Blackboard System and Students’ Academic Performance: An Experimental Study in The Philippines
Bibliographic record
Abstract
Purpose: The main purpose of this study is to determine the Blackboard System's effectiveness on students' academic performance in Araling Panlipunan. Research methodology: The study employed a quasi-experimental pre-test and post-test non-equivalent group design and was entirely quantitative. Seventy (70) Ligaya High School students in Grade 7 who were divided into the control and experimental groups made up the study's subjects. Both groups received instruction on related subjects throughout the first quarter of the Araling Panlipunan grading period. The t-test for dependent and independent samples was the statistical tool employed to evaluate the hypothesis. Results: Results indicate that using the Blackboard System to teach Araling Panlipunan is a more effective approach than using the traditional lecture technique. Further, it has a significant impact on students and learning processes. Teachers also gained great benefits in using this system since it provided them an easy way of tracking student progress reducing a lot of paperwork load. Limitations: This study was limited to only Grade 7 Araling Panlipunan learners in the school year 2019-2020. The duration of the experiment was only focused on the First Quarter grading period. Contribution: One of the key goals of the study is to raise the standard of education by using new technological trends, which will assist them to advance their skills and competencies in technology. Teachers will also benefit from the study to help them improve their craft with the use of effective pedagogy with ICT to cope with the changing world.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".