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Record W4380987870 · doi:10.24918/cs.2023.26

Facilitating Scientific Literacy Through Writing: A Write-to-Learn Assignment for Large Introductory Undergraduate Biology Courses

2023· article· en· W4380987870 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCourseSource · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersUniversity of Toronto ScarboroughDirectorate for Biological Sciences
KeywordsCuriosityCritical thinkingMathematics educationMetacognitionLiteracyProcess (computing)Writing processScientific literacyVariety (cybernetics)PedagogyPsychologyComputer scienceScience educationSocial psychologyCognition

Abstract

fetched live from OpenAlex

Write-to-learn (WTL) assignments have been used in a variety of disciplines to encourage conceptual learning and critical thinking in undergraduate education. These assignments focus on facilitating rather than assessing learning. Conversely, write-to-communicate (WTC) assignments (<em>e.g.,</em> lab reports and exams), often with the goal of assessing learning, are more commonly employed in foundation STEM courses. We developed a WTL assignment that focuses on promoting curiosity driven learning, critical thinking, and metacognition; skills that promote students&rsquo; scientific literacy through writing. We integrated theoretical frameworks for scientific literacy, that include the sub-constructs of <strong>third space</strong>, <strong>authenticity</strong>, and <strong>multiple discourse</strong> as well as <strong>science as a human endeavour</strong>, and <strong>metacognition and self-direction</strong> (<a href="#1" rel="nofollow">1</a>, <a href="#2" rel="nofollow">2</a>) to develop this 3-part WTL assignment. In this assignment, students first select a topic of interest and write freely on their current understanding of the topic (Part 1). They then develop a research question based on their writing and seek answers to their question from published literature (Part 2). Finally, they reflect on their overall experience with the WTL process and propose further avenues of investigation for their research topic (Part 3). Student feedback suggests that they enjoyed the WTL process and their overall satisfaction with the structure of the assignment was high. As we continue to evolve the assignment based on student feedback, we are gratified that students reported high self-efficacy with regard to future writing as a result of participating in this assignment. We recommend use of this type of WTL assignment in large, introductory STEM courses, so as to facilitate rather than simply assess students&rsquo; learning. <em>Primary Image:</em>&nbsp;Scientific literacy through writing. Schematic depicting a write-to-learn assignment format implemented in an introductory undergraduate biology course, along with corresponding science literacy constructs.

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.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.052
GPT teacher head0.401
Teacher spread0.349 · 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