Increasing Learning Potential in Entry Level Nutrition Students through Online Tutorial
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
<p>The objective of this study was to assess whether implementation of an online tutoring program, MasteringNutrition©, would produce measurable gains in student learning outcomes. Research conducted by Pearson©, the creator of MasteringNutrition©, indicates that the inclusion of Mastering© tutoring programs into existing courses has the ability to increase student performance on assessments and total course grades. Students of a general education, nutrition course were invited to participate in this study: spring of 2013 (no Mastering; n=182), fall of 2013 (Mastering; n=86), and spring of 2014 (Mastering; n=410). NDFS 1020 is an introductory nutrition course taught in a blended style. Course structure includes course lectures (60% of instruction time) and online assignments (40% of instruction time), Mastering© is included as part of required assignments. Learning outcome progress was measured by questions on a pre-semester quiz, final exam, and posttest six months after course completion. Assessments measured student progress based on course learning objectives. Results of statistical tests reported no significant difference in test scores for each group over time. Students who scored lower than the mean on the pretest and used the Mastering© program demonstrated greater improvements in final and posttest scores compared to those who scored higher than the mean on the pretest (p=&lt;.001). Implementation of online tutoring program did not significantly improve overall student outcomes. Online tutorial programs may be helpful for students who are enrolled in courses where they have little prior knowledge of subject matter.</p><p> </p>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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