Necessity is the Mother of Invention: How the Need for Online Schooling Impacted Mathematics Teaching Practices and Student Engagement
Bibliographic record
Abstract
In this paper, we document elementary school teachers’ attitudinal and pedagogical changes during the rapid move from brick-and-mortar to virtual schooling initiated by the COVID-19 pandemic. We administered four online surveys (in September and October 2020, and in March and June 2021) to determine teachers’ perceived challenges and successes in using online technology, as well as applied mixed-methods action research to identify their approaches to teaching mathematics online. The initial challenges included gaining skills, resources, and know-hows for teaching online, and supporting students and their families in the swift transition, while also maintaining instructional goals and overcoming stress. The later challenges included dealing with workload and engaging students in learning. As their comfort with technology increased, teachers started realizing that many old pedagogies were either impossible or inadequate in the online environment, and they began to innovate with virtual classrooms that encompassed students’ homes, parents, and the outdoors. Mathematics manipulatives were found in the kitchen and measurements were done in the home or during walks outside. Mathematics concepts became more real-life centered, and learning became more playful and problem-oriented. Technology helped to create and sustain learning communities, and exposed student thinking at their comfort level. For some students, this approach worked better than brick-and-mortar schools; for most teachers, it created opportunities to provide and receive feedback differently, collaborate widely, reinvent their practice, and contribute to changing norms. We conclude by providing suggestions for moving forward.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".