Benefits of Transitioning from Paper-Based to Online Assignments in Problem Solving Courses
Bibliographic record
Abstract
Students registered in numerical-based problem-solving courses are often given a number of assignments to complete independently in order to demonstrate and refine their problem-solving skills. Traditionally, these assignments are paper-based and all students receive the same problems to solve; thus, they often rely heavily on their peers or on solution manuals to complete their assignments. As a result, assignment grades are typically high, but do not correlate with test or exam performance. In this paper, we describe the use of Numbas, an open educational resource created by the University of Newcastle, England, as a customizable, online assignment system. Using Numbas, each student is provided with a unique set of problems, each with randomly generated values. While they are still allowed to work collaboratively with their peers, this randomization encourages students to develop their critical thinking skills to solve unique problems. To identify if the use of the online assignment system is correlated with enhanced performance, final exam grades earned by students who were exposed to either the paper-based or the online assignment system were compared. Furthermore, data from student feedback surveys were analyzed to identify student-perceived strengths and challenges associated with the online assignment system, and to determine possible opportunities for improvement. The study demonstrated an improvement in knowledge-based skills among students who were exposed to the online assignment system, compared to those who wrote paper assignments. However, no significant improvement in problem-solving skills was observed. Similar findings have been reported by other research works studied the same concept. Further, 88% of students surveyed reported that the online assignment system improved their learning experience.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".