Effect of Diagnostic Testing on Students’ Achievement in Secondary School Quantitative Economics
Bibliographic record
Abstract
This study aimed at investigating the effect of diagnostic testing on students’ academic achievement in secondary school quantitative economics. In conducting the study, 3 research questions and 3 stated hypotheses were answered. The study is quasi-experimental employing 2x4 factorial pretest-posttest design. The sample consisted of 210 Senior Secondary 3 (SS3) economics students in the four co-educational schools purposely selected from Nnewi Education Zone of Anambra State in Nigeria. They were allocated to 3 experimental groups and 1 control group. Students’ responses to two instruments titled Diagnostic Quantitative Economics Skill Test (DQEST) and Test of Achievement in Quantitative Economics (TAQE) constituted relevant data for the study. Instruments for data analysis were t-test and ANCOVA. Results of the analysis indicate a significant effect of treatment on students’ achievement in favor of DQEST with feedback and remediation group only (F (3, 209) = 22.3114, p > 0.05). Gender made no significant difference on students’ achievement in TAQE. Thus, diagnostic tests are effective when used with feedback and remediation. The use of DQEST with feedback and remediation in teaching and learning of quantitative economics is therefore recommended.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".