Utilizing Instructional Consultations to Enhance the Teaching Performance of Engineering Faculty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Although many kinds of data can be used to guide instructional consultations, research comparing the efficacy of such data is scant, especially in engineering. In this study, multiple modes of assessment were used to evaluate the impact of consultations informed by different kinds of data. This study illuminates two key aspects of instructional consultations: (1) their efficacy varies depending on the kind of data used to guide them, with student feedback from a Small Group Instructional Diagnosis (SGID) having the largest positive impact, and (2) the instructional consultant plays a key role in helping both interpret the available data and identify strategies for improvement. These findings suggest three implications for practice: (1) whenever possible, SGID‐based consultations should be offered systematically and proactively for engineering faculty, (2) data for other kinds of consultations should be tailored to the needs of the individual instructor, and (3) instructional consultants should be available to collaborate with faculty to enhance their teaching, thereby building an engineering culture that actively supports teaching and learning.
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.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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 it