Integrating Facebook Social Network for the Statistics Course: Its Outcomes of Undergraduate Students’ Prince of Songkla University Pattani Campus, Thailand
Bibliographic record
Abstract
The objective of the study was to integrate Facebook social network for the Statistics course of undergraduate students’ Prince of Songkla University Pattani campus, Thailand. The study investigated the interaction of the students’ academic learning with Facebook social network and the relationship between the laboratory scores, the achievement scores, and the interactive scores of learning with Facebook social network. Then, the study compared the difference of the student’s achievement scores after learning with Facebook social network by group interaction with laboratory score functioning as a covariate, and evaluated satisfaction of students who are learning with Facebook social network. The participants were the twenty-nine second year’s undergraduate students majoring in Information Technology and Educational Evaluation of Faculty of Education, Prince of Songkla University Pattani campus, Thailand. The research instruments were teaching course plans with questions for being posted on Facebook wall, the mid-term examination test, the record form of learning interaction, and the student’s satisfaction questionnaire. From this study, although the average of the achievement scores are not different when classified by the interaction group in which the covariate variable as the laboratory score, the use of Facebook integrated in teaching is an effective tool in increasing the students’ interaction as seen from the relationship among those of three variables. Implications of the results are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".