First-Year College Students: Perspectives on Technology and Wellness in Education
Bibliographic record
Abstract
This paper explores the impact of technology and wellness in the context of students entering post-secondary education. It aims to provide insights into the use of technology and how it affects students’ wellness. The transition from high school into post-secondary education has often been a complex phase in students’ lives, and such complexity may be especially significant for virtual high school graduates, in other words, students who finished their high school education mostly virtually due to school closures during the COVID-19 pandemic. Students starting post-secondary education are usually between 17–19 years old, an age period at which these students are more developmentally vulnerable to the effects of rapid physiological, financial, and social changes. Despite some positive aspects of technology usage in education, challenges remain. Students navigate potential academic losses due to ineffective virtual schooling experiences during school lockdowns. This may aggravate students’ adaptation to higher-education culture and norms and academic expectations, especially formal writing standards often required in university papers. Other challenges may include the over-reliance on technology for academic, social, and personal tasks, accentuating students’ difficulties with wellness and requiring a rethinking of learning practices to eloquently respond to students’ needs in the context of the legacy of the coronavirus pandemic. This paper seeks to contribute to the conversation on how post-secondary institutions respond to the need to balance technology and wellness in the context of education. Ultimately, this paper explores perspectives on potential higher institutions’ responses to the impact of technology on students’ mental health and learning as well as implementing wellness practices while integrating technology into education.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".