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Record W4388495702 · doi:10.5430/wjel.v14n1p1

Learning-Oriented Assessment (LOA) Implementation in EFL Tertiary Contexts: Towards a More Task-based Learning (TBL) Environment

2023· article· en· W4388495702 on OpenAlexvenueno aff
Hadeel Mohammad Al Kamli, Mansoor S. Almalki

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsEnglish as a foreign languagePsychologyMathematics educationTask (project management)Adaptation (eye)Tertiary levelMedical educationMedicineEngineering

Abstract

fetched live from OpenAlex

Over the past decade, learning-oriented assessment (LOA) has gained increasing attention as an emerging approach to classroom-based assessment. LOA prioritises learning and focuses on engaging learners actively in assessment and feedback activities. To enhance the learning environment in higher education, it is crucial for teachers of English as a foreign language (EFL) to be aware of and implement innovative assessment methods that support student learning, such as LOA. The purpose of this mixed-methods study was to investigate the knowledge, use, and challenges of LOA in tertiary contexts. A total of (93) male and female EFL teachers teaching in tertiary education participated in the study. An adaptation of The Teachers' Learning-Oriented Assessment Questionnaire survey (Alsowat, 2022) and items from semi-structured interviews (Fazel & Ali, 2022) were used to collect the data for this study. The findings of this study show that EFL teachers had good knowledge of LOA concepts but suggest that they need further focused training on the implementation of LOA. The study also shows that the teachers faced pedagogical, practical, attitudinal, and institutional challenges that prevented the better implementation of LOA practices.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.383
Teacher spread0.371 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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