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Record W3216960811 · doi:10.7939/r3-rwzs-rm42

A Case Study of MicroSociety Students: Engaging Learners in a “Real-World” Learning Community

2021· article· en· W3216960811 on OpenAlexaboutno aff
Albert Brent Galloway

Bibliographic record

VenueUniversity of Alberta Library · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPedagogyComputer scienceSociologyPsychology

Abstract

fetched live from OpenAlex

This case study examined the perspectives of six students who participated in the MicroSociety program as elementary-aged children. MicroSociety is an experiential program designed to provide authentic learning experiences that help prepare students for life beyond school. The purpose of this study was to investigate how these young people experienced learning within this program and how these experiences contributed to their feelings of engagement and disengagement as students in a MicroSociety school. The theories and research of John Dewey (1938) and Jerome Bruner (1960) provided the theoretical and conceptual framework for this study. Through his theory of experiential education, Dewey advocated that learners should learn by “doing” through exploration, problem solving, collaboration and making decisions as members of their communities. Bruner also saw the value in student-centered approaches to learning that allowed students to construct their own meanings. He saw the value in students working collaboratively and believed they had a role to contribute as part of a culture of learning. Both researchers saw the importance of providing authentic and relevant learning experiences that could motivate and engage learners. The MicroSociety (n.d.) program aligned well with this framework, as evidenced by their mission to create learning experiences that motivate children to learn and be successful by engaging them in their communities and in real life. Following data analysis, an additional framework centered on communities of practice from the research of Jean Lave and Etienne Wenger (1991) was added to complement my study findings. The review of the literature in this study focused on student engagement, student disengagement, and the MicroSociety School experience. Recent Canadian evidence has indicated that many students are disengaged as learners, and that this continues in a downward trend throughout the middle years and onto high school. Disengagement may lead to a lack of enjoyment of school/learning or students who are simply bored with the experiences presented to them. Research on MicroSociety has rarely accounted for the student perspective, with little evidence available to suggest how or whether it contributes to student engagement. My primary interest in this case study was to understand how students experienced learning in a MicroSociety school. I collected data through interpretive inquiry interviews involving participants who had taken part in the program as elementary-aged children. This study suggests the MicroSociety program, as experienced by my participants, was engaging and empowering for them. This appeared to be due to the provision of real-world learning opportunities that replicated the adult world and provided participants with roles and responsibilities that allowed them to develop competence and confidence. Most importantly, participants became contributing members of a community of practice that, as they have indicated, continues to have a meaningful impact on their lives.

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)0.005
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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0190.008
Scholarly communication0.0050.006
Open science0.0040.008
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.291
Teacher spread0.267 · 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 designCase report
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
Published2021
Admission routes1
Has abstractyes

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