“I Learn, But They Say It Doesn’t Count”: Academic Support Staff and the Struggle for Recognition in Lifelong Learning
Bibliographic record
Abstract
This study explores the factors influencing lifelong learning engagement among academic support staff in Thai higher education institutions. Using a qualitative research approach, semi-structured interviews and focus group discussions were conducted with 20 participants from various universities. The findings reveal five key characteristics of lifelong learners: broad knowledge, curiosity, self-directed learning, positive attitudes, and diverse skill sets. Despite recognizing the value of lifelong learning, participants faced significant barriers, including high workloads, lack of career incentives, limited access to training, and digital literacy challenges. Institutional policies often prioritized faculty development, leaving support staff with fewer professional learning opportunities. However, universities that offered structured, flexible, and job-relevant training programs saw greater staff engagement. The study highlights the critical role of workplace learning culture, particularly managerial encouragement, mentorship, and recognition systems, in fostering professional growth. To enhance lifelong learning participation, universities must implement clear career advancement pathways, reduce workload barriers, expand digital literacy programs, and promote inclusive workplace learning environments. These findings contribute to adult learning and workplace education theories, providing policy recommendations to strengthen lifelong learning among academic support staff in Thai higher education.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".