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Record W2127785047 · doi:10.5539/elt.v8n2p109

An Investigation of Saudi English-Major Learners’ Perceptions of Formative Assessment Tasks and Their Learning

2015· article· en· W2127785047 on OpenAlexvenueno aff
Muhammad Umer, Abdul Majeed Attayib Omer

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentSummative assessmentPsychologyScope (computer science)SyllabusKnowledge surveyMathematics educationCurriculumAssessment for learningPerceptionPedagogyMedical educationComputer science

Abstract

fetched live from OpenAlex

The effect of standardised and summative assessment on teaching and learning has been explored in various settings. Formative assessment or classroom assessment, however, has not captured considerable attention of washback researchers. The prime goal of the inclusion of formative assessment in the assessment regime of a curriculum is to allow learners to grow as independent learners. This study investigated if learners’ perceptions of formative assessment tools influenced their learning strategies, the scope of what they learned, and the depth of their learning. The results of a survey, distributed among 400 Taif University English-major female learners (TUEMFL) showed that the respondents preferred formative assessment tasks to comprise expected questions in the form of multiple-choice questions. In addition, formative assessment tasks narrowed down the scope of the syllabus the learners studied. However, the participants deemed formative assessment helpful in diagnosing and improving their mistakes. Therefore, it is suggested that the nature formative assessment tasks should synchronise with their course objectives to help learners improve their academic skills. This may mean that the assessment tasks should be more authentic in nature and should have a greater consequential validity replacing the multiple-choice questions which often culminate in surface-level learning.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.130
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.335
Teacher spread0.316 · 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 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

Citations7
Published2015
Admission routes1
Has abstractyes

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