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Record W2794519204 · doi:10.1139/cjss-2018-0006

Introductory soil courses: a frontier of soil science education in Canada

2018· article· en· W2794519204 on OpenAlexaffvenueabout
Maja Kržić, Thomas Yates, Nathan Basiliko, Maxime C. Paré, Amanda Diochon, Mathew J.B. Swallow

Bibliographic record

VenueCanadian Journal of Soil Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsMount Royal UniversityLakehead UniversityUniversité du Québec à ChicoutimiLaurentian UniversityUniversity of SaskatchewanUniversity of British Columbia
Fundersnot available
KeywordsScience educationMathematics educationFrontierTeaching methodSoil scienceSociologyPsychologyEnvironmental sciencePolitical science

Abstract

fetched live from OpenAlex

As the focus of soil science education in Canada and elsewhere has shifted towards nonsoil science majors, it is important to understand if and how this has affected the scope of introductory soil science courses. The objectives of this study were to inventory Canadian postsecondary units that offer introductory soil science courses and to document attributes of instructors, students, and teaching approaches in these courses. We surveyed 58% of the instructors of introductory soil science courses across Canada, and most of these courses were offered by geography and environmental science units. The majority of instructors followed a traditional lecture (86%) and laboratory (76%) delivery format, whereas 36% used online teaching resources. Introductory courses were delivered by primarily one instructor, who held a Ph.D. in a tenure track position and in most cases developed the course themselves. Over half of the instructors surveyed used either a required or a recommended textbook, pointing to the need for creation of a Canadian-authored soil science textbook. Several follow-up studies are needed to evaluate teaching methods used in the upper level soil science courses, students’ perceptions of teaching in soil science, and instructors’ knowledge of resources available for online and (or) blended 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.

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)0.012
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.006
Scholarly communication0.0000.002
Open science0.0020.000
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 head0.036
GPT teacher head0.357
Teacher spread0.321 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations20
Published2018
Admission routes3
Has abstractyes

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