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Record W2996204806 · doi:10.11575/prism/36970

Student Engagement of First Year Students at the University of Calgary in Qatar

2019· dissertation· en· W2996204806 on OpenAlexaboutno aff
Mohamoud Adam

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationStudent engagementPedagogyPsychology

Abstract

fetched live from OpenAlex

The purpose of this case study was to examine the University of Calgary in Qatar’s (UCQ) nursing program to discover, through an analysis of the experiences of first-year undergraduate students in the program, factors that promote and hinder student engagement. The aim of this study is to suggest how it may be possible to better nurture, support, and promote student engagement in first-year students. From a constructivist–interpretive paradigm, a qualitative dimension approach was used for this project. A purposeful sampling technique was employed to select first-year undergraduate students. Furthermore, interviews were conducted to serve as data sources.The interview data were analyzed using a thematic analysis methodology. Five major themes were identified as factors impacting engagement in first-year undergraduate students. The first theme was the importance of personality development and emotional support. The second was the role of motivation, under which there were five subthemes: giving back to society, giving back to Qatar, family expectations, personal growth, and development and recognition. The third was teaching and learning, a major factor impacting student engagement, and three subthemes were identified: the course material, instructor/student relationships, and teaching strategies. The fourth was institutional (internal) factors, and four internal factors were highlighted: language, student counseling support, the Learning Commons, and financial support. The fifth was the impact peers (other UCQ students) had on student engagement.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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)0.0060.000

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 head0.129
GPT teacher head0.469
Teacher spread0.340 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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