Influence of Students’ Understanding and Goal Commitment on Academic Achievement in Introductory Technology in Akwa Ibom State, Nigeria
Bibliographic record
Abstract
The study examined the influence of students’ understanding and goal commitment on their academic achievement in Introductory Technology in secondary schools in Akwa Ibom State, Nigeria. An ex-post facto survey design was used and a random sample of 2,500 junior secondary three (13-14 years old) students from a population of 48,302 JSS three students in the state public schools in 2008/2009 session. Data on independent variables were gathered with researchers – developed instrument called Students’ Understanding and Goal Commitment in Introductory Technology (SUGCIT). The instrument had a Kuder-Richardson (KR-21) computed reliability index of .86. The data on students’ academic achievement were obtained from Introductory Technology examination results of first semester 2008/2009. Two null hypotheses were tested at P<.05 using the Z-test statistics and multiple analysis of variance. Results showed that: 62.4 per-cent of respondents did not understand the concept of Introductory Technology, while 37.6 per-cent did; Students who understood the concept of Introductory Technology had higher academic achievement in the subject than those who did not; 70 per-cent of the respondents were not committed to the pursuit of engineering/technology after the high school, while 30 per-cent were committed; Students who were committed to technology had higher achievement in Introductory Technology than those who were not. It was recommended that more proactive policies should be put in place by government and other agencies to provide technology – friendly environment and qualified staff in schools for effective teaching of Introductory Technology in order to stimulate youth interest in technology.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 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".