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Record W2989744490 · doi:10.22329/jtl.v13i2.5971

Finnish Upper Secondary School Students’ Perceptions of Their Teachers’ Assessment Practices

2020· article· en· W2989744490 on OpenAlexvenueno aff
Toni Mäkipää, Najat Ouakrim-Soivio

Bibliographic record

VenueJournal of Teaching and Learning · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionLiteracyMathematics educationPsychologySchool teachersMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

The paper addresses Finnish students’ perceptions of assessment practices in upper secondary school. We study what experiences students have about assessment, and how they assess their ability to use and understand teachers’ feedback. The data were gathered on a web-based questionnaire to 918 students in four upper secondary schools. The questionnaire contained both closed-ended and open-ended questions. According to students’ responses, most students consider that they are able to use and understand their teachers’ feedback, and that teachers are prone to apply traditional assessment methods. The results pave the way for enhancing versatility in assessment practices. At the end of this paper, we will discuss the important role of assessment in teaching and how teachers’ assessment literacy could be enhanced and made more visible. We also ponder whether alongside teachers’ assessment literacy we should also consider students’ assessment literacy.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0050.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.397
Teacher spread0.360 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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