Integrating Biochemistry Into a Program Serving Multiple Tracks in Medical Laboratory Science
Bibliographic record
Abstract
<h3>ABSTRACT</h3> This education case study addresses 2 issues: the program-identified need for more biochemistry content in the prerequisite course, Organic Chemistry I, and its availability to the Medical Laboratory Science Online Career Ladder track students. The underlying principles for resolving the issues are the constraints of the program curriculum and the unavailability of an online organic chemistry course at Georgia Southern University. The first step in addressing the issues involved collaboration between the Medical Laboratory Science and Chemistry faculty members to identify the specific biochemistry topics on which program courses build. Because the Biochemistry I course has the prerequisites of 2 semesters of Organic Chemistry and the medical laboratory program of study only had room for 1 course in this area, a new course was designed to address the program-identified content need. The new course was offered in a face-to-face format, but one of the program tracks utilizes entirely online instruction. To allow those students access to the new course, the course instructor obtained e-Faculty status and undertook designing an online version. The process included the expertise of an instructional designer, and the resulting online course was rich in content and teaching strategies to provide the social presence necessary for student engagement in that venue. The online course immediately earned Quality Matters Certification and has been offered 3 times to date. Forty-one students have taken the new course. Of those eligible to apply, 71% entered the Medical Laboratory Science program, and 78% of the entrants have graduated or are on track to do so. The average course grade point average data for the 5 cohorts reveal that the strong level of student success in the face-to-face course has not been observed in the online course (3.55 and 2.53, respectively), requiring continued efforts to deliver content, engage students, and assess learning in alternative ways.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".