Data Analytics for HR Students: Using RapidMiner to Develop Systems Thinking Skills
Bibliographic record
Abstract
In this study, we investigated the value of the data mining tool RapidMiner for teaching data analytics to human resource (HR) students.In a way, the idea for another analysis tool contradicts the demand for demonstrable spreadsheet skills in business professions.Fluency in MS Excel is seen as a necessary skill for data analysis in business; however, more advanced analysis like predictive analytics (i.e., predicting employee churn, salary levels, survey text analysis, etc.) leaves a gap in MS Excel's skill set.Introducing 34 The BRC Academy Journal of Business Vol. 13, No. 1 HR students to RapidMiner fills that gap.We wanted to provide students with a deeper learning experience beyond the spreadsheet to give them a competitive advantage in the job market.This study explained how faculty could use RapidMiner to teach data analytics to HR students.Based on their experience, students completed a survey that analyzed the impact of using RapidMiner on their engagement, learning satisfaction and understanding of Systems Thinking.We adapted active learning strategies and an HR data set from IBM to reduce the gap between business education and practice.We provided practical recommendations on using RapidMiner for teaching Data Analytics and as a method to develop fundamentals of systems thinking for business students.Our findings showed that using RapidMiner tool resulted in high student engagement, satisfaction, confidence and helped introduce the Systems Thinking approach.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".