Design of the System Architecture of Competency-Based MOOC Using Microlearning Objects to Facilitate Upskilling and Reskilling for Industrial Workforce: Digital MicroLearn
Bibliographic record
Abstract
In response to rapid technological advancements, higher education institutions face an urgent need for innovative, competency-based learning systems to address workforce development challenges. This research aims to design and validate the competency-based MOOC system architecture using microlearning objects, referred to as the digital MicroLearn system architecture, to facilitate upskilling and reskilling for the industrial workforce. The study employs a mixed methods research (MMR) design comprising three phases: Phase 1, qualitative data were gathered through focus group discussions (FGDs) with experts and industry stakeholders to identify practical needs, challenges, and system requirements; Phase 2, design and validation of the system architecture; and Phase 3, expert review to assess the system's suitability. The findings highlight that the system capably supports competency-based microlearning activities, with Moodle as the core MOOC platform integrating H5P for interactive content, YouTube for video delivery, and cmi5 and xAPI for learner progress tracking via a Learning Record Store (LRS). The responsive interface ensures compatibility across devices, enhancing microlearning efficiency and learner engagement. Expert evaluations confirmed the architecture's suitability for industrial workforce development, with an overall mean score of 4.42 ± 0.48, indicating agreement. Future research should investigate the implementation of the system in real-world educational settings to assess its scalability and adaptability across diverse institutional contexts. Additionally, studies should examine its effectiveness in enhancing competency-based learning experiences and its long-term impact on institutional performance and learner outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".