The Effects of Future Skills Development through Constructivist Learning using Virtual Stores for Higher Education
Bibliographic record
Abstract
This study aimed to develop and evaluate the effectiveness of the LADEC model in promoting future skills among undergraduate learners. The research objectives included: (1) analyzing the components of the LADEC model; (2) designing and implementing the model; (3) comparing the effectiveness of the LADEC model with the DTP model; and (4) assessing learners' future skills in digital entrepreneurship regarding design and creativity. A total of 102 learners enrolled in the e-commerce and digital marketing course were divided into an experimental group of 52 participants (LADEC model) and a control group of 50 participants (DTP model). The results showed that the LADEC model comprises three core elements: (1) learning management, (2) learning environment, and (3) constructivist learning theory. Expert evaluation confirmed the model’s suitability for fostering future skills (mean = 4.76, SD = 0.42). Statistical analysis revealed that learners in the LADEC group demonstrated significant improvement in academic achievement and future skills (t = 14.30, p <.05, large effect size). Although there was no statistically significant difference in overall future skill development between the LADEC and DTP groups, the LADEC model notably enhanced learners’ creativity and digital entrepreneurship skills.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".