Analysis of Facebook in the Teaching-Learning Process about Mathematics Through Data Science
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 aim of this quantitative research is to analyze the impact of Facebook in the teaching-learning process in financial mathematics education, using data science, machine learning, and neural networks. The sample is composed of 46 students from the Bachelor of Administration, Commerce and Marketing program at La Salle University. The results of machine learning (linear regression) indicate that sending messages, watching instructional videos, and publishing exercises on Facebook supports the teaching-learning process in financial mathematics. Likewise, data science identified six predictive models for the use of Facebook in the educational context, by means of the decision tree technique. Analysis using neural networks identified the influence of sending messages, watching instructional videos, and publishing exercises on Facebook during the assimilation of knowledge and development of mathematical skills. Finally, Facebook is a technological and communication tool that transforms the organization of teaching and learning activities in financial mathematics education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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