Know thy student: Using a graduate student learning assessment questionnaire
Bibliographic record
Abstract
Background: Creating an online learning environment for today’s adult learner that is engaging and conducive to meeting the various needs can be a challenge for any educator, new or seasoned. Understanding the students’ perspectives of what they need from faculty to be successful is underexplored. Therefore, the purpose of this study was to describe the students’ perspectives of their learning needs and motivation as reported in a faculty developed Graduate Student Learning Assessment Questionnaire (GSLAQ).Methods: A cross-sectional descriptive survey design was employed and consisted of 120 non-traditional adult learners in graduate nursing school. The demographics are as follows: 100 females (83.3%) and 20 males (16.7%) with a mean age of 34.9 years.Results: Five themes emerged: (1) motivation, (2) time orientation, (3) autonomy/role change, (4) caring, and (5) authentic engagement/communication.Conclusions: Understanding students’ concerns as well as their perceptions of what they need from faculty to be successful enables faculty to better communicate with the students as well as provide the needed support as expressed by the students.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".