Teaching Basic Medical Sciences at a Distance: Strategies for Effective Teaching and Learning in Internet-Based Courses
Bibliographic record
Abstract
In recent years, the Internet has become an effective and accessible delivery mechanism for distance education. In 2003, 81% of all institutions of higher education offered at least one fully online or hybrid course. By 2005, the proportion of institutions that listed online education as important to their long-term goals had increased by 8%. This growth in available online courses and their increased convenience and flexibility have stimulated dramatic increases in enrollment in online programs, including the Veterinary Technology Distance Learning Program (VT-DLP) at Purdue University. Regardless of the obvious benefits, distance learning (DL) can be frustrating for the learners if course developers are unable to merge their knowledge about the learners, the process of instructional design, and the appropriate uses of technology and interactivity options into effective course designs. This article describes strategies that we have used to increase students' learning of physiology content in an online environment. While some of these are similar, if not identical, to strategies that might be used in a face-to-face (f2f) environment (e.g., case studies, videos, concept maps), additional strategies (e.g., animations, virtual microscopy) are needed to replace or supplement what might normally occur in a f2f course. We describe how we have addressed students' need for instructional interaction, specifically in the context of two foundational physiology courses that occur early in the VT-DLP. Although the teaching and learning strategies we have used have led to increasingly high levels of interaction, there is an ongoing need to evaluate these strategies to determine their impact on students' learning of physiology content, their development of problem-solving skills, and their retention of information.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".