DepEd Computerization Program: Venue for Improving Teachers Pedagogy
Bibliographic record
Abstract
DepEd Computerization Program (DCP) provided the public schools with appropriate technologies that would enhance the teaching-learning process (DO 78, s. 2010). However, UNESCO Institute for Statistics (April 2014) on their study of Information and Communication Technology (ICT) in Education in Asia data revealed that there were no data yet for public school teachers teaching using ICT and only two (2) percent of the public school teachers were trained to use ICT. Thus, this study was conducted to determine the level of increase in the ICT integration in the lesson after the intervention was given. The study employed the Continuous Improvement (CI) Research Method that includes three (3) main stages: Assess, Analyze and Act. Descriptive statistics were used after the survey responses. Interview and focus group discussions among students and teachers were done to further validate results. A survey questionnaire was adapted from UNESCO ICT Inventory Questionnaire. The paired t-test was used to determine the significant differences between mean scores on ICT teachers' competence. The findings of the study revealed that through mentoring program conducted by the ICT Coordinator, teachers were introduced to various computer applications such as Basic Computer System, Google Drive applications, Graphic tablet and PHET Simulations. There was a total of two hundred fifty percent (250%) increase in the teachers integrating ICT in their classes. And after the first quarter of the school year there was a significant increase in the Teachers ICT Competence. It is then recommended that teachers should make use of the multimedia instruction in their respective schools so as to help students concretize abstract concepts and processes skills which may improve students' academic achievement. Furthermore, there is a need to examine further the type of multimedia used for teaching and learning in relation to exchange of communications for students' better comprehension, as much as possible the multimedia should commensurate to students' cognitive structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".