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Record W2082495800 · doi:10.5539/res.v4n1p28

A Creative Way to Teach and Learn Advanced Technical Concepts in Geographic Information Systems

2012· article· en· W2082495800 on OpenAlexvenueno aff
Tonny J. Oyana

Bibliographic record

VenueReview of European Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Style (visual arts)Subject matterMathematics educationComputer scienceSubject (documents)Sample (material)PsychologyPedagogyCurriculumWorld Wide Web

Abstract

fetched live from OpenAlex

This paper presents a creative case-based modern-style pedagogical approach for teaching and learning advanced technical concepts in geographic information systems (GIS) using classroom observations covering an eight-year study period, 2004-2011. Assessment data was collected and analyzed to provide useful insights about this approach. Included in this paper are results of specific case studies that were analyzed using a sample of students between 2004 and 2006. The assessment data and respondents consistently indicated that a case study approach offered them an excellent and enabling environment to learn advanced technical concepts. These findings support the use of a case-based modern-style pedagogical method because it does not only promote a student's desire to learn and discover new concepts, but also allows them to be actively involved in finding real world GIS solutions. The teaching method encourages, engages, and provokes students to think critically of the technical subject matter. Besides, the method creates an interesting learning experience, simulates learning, and promotes interactive dialogue between the instructor and the students. Findings in this study have implications on the learning process and the adoption of this creative approach could help provide a meaningful learning experience for educators involved in teaching advanced technical concepts.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.414
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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