Course syllabi in GIS programming: Trends and patterns in the integration of computer science and programming
Bibliographic record
Abstract
Understanding of fundamentals of computer science and abilities in programming are becoming more important components of the GIS practitioner's skillset. As the frontiers of GIS expand into areas of inquiry and modes of operation which require such domain capabilities, teaching and instruction in GIS must begin that shift as well. And while the knowledge, skills, and practices of the GIS practitioner have always been in flux, the range of potential topics from computer science and programming to integrate into GIS instruction is a major pedagogical challenge. This paper examines a range of syllabi from variant GIS programming courses to explore the landscape of programming coursework. Through content analysis, these syllabi reveal three general themes: courses built on learning scripting with Python, courses built on learning fundamentals of WebGIS, and courses built on learning fundamentals in geodatabase design and management. While each of these focus areas are impactful skills for GIS learners to explore, there is by no means any consistency or cohesion in how GIS programming courses operate, nor does there seem to be any general resources or approaches to unify course design. More work is necessary among GIS instructors to begin building evidence‐based practices in GIS programming instruction.
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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".