The Effect of Social Media on Student Learning Motivation Using the Apriori Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The success of student learning can be determined by their motivation. Students who have high learning motivation tend to have high achievement as well, otherwise their learning motivation is low, their learning achievement will also be low. student learning motivation in the subject is very low. Some students prefer to play social media rather than pay attention to the material explained by the teacher during class hours. Therefore, this study aims to explore the influence of social media on students' learning motivation. This research uses data mining method with Apriori algorithm to identify patterns related to social media usage and students' learning motivation. The Apriori algorithm is one of many algorithms in data mining that is used for frequent itemsets and association rules in databases on transactional data that are generated by identifying each item that exists, and combining larger sets of items provided that the items appear frequently enough in the database. Based on the research that has been done, the author can draw the conclusion that using the Rapid Miner 7.1 application tools in applying the apriori algorithm produces the same rules as manual calculations using 300 data on the learning motivation of Abdi Negara Binjai SMKS students and the system can generate association rules using 300 student learning motivation data with a minimum support of 12% and a minimum confidence of 75% and produce 5 association rules 3 itemsets to determine the learning motivation of Abdi Negara Binjai SMKS students. One of the rules that has the highest confidence value is, if YT and J2 then M1. Which means that every student who uses YOUTUBE Social Media with a length of use is 3-4 HOURS then INCREASES STUDY MOTIVATION. Then the less the ɸ (frequent) value is set, the more data that can be processed, as well as the minimum support value and confidence value, where the smaller the value determined, the more association results will be issued.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it