Bibliographic record
Abstract
Students learn best when teachers get out of the way. Unfortunately, university classrooms continue to be intensely teacher-centric, are driven by the teacher’s agenda and calendar, and embrace simple models rather complex alternatives. These simple types of learning environments frustrate students’ development of the risk-taking and choice making confidence they need in the workplace. Bain (2004) makes the point that environments embracing choice as a priority, welcoming risk taking, and nurturing students who make mistakes, are better at preparing students for professional success. In this paper, we intend to provide context to the conversation about how learning-risks and agency impact and promote the individual growth of the student when the teacher gets out of the way.Combining a Rapid Assessment Process (RAP) (Beebe, 2001) informed by Action Research (AR) (Stringer, 2007; Schön, 1983; Argyris, 1993) we devised an experiment to determine if a university course would invite more student growth when the environment changed from being teacher-centric with highly structured assignments and critical assessments, to one that embraces the tenets of complexity theory. The purpose of this approach was an attempt to challenge the status quo; to show how complex interactions between risk-taking, agency, learning culture, teacher-facilitator-mentors, peers, coursework, and outcomes are important to students’ preparation for successful professional work. To accomplish this we experimented within a software development course at a large university in the northwestern United States and found students appeared more prepared to move on to the professional workplace when they had experienced risk taking and agency in a learning environment based on complexity theory precepts.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".