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Record W7161937797 · doi:10.82308/46387

Individual instructor's perceptions of teaching context : identifying facilitators and barriers to completion of teaching projects

2001· dissertation· en· W7161937797 on OpenAlexaboutno aff
Katherine. Moxness

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleContext (archaeology)PerceptionProfessional developmentHigher educationWork (physics)Scale (ratio)Faculty development

Abstract

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Frameworks seeking to explain teaching competency and development in higher education indicate that context and personal perspectives, as well as knowledge and action are crucial components in the understanding of how and why faculty teach as they do and how development may be encouraged and may be supported. This study sought to contribute to a deeper understanding of individual instructors' perceptions of context of higher education as it related to their teaching projects. This study investigated the daily pursuits and pre-occupations (teaching goals/projects) of an individual instructor, specifically, the instructional demands, departmental demands, the personal and professional pursuits of knowledge and the pursuits of pedagogical knowledge. More specifically, this study investigated perceived facilitators and barriers to the realization of individual teaching and other work projects. Nineteen full-time faculty members in the Departments of Physiotherapy, Occupational therapy, Nursing, Social Work, Educational Psychology and Education at a large research and teaching university in Montreal, Quebec participated in this study. The instructors were asked to complete an adapted version of Little's (1983) Personal Project Analysis (P.P.A.) instrument, which is designed to elicit an instructor's current pre-occupations or projects in his or her current context. The instructors were asked to rate these projects (seven teaching projects and seven other work projects) using a Likert scale (0 to 10) on twenty-one empirically supported dimensions. These dimensions included the following: enjoyment, difficulty, control, initiative, stress, time pressure, outcome, self-identity, others' view, value congruency, challenge, commitment, competence, support, self-worth, fun, others' benefit, self-benefit, supportiveness of culture (departmental level), hindrance of culture (departmental level), and overall current satisfaction. Instructors were asked to assess their perceived conflicts between two of their teaching projects and two of their other work projects in addition to completing a demographic questionnaire. The findings indicate that instructors identified five different types of daily pursuits that formed and defined their teaching context, as they perceived it. These five types of daily pursuits (projects) included: course planning and preparation projects; student investment, support and delegation of tasks to student projects; knowledge building and knowledge sharing projects; committees, faculty support and faculty teaching projects; and finally, teaching strategy projects. The instructors also identified five different types of daily pursuits that formed and defined their other work context. These included: publishing, conference presentation and research projects; grant proposals and funding projects; office organization projects; correspondence, university committees, outside mandates, departmental expectations and management of student and faculty projects; and finally, personal objectives and technical skill building projects. P.P.A. enabled the researcher to identify on an individual instructor level the instructor's perceived facilitators and barriers to the successful completion of teaching and other work projects. Furthermore, P.P.A. as a faculty development instrument or as an alternative to semi-structured interview methods is supported by the findings.

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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.107
GPT teacher head0.441
Teacher spread0.334 · 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 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
Published2001
Admission routes1
Has abstractyes

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