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Record W4392721363 · doi:10.22318/icls2023.866245

Self-Regulated Learning Through Online Formative Assessments: The Effects of Assessment Frequency and Participation on Student Performance

2023· article· en· W4392721363 on OpenAlex
Okan Bulut, Guher Gorgun, Bin Tan, Tarid Wongvorachan, Seyma N. Yildirim‐Erbasli, Ashley Clelland

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings. · 2023
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of AlbertaConcordia University of Edmonton
Fundersnot available
KeywordsFormative assessmentComputer scienceOnline assessmentMedical educationMathematics educationPsychologyMedicine

Abstract

fetched live from OpenAlex

Using online formative assessments, students can monitor their learning and find strategies to attain their self-regulated learning (SRL) goals.However, as formative assessments are often optional and ungraded, some students may not be motivated enough to participate in such assessments.In this study, we argue that the frequency and stakes (i.e., optional vs. required) of formative assessment can influence the interplay between SRL and student performance.Using data from multiple offerings of an undergraduate course, we investigate the role of formative assessment frequency and participation rate in predicting students' course performance.Our findings showed that student performance and participation in the required formative assessments were more predictive of their course outcomes.Furthermore, increasing the frequency of formative assessments did not improve student performance.

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.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.446
Teacher spread0.404 · 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