Increasing Learning Potential in Entry Level Nutrition Students through Online Tutorial
Bibliographic record
Abstract
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=<.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.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".