Online Homework Put to the Test: A Report on the Impact of Two Online Learning Systems on Student Performance in General Chemistry
Bibliographic record
Abstract
Two different online homework systems were administered to students in a first-quarter general chemistry course. This study used a multiple regression model to control for the students’ academic and socioeconomic background, and it was found that students who completed the online homework activities performed significantly better on a common comprehensive final exam than students who did not participate. More specifically, it was found that students who completed a precourse assignment on an adaptive-responsive homework system (ALEKS; Assessment and Learning in Knowledge Spaces) could expect on average their final exam score to increase by over 13 points when compared to nonparticipating students. Students who completed a precourse assignment on a traditional responsive homework system (MasteringChemistry) also saw an average increase in their final exam score by roughly 8 points versus those who did not participate. Students who worked on the online homework for the entire quarter saw even greater gains in their final exam scores compared to nonparticipants. These findings suggest responsive online homework in general, and a responsive–adaptive learning system driven by knowledge space theory in particular, has a significant positive impact on student performance in the first-quarter general chemistry course.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".