Student Satisfaction with Broadband in Higher Education: A Survey Analysis
Bibliographic record
Abstract
The COVID-19 pandemic and broader digital transformation have reshaped higher education, challenging institutions to adapt instructional models and address disparities in technology access. These changes may have lasting implications for learning outcomes, particularly among students in at-risk counties and among nontraditional student populations.This study uses primary survey data to examine student satisfaction with broadband access, comparing traditional and nontraditional students. Key variables include demographic background, student status (e.g., first-generation, full-time enrollment, employment), and subjective learning experiences. Both qualitative responses and a logistic regression model were analyzed to identify predictors of satisfaction.Qualitative findings indicate that satisfaction with broadband varies by student characteristics. For example, the impact of being a full-time student without a job differs between traditional and nontraditional students. Conversely, being a non-first-generation student shows a consistent positive association with satisfaction across both groups. The quantitative model finds that students reporting a positive online learning experience are significantly more likely to be satisfied with broadband. However, no statistically significant difference in overall broadband satisfaction was found between traditional and nontraditional students.These findings suggest shifting expectations and nuanced influences on technology satisfaction. As institutions increasingly rely on digital tools, understanding how different student populations perceive and access technology is critical. In particular, institutions must ensure that students in at-risk counties and those with nontraditional profiles are not left behind in a rapidly evolving learning environment.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".