The Impact of Quizizz-Based Learning on Students’ Emotional Intelligence in Islamic Education
Bibliographic record
Abstract
Objective: Research to determine the effect of the use of Games Quizizz-based applications on students' emotional intelligence in PAI and Al Islam subjects at SD Muhammadiyah PK Palur. Theoretical framework: this research focuses on the theory of emotional intelligence, which emphasizes the importance of students' ability to recognize, understand, and manage emotions in the learning process, as well as game-based learning theory, which is believed to increase student motivation and involvement. Literature review: Previous research has shown that the use of technology-based learning media and interactive games such as Quizizz is able to improve students' material understanding and social-emotional skills, but not many have specifically linked it to increased emotional intelligence in the context of religious education. Methods: This study uses a quantitative approach with a true experimental design in the form of a pre-test and post-test control group design. The sample consisted of two classes, namely classes 1A and 1B which each amounted to 23 students. Data was collected through an emotional intelligence questionnaire using the Likert scale and the score of the Quizizz game, which contained 20 questions from PAI and Al Islam materials with varying levels of difficulty. Results: The study showed that there was a significant influence of the use of the Quizizz application on the improvement of students' emotional intelligence, as evidenced by the results of the t-test, which obtained a calculated t value of 7,803 greater than the t-table of 1,708, and a significance value of 0.00 < 0.05. Implication: This study shows that the integration of game applications such as Quizizz in PAI and Al Islam learning can be an effective strategy for improving students' emotional intelligence from an early age. Novelty: this research lies in the application of game-based learning, especially through the Quizizz platform, which is directly associated with the increase in emotional intelligence in the context of religious education learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".