Using Rich Picture to Understand the Issues and Challenges in E-Learning Environment: A Case Study of Students in Higher Education Institution
Bibliographic record
Abstract
E-learning has become an important mechanism for teaching and learning in the 21st century. However, little is known about the issues and challenges faced by students as one of the primary users of e-learning in higher education institutions. Thus, this research adopts a qualitative case study with a purposive sample of ten students in a public higher education institution to explore issues and challenges of e-learning usage. Rich Picture originated from Soft System Methodology (SSM) was applied to the case study to explore the issues and challenges faced by the students, specifically in using e-learning as a tool in their learning process. The Rich Picture can be a useful tool to understand and map complexity and to solve problems. A thematic analysis was employed to understand the intricate patterns of emerging themes for achieving patterns in the data. The findings specify that through e-learning, the issues and challenges are categorised into three main aspects which include people/human, environment and technical. Each of these elements is narrowed down into positive and negative side. The most frequently mentioned of the positive side for i) people/human element is the 'self-initiative', ii) environment element shows the 'encouraged students to learn at their own speed' and iii) technical element shows' accessible at anywhere and anytime'. Meanwhile, the most cited for negative side for i) people/human element is the 'lack of discipline', 'struggle to understand course topic' and 'lack communication skills', ii) environment shows 'distraction' and iii) technical element shows' poor internet connection'. The findings contribute to the theoretical perspective by utilising the Rich Picture of SSM. Following the Rich Picture approach, this research produces a picture with a holistic view of the issues and challenges in e-learning usage among students. Practically, the findings outlined in this research could be a reference to university management to revise the e-learning system plans and improve the platform for better students learning experience.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".