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Record W2145890180 · doi:10.5539/jel.v3n3p1

(Dis)advantage and (Dis)engaged: Reflections from the First Year of Secondary School in Australia

2014· article· en· W2145890180 on OpenAlexvenueno aff
Greg Neal, Nicola Yelland

Bibliographic record

VenueJournal of Education and Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPerceptionAffect (linguistics)Intervention (counseling)Student engagementSecondary educationPedagogyAcademic achievementMathematics educationMedical educationMedicine

Abstract

fetched live from OpenAlex

Adolescents continue to be at risk of disengaging from formal education, particularly in the transition year from primary to secondary schooling. This is a critical time in their education journey and can affect their ongoing academic performances. This paper reflects on the initial findings of a project to gauge students’ levels of engagement in the first year of secondary school (12-13 years of age). The project was undertaken with students in 4 schools in two Australian states, located in low socio economic areas. Approximately 80 students from the 4 schools were selected to participate in an intervention project with specific targeted activities that aimed to increase levels of engagement in schooling to eventually aid them to aspire to desired career choices. A mixed methods research approach enabled us to capture, analyse and report on the participating students’ perceptions in terms of their attitudes towards schooling, their academic performance, and selected aspects of school life. We also interviewed their parents and teachers about these topics. The results indicated that there were some changes in attitudes towards schooling for some individuals, but generally the majority of student engagement levels remained static or tended to be negative. This remains a cause of concern for educators who are trying to find ways to encourage students to be more engaged with formal education that supports their career aspirations.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.032
GPT teacher head0.356
Teacher spread0.324 · 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 teacher head, 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".

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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