication technologies. While such courses allow time and/or space flexibility for both students and instructors, they also promote active learning and require more autonomy from the students. In this paper, we present the main design features of a new prerequisite mathematics online course in a business faculty. While most of the course was designed in an asynchronous mode, it also includes blended synchronous support sessions that students can attend each week. As a Scholarship of Teaching and Learning (SoTL) project, we related the design features of the course to students’ learning support and perceptions by analyzing the content of the learning management system as well as students’ narrative comments in course teaching evaluations over five semesters. The main themes reported concerned the appreciated course design and structure, the enhanced instructor’s presence through commented slideshows and support sessions, the instructor’s accessibility and care, a challenging but relevant course, and collaborative practice with a software application. In particular, the instructor’s presence and follow-up throughout the semester was highlighted by the students as a means to support their learning. Furthermore, most students’ comments reported positive perceptions about the online course and specific design features. Several comments also allowed to identify potential areas for change in a future version on the course, as part of the SoTL research that focuses on teaching and learning improvement.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".