WIP: Decoding a Discipline – Toward Identifying Threshold Concepts in Geomatics Engineering
Bibliographic record
Abstract
Abstract This is a work-in-progress paper on a descriptive or a ‘what is’ type of teaching and learning project related to deep learning of fundamental knowledge in geomatics engineering. On the hard-soft and pure-applied spectrums the discipline of geomatics engineering can be classified as hard and applied. This makes sustaining an environment for deep learning, as opposed to superficial learning, of core geomatics engineering knowledge a challenging task. This environment sometimes comes at the cost of instructors of higher level courses having to repeatedly review concepts taught in lower level courses. As a result, little time is left for tackling advanced learning outcomes. This problem can be mitigated by assessing the current learning environments in core geomatics engineering courses and, more specifically, identifying threshold concepts or areas of troublesome knowledge in these courses; developing and implementing a collection of learning resources and teaching activities to address these matters; and observing and analyzing the effect of using these resources and activities on geomatics engineering students. This paper will focus on the first part of the problem, namely, what methods to use in order to assess the learning environment and detect the threshold concepts in select geomatics engineering courses. The authors propose a series of questionnaires, observation sessions, and reflection meetings where students in their second and third year of geomatics engineering will be invited to participate. The questionnaires will be formative minute papers on muddy concepts throughout the semester, and summative end-of-term surveys asking the students to describe their learning experience during the semester. The observation sessions will be in-class (teacher-student interactions) and think-aloud (individual or in groups). The reflection meetings will be conducted after certain exams, tutorials or labs, where the students will be given the opportunity to express their opinions on the learning objectives involved. In addition, grades from midterm and final exams will be analyzed for any alignment between the concepts in question and the respective student performance. This part of the project will be run for two years, so the authors will be seeking input from the engineering education community in order to improve the study in its second iteration.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".