Beating The COVID-19 Slide in Education: The Impact of Pandemic-induced School Closures on Student Engagement And Education Equity in Chicago Public Schools
Bibliographic record
Abstract
Enhancing student engagement has been an important goal for schools and education reformers. Although many definitions of engagement were introduced since it first appeared in the 1930s, this paper defines engagement as the degree of student's active participation and course performance under both traditional classrooms and remote learning environments. This definition recognizes that engagement depends not only on the time (pre-pandemic or during-pandemic), but, more importantly, on the agents (students), and the place and space that these agents situate. Since the onset of the COVID-19 pandemic, traditional in-person classrooms were gradually replaced by online remote instructions beginning in March 2020. The goal of this study is to examine the effect of pandemic-induced school closures on student engagement. Using data from 406 Chicago public schools, I analyzed course grades from a total of 144,403 actively enrolled sixth- to eighth-grade students using a three-level hierarchical linear modeling technique, examining the pandemic-engagement relationship across students of various backgrounds and schools of varying resources. Analyses on students' engagement trends revealed two distinct patterns. Students earning a worse quarter grade (such as a B, C, or D) in pre-pandemic quarters demonstrated higher course performance under remote learning environments. However, students with disabilities, and schools in high poverty-concentrated neighborhoods showed significant declines in course grades in Spring 2020. Nevertheless, this study has implications for ensuring more accessible and equal education for students of different backgrounds, as well as delivering objective and accurate data to help inform policymakers and district leaders in the decision-making on remote or in-person instruction.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".