Exploring the difficulties in learning ERP systems from students’ perspective: The case of Oracle E-Business Suite ERP
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores and analyzes students’ difficulties in learning an ERP system to help design more appropriate teaching methods and materials. Global enterprises have widely used ERP systems to manage their operations effectively and efficiently. Hence, many business schools have offered courses on ERP systems to sharpen ERP skills for their students. To help design more appropriate teaching methods and materials for ERP learning, one must know students’ difficulties in understanding. This study analyzes students’ difficulties in learning the Oracle E-Business Suite ERP system through interviews and qualitative analysis. As a result, this study identifies five categories of problems in the various areas of the Revised Bloom’s Taxonomy. Their relevant educational objectives can guide the redesign of ERP teaching methods and materials. One of the difficulties belongs to the area of Remember Factual Knowledge. The rest of them are in Understand, Remember, Apply, and Analysis of Procedural Knowledge. Lastly, this study provides some implications for teaching ERP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it