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Record W2927880235

From Syllabus to Final Grades: A Wrap-around Workshop to Support Student Motivation

2018· article· en· W2927880235 on OpenAlexaff
Lia M. Daniels

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnthusiasmSyllabusGrading (engineering)Intrinsic motivationMathematics educationPsychologyMotivation to learnClass (philosophy)Self-determination theoryPedagogyComputer scienceSocial psychologyEngineeringAutonomy
DOInot available

Abstract

fetched live from OpenAlex

Student motivation tends to be a somewhat elusive topic in teaching – either K-12 or post-secondary. And yet almost every instructor desires students who are enthusiastically engaged in the course content and related activities and assignments. Unfortunately, students often become more pragmatic and grade-focused during post-secondary education rendering enthusiasm sometimes in short supply. Achievement motivation can be used to distinguish between these two groups of students: the former is considered intrinsically motivated and the latter extrinsically motivation. Achievement motivation can also go beyond simply describing students to provide concrete suggestions on how instructors can design classroom environments that shift students away from extrinsic and towards intrinsic. If you are interested in helping students (re)embrace their love for learning – regardless of the content, class size, or grading distribution – this workshop is for you. Based on a two SSHRC-funded programs of research, the presenter will describe contemporary theorizing on student motivation and related classroom design principles. Next the presenter will take attendees through a series of activities designed to (a) clarify their own beliefs about student motivation, (b) tailor course components to maximize intrinsic motivation, and (c) offer alternatives to using grades to motivate students.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.159
GPT teacher head0.434
Teacher spread0.276 · 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 designQualitative
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

Citations0
Published2018
Admission routes1
Has abstractyes

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