Enhancing the Study of Business Statistics with an e-Homework System
Bibliographic record
Abstract
This paper compares the performance of two classes of students who were enrolled in an Introductory Business Statistics course. Students in one class were required to use e-homework, an online system and another class completed their homework assignments without the online system. The major objective of this study was to determine whether there was any difference in the level of performance between students who used the online homework system and those who did homework assignments in the traditional method. The students in a large class with online homework were compared to students without online homework. This e-homework system enables instructors to monitor individual student’s performance and transmit immediate feedback to students. This paper answers the question: Can online homework improve the performance of students enrolled in an Introductory Business Statistics course? To answer this question, we evaluated the students’ performance based on their final grades in a first year Introductory Statistics course. Students’ perceptions of the usefulness of online homework were also considered. The findings of this research showed that students obtain many benefits from online homework. Students were better prepared in writing test, their ability to understand course concepts increased because of the timely feedback they receive from instructors, discussion about Statistics among peers occurred more frequently than in previous semesters. Students cultivated better study habits, and consequently, they developed more confidence in applying their knowledge of statistical concepts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".