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Record W1523324611 · doi:10.3968/6313

Application of Formative Assessment in College English Teaching

2015· article· en· W1523324611 on OpenAlexvenueno aff
xinyan Wang

Bibliographic record

VenueHigher education of social science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentComputer scienceQuality (philosophy)College EnglishMathematics educationOrder (exchange)Key (lock)Psychology

Abstract

fetched live from OpenAlex

A comprehensive, objective, scientific and accurate evaluation system is a key component in College English teaching. On the one hand, it helps the students to obtain feedback to adjust their learning strategies in order to improve their learning efficiency. On the other hand, it provides the teacher with information to better achieve the teaching goal and ensure the teaching quality. The formative assessment is in nature a procedural evaluation that aims to cover the whole growth and progress of a student in learning a specific course, to provide an accurate judgment on whether and to what extent the students have achieved their learning objectives from various aspects. It facilitates the effective monitoring of students’ autonomous learning, and is particularly important in the college English teaching. This article makes an analysis on the characteristics of formative assessment, points out the principles in implementing formative evaluation and discusses the specific approaches to applying formative assessment in the college English teaching.

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)0.063
metaresearch head score (Gemma)0.117
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.063
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.117
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
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 head0.030
GPT teacher head0.411
Teacher spread0.381 · 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 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
Published2015
Admission routes1
Has abstractyes

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