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Learning and Leading Within a Multigenerational University Course Experience

2023· book-chapter· en· W4387583052 on OpenAlexaff
Janice Moore Newsum, Jennifer K. Young Wallace, James L. Dillard, Billi L. Bromer, Sharon K. Andrews, Virginia Dickenson, Noran L. Moffett, Caroline M. Crawford

Bibliographic record

VenueAdvances in early childhood and K-12 education · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSubject matterPsychologyCourse (navigation)Qualitative researchPedagogyLife course approachMathematics educationMedical educationSociologySocial psychologyEngineeringCurriculumMedicine

Abstract

fetched live from OpenAlex

The university instructor experience around leading a course learning experience that includes multigenerational students with different levels of subject matter knowledge and understanding, as well as different levels of life experience, is the focus of this qualitative multiple case study. In addition to the university instructor participants, a student participant was also engaged in the study. The relational, international differences, similarities, conflicts, and strengths, as reflected through the participant interview reflections, resulted in discussions focused upon the primary themes of course design, course engagement, instructor collaboration, instructor community in-course efforts, assessment considerations, instructor-sustained emotional engagement or support, the journey through differentiated instructional efforts that may be impacted by generations intermixing within the course learning environment, as well as the potential generational differences and similarities between the university instructor and the course learners.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score0.674

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.370
Teacher spread0.335 · 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
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
Published2023
Admission routes1
Has abstractyes

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