WeBWorK as an open online homework system in a second-year material and energy balances course
Bibliographic record
Abstract
–Many studies have shown the advantages of dynamic homework tools for student learning. Material and energy balances is a common foundational course in chemical engineering and related disciplines. There are a number of open educational resources that have been developed around material and energy balances such as screencasts, interactive simulations and conceptual multiple choice tests. In order to build upon these resources, we sought to create online homework assignments on an easily accessible platform, with instant feedback and dynamic questions with individual numbers and unique solutions. We selected WeBWorK as a tool for this due to its common use, open and editable nature and the ease to which problems can be shared between authors through the Open Problem Library system built into the software. WeBWorK problem sets were easy to use for students and well received. The switch from pencil and paper to WeBWorK showed no significant effect on student grades or participation in homework assignments. On the Instructors’ side, WeBWorK saved time overall by reducing grading significantly. This allowed greater time for student interaction with instructors. However certain problem types, notably creating diagrams or explaining problem solving methodology could not be accomplished with WeBWorK. A hybrid of pencil and paper homework as well as WeBWorK is recommended for material and energy balances due to the course material and objectives
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.031 |
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".