A Foundation for Spatial Thinking: Towards a Threshold Concept Framework in GIScience and its Implications for STEM Education
Bibliographic record
Abstract
Geographic Information Science (GIScience) is a fairly modern, rapidly emerging multidisciplinary field, addressing the theories and concepts behind the spatial technology called Geographic Information Systems (GIS). With the proliferation of this technology, the demand for GIS professionals has also increased, as has pressure to support their competency in the community of practice. This study investigates a framework of threshold concepts to provide insight into the learning process for distinctive ways of thinking and practising within GIScience. Despite some theoretical investigations of this framework, no empirical studies have explored learner insights on this topic and its implications. The main goal of the study is to investigate empirical evidence of a threshold concept framework in GIScience and its potential implications for Science, Technology, Engineering, and Mathematics (STEM) education. \n \nThe investigation relied on data collected through survey questionnaires and personal interviews, administered in an introductory GIS course at the University of Waterloo in Ontario, Canada. The qualitative assessment of the study was based on a phenomenographic approach to examine different ways in which students experience GIS learning. First, threshold concepts were explored and examined based on their transformative, irreversible, integrative, bounded, and troublesome characteristics. In addition, statistical analyses were employed to identify important factors promoting student proficiency in GIScience. These factors were further examined with respect to variations in students’ discipline-specific ways of thinking and practising. The implications for STEM education were also discussed, in terms of shared misconceptions, spatial thinking abilities, and academic and career competencies. \n \nFindings from this study suggest that the most prominent threshold concepts perceived by GIScience students are map projections and advantages and disadvantages of raster and vector data models, which are likely to open up new and previously inaccessible ways of thinking (i.e., ways of looking at a map). Important factors for students to acquire an understanding of such concepts were also identified, including academic preparedness, educational status, major field of study, type of academic background, ArcGIS software experience, GIS learning resource, and prior subject learning experience in mathematics, GIS, programming, or computer science. The implications include enhanced spatial thinking ability, as well as creativity, critical thinking, and problem-solving skills, all of which can help to promote interest and self-confidence in pursuing STEM fields. Overall, results from this study offer valuable insights for enhancing the efficacy of teaching and learning in GIScience.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".