Bibliographic record
Abstract
Teaching adult learners presents unique challenges, as they bring diverse life experiences and prior knowledge to the classroom. Therefore, instructors must be well-versed in the fundamental principles of andragogy—the theory of adult learning. This study explores the experiences of university teachers in teaching adult learners and examines classroom practices in adult education settings at the university level. A qualitative research design was employed to collect and analyze data. Six university-level teachers were interviewed, and two classroom observations were conducted for each participant. Data were analyzed using thematic analysis, which included transcription, coding, and thematization. The findings revealed that most teachers primarily relied on lecture-based methods and demonstrated limited awareness of andragogical principles. Common classroom activities included explanation, question-and-answer sessions, and dictation of notes. Observations indicated that andragogical principles were largely absent in instructional practices. While a few participants reported incorporating learners' prior experiences into discussions, other key andragogical elements were notably overlooked. Based on these findings, it is suggested that the integration of andragogical principles in university teaching could enhance the effectiveness of adult learning. Reducing teacher talk time and increasing activities that foster critical thinking, problem-solving, and individualized instruction are recommended to improve student performance and engagement.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".