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Record W2303583515

Is a first year multi-activity orientation program effective at developing relationships between students, their peers, staff and faculty?

2016· article· en· W2303583515 on OpenAlexaboutno aff
Natalie D. Heeney, Jess C. Dixon, Adriana M. Duquette, Tiffany Martindale, David M. Andrews

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2016
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsnot available
Fundersnot available
KeywordsOrientation (vector space)Medical educationFaculty developmentPsychologyProfessional developmentPedagogyMedicine
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT First year orientation activities at post-secondary institutions serve the purpose of facilitating peer-to-peer and peer-to-instructor relationships [1,2]. It has been shown that the creation of these relationships is positively correlated with increased student retention rates [2-4]. Several studies have evaluated activities pertaining to outdoor/physical education, small focus groups, program and faculty information sessions, and facility orientation to try and determine which are most effective in increasing student outcomes [1-4]. However, little research has been conducted to date regarding the evaluation and effectiveness of multi-activity programs. Therefore, the primary purpose of this study is to assess the effectiveness of first year orientation activities (e.g., peer mentoring, outdoor team building) in an Ontario university Kinesiology program for developing relationships between first year students and others within the department. It is hoped that by improving these relationships, there will be an associated long term increase in academic achievement and retention rates. Each winter semester over the next five years, first year undergraduate students will complete an online survey regarding their orientation experiences from the previous fall. The survey will ask students to reflect on their first year orientation experiences and provide feedback on how effective they thought each activity was at orienting them to the campus, developing relationships among their peers, staff and faculty, and other success criteria. Quantitative results will be summarized using descriptive statistics and stratified based on factors such as year and sex. Qualitative responses will be assessed using open and axial coding techniques using QSR Nvivo software. The results of these analyses will be used to modify orientation activities in future years, with the intent of improving relationships between students, staff, and faculty on a continual basis. The first set of analyzed data regarding Fall 2015 orientation will be available at the time of UWill Discover in March. REFERENCES [1] Bell, B. J. (2006). Wilderness orientation: Exploring the relationship between college preorientation programs and social support. Journal of Experiential Education, 29(2), 145-167. [2] Power, R. K., Miles, B., Peruzzi, A., & Voerman, A. (2011). Building bridges: A practical guide to developing and implementing a subject-specific peer-to-peer academic mentoring program for first-year higher education students. Asian Social Science, 7(11), 75-80. [3] Price, D. V., & Lee, M. (2005). Learning communities and student success in postsecondary education [PDF]. New York City: MDRC. [4] Wolfe, B. D., & Kay, G. (2001). Perceived impact of an outdoor orientation program for first-year university students. Journal of Experiential Education, 34(1), 19-34.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.084
GPT teacher head0.346
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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