EFFECTIVE LEARNING ENVIRONMENTS: IS THERE ALIGNMENT BETWEEN THE IDEAL, THE ACTUAL, AND THE STUDENTS' PERSPECTIVE?
Bibliographic record
Abstract
The overarching principles of effective educational practice in higher education define the characteristics of an effective learning environment. Institutions of all sizes have demonstrated that it is possible to increase student success and add value to the learners' experiences by applying these principles. This qualitative study explores the alignment between the ideal learning environment, the actual undergraduate engineering experience as defined by engineering educators, and the learners' perspective. Building on the benchmarks of effective learning environments, students were asked to complete an online survey based on the Stop, Start, Continue method for acquiring formative feedback. Thematic analysis identified five themes: focus on learning, supported instruction, quality of teaching, student engagement, and other related items including academic rigour and strong relationships between students, instructors, and staff. Students indicate that their primary learning environment is teacher-directed and lecture-based. This is aligned with the current practices identified by engineering educators, but only partially in line with those of an effective learning environment. Coded items indicate that many students have sub-optimal motivational outlooks, but feel their ability to survive and thrive is improved when they are more involved in their learning and are supported in a more student-centred learning environment. They value instructors who provide clear and accurate resources, and who are supportive through actions both in and out of the classroom. Engineering educators can use these insights to better align their teaching practices with the principles and practices of effective learning.
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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 | 0.017 | 0.024 |
| 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 | 0.012 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".