IMPACT OF CONTINUOUS ASSESSMENT ON PRIMARY EDUCATION STUDENTS’ ATTITUDE TOWARDS LEARNING IN TERTIARY INSTITUTIONS IN ANAMBRA STATE
Bibliographic record
Abstract
This study investigated the influence of continuous assessment (CA) on primary education students' attitudes towards learning in tertiary institutions in Anambra state.The study assessed various factors such as CA practices, resource availability, and student-lecturer ratios.The study employed a descriptive research design.A questionnaire with 40 items, validated by experts, was used.Distribution of the questionnaire was facilitated through virtual platforms namely Google online survey system, shared across WhatsApp, Instagram, Facebook groups, and other social media channels.A total of 63 correctly filled questionnaires were received, extracted from the Google platform, and transferred to Microsoft Excel for coding.Subsequently, the coded variables and data were analyzed using SPSS software, employing mean and standard deviation calculations.Findings reveal that CA positively impacts students' attitudes by providing regular feedback, promoting self-awareness, and reducing test anxiety.However, challenges such as resource scarcity, time constraints, and high studentlecturer ratios hinder effective CA implementation.The study emphasizes the importance of collaborative efforts from educational stakeholders to address these challenges and enhance the positive impact of CA on student learning experiences.Further research is recommended to explore additional factors influencing students' attitudes towards learning in tertiary institutions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".