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Record W2893403808 · doi:10.5430/ijhe.v7n5p61

Interdisciplinary Connections Across the Curriculum: Fostering Collaborations between Freshman and Capstone Students Through Peer-Review Assignments

2018· article· en· W2893403808 on OpenAlexvenueno aff
Habiba Boumlik, Reem Jaafar, Ian L. Alberts

Bibliographic record

VenueInternational Journal of Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsCapstoneCurriculumContext (archaeology)Class (philosophy)Process (computing)PedagogyCapstone coursePsychologyMedical educationMathematics educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

Cultivating interdisciplinary connections between freshman and capstone students epitomizes a novel pedagogical approach to deepen student understanding of the learning process in a Community College environment. Within such a context, this article focuses on the outcomes of a two-semester collaborative effort that aims to establish and strengthen interactions between students at opposite ends of the academic spectrum. The work discussed focuses on an initiative in which capstone students in their culminating college class are supported in using their educational experiences to guide their first-year peers as they make the transition to college life. After discussing the creation and implementation of scaffolded collaborative assignments in which capstone students peer-reviewed freshman students work, the paper analyzes the impact of the research on student understanding of the learning process, the outcomes of self-reflection activities, student integration of knowledge and skills from diverse sources and the quality of their work in the peer-review endeavor.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.759

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 science0.0010.000
Research integrity0.0000.000
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 head0.096
GPT teacher head0.507
Teacher spread0.412 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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