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(Re)Visiting the Efficacy of PBLA for Adult EAL Learners in College-Based Education in Canada

2024· book-chapter· en· W4401993837 on OpenAlexaffabout
Md Shayeekh-Us Saleheen, Mousume Akhter Flora, Md Nazim Mahmud

Bibliographic record

VenueAdvances in higher education and professional development book series · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsSeneca PolytechnicUniversity of Manitoba
Fundersnot available
KeywordsMedical educationEvaluation methodsMedicineEngineering

Abstract

fetched live from OpenAlex

This chapter provides a comprehensive analysis of the benefits and challenges associated with the implementation of Portfolio-Based Language Assessment (PBLA) in Language Instruction for Newcomers to Canada (LINC) programs across Canada. In addition, it delves into the effectiveness of integrating PBLA into teaching and assessment practices in college-based LINC education programs in Canada. The objective is to evaluate the effectiveness of PBLA in enhancing the overall quality of LINC education in Canada. The chapter commences with an informative overview of PBLA and LINC program, drawing from relevant literature. Subsequently, the authors share their valuable insights and perceptions regarding PBLA, based on their extensive experiences. Collaborative autoethnography served as the authors' methodology for exploring PBLA, with data derived from multiple sources. Furthermore, their effective collaboration as a trio involved frequent meetings, where they exchanged their perspectives, experiences, reviewed pertinent literature, and critically reflected on their research findings. Overall, the chapter offers a rich and detailed account of PBLA and LINC programs, underlining their significance in the field of language teaching. Finally, the chapter offers some valuable insights and recommendations for both teachers and students regarding the future implementation of PBLA in LINC programs in Canadian college-based education.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.913
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.020
GPT teacher head0.347
Teacher spread0.328 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes2
Has abstractyes

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