PROJECT TALA (TECHNOLOGY-BASED ACTIVITY AND LEARNING ASSISTANT): AN INTERVENTION TO MAXIMIZE PERFORMANCE OF ICT LEARNERS IN MODULAR DISTANCE LEARNING
Bibliographic record
Abstract
This study aimed to develop an intervention to maximize the performance of ICT 12-Shannon learners in Computer Programming during the first semester of S.Y. 2021-2022. The study involved 56 learners to gather data and utilized the descriptive research design. Based on the findings, learners experienced difficulties in Computer Programming specifically in one of the learning competencies during the second quarter which is apply basic of java language. To address this problem, the researcher utilized chatbot technology and came up with Project TALA (Technology-based Activity and Learning Assistant) which provides interactive and immediate responses when prompted by learners and delivers the content of the learning activity sheet. It is an automated instructional material that can be accessed by learners through the messenger application. The learner should provide the lesson code to prompt TALA to deliver the lesson. There will be two options on how to go through the lesson; text/image format and video format. The content is based on the learning activity sheet for week six of the second quarter with the learning competency of apply basic of the java language. TALA can also provide supplementary learning materials and additional examples of the lesson. Learners are expected to give their answers to the activity in real-time. After the implementation of Project TALA, it was revealed that there is an improvement in the performance of the learners as revealed by Pre-Test and Post-Test results. With this result, the researcher recommends TALA be replicated in other learning areas.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".