Comparing the Impact of Intangible and Tangible Rewards on the Academic Achievement of Grade- 9 Economics Students: An Experimental Study
Bibliographic record
Abstract
ABSTRACT As our world evolves at a rapid pace, education remains a vital catalyst for both societal progress and individual growth. As students navigate increased demands and challenges, educators and scholars have made it their mission to uncover avenues for enhancing academic achievement. A particularly noteworthy aspect to examine is the influence of rewards on student motivation and accomplishments. This study delves into a comparison between tangible rewards, such as certificates and merit cards, and intangible rewards, such as recognition and praise, on the academic performance of Grade 9 Economics students at Alugan National School of Craftsmanship and Home Industries in Brgy. Alugan, San Policarpo, Eastern Samar during the second quarter of the 2023-2024 academic year. Using systematic random sampling, a total of 80 students from two sections were selected to participate in a post-test-only experimental design. One section was assigned to receive tangible rewards, while the other received intangible rewards. The subsequent achievement test was used to evaluate their academic achievement. The results revealed significant disparities between the two groups. On one hand, students who received tangible rewards demonstrated significantly higher scores on the post-test (89.85, SD = 1.28) than those who received intangible rewards (87.48, SD = 1.022). These results were further supported by the statistical analysis, which showed a strikingly low p-value (< .00001) and a large t-value of -9.15, clearly rejecting the null hypothesis. This unequivocally proves that tangible rewards have a greater impact on academic achievement among Grade 9 Economics students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".