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Record W7133037415

A Preliminary Exploration of Students' Perceptions of Their Own Learning

2018· dissertation· W7133037415 on OpenAlexaff
Cristina Bianchi

Bibliographic record

VenueTSpace · 2018
Typedissertation
Language
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsPerceptionVariety (cybernetics)Thematic analysisAffect (linguistics)Sample (material)Qualitative research
DOInot available

Abstract

fetched live from OpenAlex

Students acquire knowledge in the classroom from a variety of sources, such as the teacher, learning activities and the academic environment. Yet, there is limited research on student perceptions of their own learning in the classroom. This study is a preliminary investigation of how students aged 8 to 12 think they learn best in the classroom. A sample of 229 students answered the question 'how do you learn best at school?' Out of the 229 responses, 210 responses were used. Thematic analysis was used to examine student answers. The results demonstrated that students were able to recognize different relationships within the classroom that help them learn best. Areas defined include classroom tools, classroom management, and student readiness. Findings support that students were aware of their classroom environment and how it can affect their learning, whether it be in a positive or negative manner.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.064
GPT teacher head0.455
Teacher spread0.391 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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