Technological Tool for Formative Assessment in Higher Education: ZipGrade
Bibliographic record
Abstract
This descriptive study examines the effectiveness of ZipGrade, a digital assessment tool, in the context of formative evaluations within classroom settings, focusing on its deployment for multiple-choice question quizzes. This research contributes to the dialogue on the integration of information and communication technology to promote quality education and address the literature gap in providing immediate feedback to enhance the learning outcomes. Drawing on a sample of 63 fourth year B.Ed. students in Bahrain, the study combines quantitative and qualitative methodologies to assess student perceptions about the utility and effectiveness of ZipGrade. Data were collected through a semi-structured questionnaire following the administration of a series of formative tests across selected course segments. The findings reveal a predominantly positive reception of ZipGrade among students, highlighting its ease of use, immediate feedback provision, and potential to more effectively engage learners in the assessment process. Challenges such as the necessity for physical printing of answer sheets, a predisposition towards multiple-choice questions, and infrastructural and policy-related barriers were identified, suggesting areas for further development and support.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".