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Record W2751868612

The Challenges and Barriers to Academic Success Immigrant Students Encounter in Primary Grades

2016· article· en· W2751868612 on OpenAlexaboutno aff
Jeevan Heir

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationPedagogyMathematics educationAcademic integrityPsychologySociologyMedical educationPolitical scienceSocial psychologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

This research study focused on the challenges and barriers to academic success that immigrant students encounter in their primary grade class. Additionally, the study investigated what resources and initiatives are available in supporting immigrant students. Qualitative research was conducted by collecting data through semi-structured interviews with two participants. Both participants work as teachers with the Peel District School Board in Brampton. From the findings, it is evident immigrant students continue to experience a number of challenges and barriers to academic success. In particular, the English language persists to be the main challenge. The implications of this study indicate a need for improvements to combat the challenges immigrant students encounter to academic success. Two main recommendations emerged demonstrating firstly, a need for more funding to allocate increased ESL (English as a second language) support time and secondly, training and support to encourage teachers’ effective use of available resources.

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)0.002
metaresearch head score (Gemma)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0080.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.305
Teacher spread0.290 · 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
Published2016
Admission routes1
Has abstractyes

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