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Record W4313425625 · doi:10.5430/jct.v12n1p27

Utilisation of High-Impact Educational Practises (HIPS) to Engage Undergraduates: A Preliminary Case Study

2023· article· en· W4313425625 on OpenAlexvenueno aff
Subashini K. Rajanthran, Walton Wider, Ling Shing Wong, Choon Kit Chan, Siti Sarah Maidin

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumVariety (cybernetics)Medical educationDescriptive statisticsPsychologyMathematics educationMedicinePedagogyComputer science

Abstract

fetched live from OpenAlex

The purpose of this preliminary study is, firstly, to identify the most preferred high-impact educational practises (HIPs) among undergraduates and, secondly, to recommend best practises and strategies for implementing HIPs in higher education. This study included 61 undergraduates from a variety of degree programmes that implemented HIPs in one general studies course. Descriptive statistics and frequency were used to analyse the data. The findings revealed that the most popular HIP among undergraduates is service/community-based learning (SBL), and the least preferred HIP among undergraduates is Intensive Academic Writing (IAW). This study's findings are critical for preliminary understanding of the importance of learning styles in order to be effective and sensitive in teaching and learning, to have flexible and diverse instructional planning, and to diversify teaching methods. This was a preliminary case study that emphasised the significance of HIPs in the higher education curriculum and their implementation for a positive academic learning experience among undergraduates.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.397
Teacher spread0.344 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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