Understanding the Impact of COVID-19 on Students in Institutions of Higher Education
Bibliographic record
Abstract
United States institutions of higher education (IHEs) transitioned to online learning in response to the COVID-19 pandemic, and guidelines to reduce risk of spreading or contracting the disease. This large scale, impromptu transition to online learning had implications for students, and IHEs. This study used a survey design to explore the impact of the COVID-19 pandemic on students in IHEs, including the impact of the move to online learning on students learning and well-being. Recruitment focused on college students in IHEs, with a sample drawn from 17 IHEs across America (n = 501). We developed a 91-question survey, to collect information regarding impact of COVID-19, and pandemic-related transition to online learning on participants, as well as participants’ well-being. We measured student well-being using the following measures: the Generalized Anxiety Disorder Assessment (GAD-7), the patient Health Questionnaire (PHQ-9), Perceived Social Support scale (PSS), and Multidimensional Scale of Perceived Support (MSPSS). Students with disruptions to online learning were likely to be more stressed, and more anxious. About 88% of students reported facing disruptions to online learning from family members, friends, or pets. Overall, students reported moderate to high levels of stress, anxiety, and depression. And, relatively low levels of perceived social support. The impact of COVID-19 on college students and student learning are multifactorial and represent a combination of benefits and burdens. It is imperative that we take lessons learned for this pandemic and apply it to future learning.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".