Formative assessments using text messages to develop students’ ability to provide causal reasoning in general chemistry
Bibliographic record
Abstract
Formative assessment is critical in providing students the opportunity to self-assess their content knowledge and providing data to inform instructional decisions. It also provides students with information about course expectations. If, as called for in numerous science instruction reform efforts, we expect students to be able to apply their chemistry knowledge to analyze data and construct coherent explanations, then not only must summative assessments include items that require this of students, but students must also be provided with frequent and ongoing opportunities to individually practice this difficult task and receive feedback. Although online homework systems can be quite effective at providing students with feedback regarding their mastery of basic skills, it is typically less useful in providing meaningful feedback on constructed student explanations. This study examined the impact of providing students with frequent out-of-class formative assessment activities initiated by text messages. Student responses were then used to facilitate in-class instruction. Increased student participation in these formative assessment tasks correlated positively with success on exams even after accounting for student prior knowledge. There was also evidence that students increased their ability to construct complete explanation over the course of the semester. All results were consistent across two different institutions and three instructors.
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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 | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".