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Record W4313526014 · doi:10.1108/jarhe-09-2020-0313

First-year interdisciplinary science experience enhances science belongingness and scientific literacy skills

2023· article· en· W4313526014 on OpenAlexaffabout
Anna Rissanen, John Hoang, Michelle Spila

Bibliographic record

VenueJournal of Applied Research in Higher Education · 2023
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of AlbertaMacEwan University
Fundersnot available
KeywordsBelongingnessCurriculumCapstoneSummative assessmentMathematics educationScientific literacyFormative assessmentPsychologyLiteracyMedical educationPedagogyScience educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

Purpose The goals of this research study included evaluating the outcomes of Interdisciplinary Science Threshold Experience (InSciTE) on student experience of science discipline, level of sense belongingness to a large Faculty of Science (FoS), outcomes in learning science literacy skills and whether a student's background played a role in the differences of effects of the high-impact teaching practices. InSciTE was designed to facilitate the transition from high school to a large research-intensive university, and specifically to a FoS with over 6,000 undergraduate students. Design/methodology/approach The FoS in a Canadian university engaged in the development of a *9 credit program bundling foundational statistics and chemistry courses with integration of aspects of mathematics and biology or physics to create a new first-year, academic interdisciplinary experience called InSciTE. This project-based curriculum emphasized teamwork and leadership, and presented complex interdisciplinary challenges facing today's world. A team-teaching environment consisting of instructors, a lab coordinator and teaching assistants was instrumental for the core InSciTE courses. In addition, the authors utilized a variety of learning practices with interdisciplinary themes to meet the learning outcomes. Course activities included field experience and tours, blended learning and flipped lectures, guest speakers, discovery-based lab activities, group discussions and projects, a capstone research project, and a combination of formative and summative assessments. The authors proposed two hypotheses for the evaluative study; first that the high-impact practices (HIP) will improve students’ experiences and belongingness to science faculty, and second that InSciTE facilitates learning of scientific literacy skills. To assess the effectiveness of InSciTE, the authors used two surveys, the first being the Test of Scientific Literacy Skills (TOSLS), which measures skills related to major aspects of scientific literacy: recognizing and analysing the use of methods of inquiry that lead to scientific knowledge and the ability to organize, analyse, and interpret quantitative data and scientific information. The second survey examined student belongingness, motivation and autonomous learning, combined with demographic data questions. Findings The results suggest that InSciTE students reported higher feelings of relatedness, group membership, and career aspirations and performed better on the TOSLS compared to students in other science courses. Originality/value As a leader in interdisciplinary science, the FoS at a Canadian university developed a full-year course bundling foundational statistics and chemistry courses with integration of some aspects of mathematics and biology or physics to create a new first-year, academic interdisciplinary experience called InSciTE. This project-based curriculum emphasized teamwork and leadership, and presented complex interdisciplinary challenges facing today's world aiming to facilitate transition from high school to a research-intensive university.

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)0.025
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.010
Science and technology studies0.0010.004
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.110
GPT teacher head0.541
Teacher spread0.431 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2023
Admission routes2
Has abstractyes

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