Student Self-Assessment in Higher Education: The International Experience and the Greek Example
Bibliographic record
Abstract
This study is a review of 34 empirical studies internationally and in Greece from 2008-2018 and aims at investigating:a. the implementation of student self-assessment in Higher education and the outcomes on students, b. the ability ofstudents to self-assess accurately and the factors that affect this ability. According to the main findings, self-assessmentis implemented through various ways that include inter alia electronic and non-electronic self-assessment tools.Internationally, most studies have examined and proved the contribution of student self-assessment to improvement ofperformance and learning. Moreover, self-assessment develops self-regulating learning, increases self-confidence,motivates students to ask guidance from their professors and help from their peers, increases self-efficacy, students’awareness of self-assessment ability and self-control, makes students change attitudes towards course, preparesemployability skills of students, reduces anxiety for assessment, increases students’ responsibility about theirlearning, makes them have a critical view on their work and develops critical thinking skills. In Greece, it was foundonly one study that examined the implementation of student self-assessment in Higher education and its impact onstudents and findings indicate that self-assessment through a quiz improves performance, self-regulation, motivatesstudents to try more and helps them identify gaps in their learning. Student self-assessment ability and factors thataffect this ability have been examined only internationally, so in Greece there is a research gap concerning theseparameters. Tertiary students can self-assess accurately and this ability depends on specific factors such asconfidence, prior achievement, learning style, scaffolding from professors, training, dialogical interaction anddynamic assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".