1. Data minimization
Only the variables required for workshop design were retained: prior experience flags, Likert confidence ratings, knowledge-check scores, anonymous indices, and aggregate open-response themes.
Anonymization and analysis method
How the dashboard protects students while preserving instructional insight
The original questionnaire contained direct identifiers. This app does not include the raw CSV and does not expose names, registration numbers, contact details, batch labels or raw open-ended responses.
2. Removed identifiers
Name, registration number, email/contact, exact timestamp and year/batch labels were removed. Open-ended responses were coded into themes and not displayed verbatim.
3. Pseudonymous records
The scatterplot uses anonymous IDs such as P001. These are not linkable to the original respondent identities in the app package.
4. Aggregation threshold
The dashboard emphasizes group patterns, percentages, and means. It avoids individual text examples and small-group identity labels.
5. Thematic coding
Open-ended answers were coded into non-identifying themes such as recruitment, privacy, gamification, IoT, responsible AI and practical HRM tools. A response could belong to more than one theme.
6. Interpretation limits
The survey covers one cohort of 95 students. Findings should be used for instructional design, not as a generalizable national estimate of Sri Lankan HRM students.
Dataset included in this app
Privacy-safe contents only
IncludedAggregated counts, means, theme counts, anonymous respondent metrics for filtering and scatterplots.
ExcludedRaw CSV, names, registration numbers, contacts, batch labels, raw free-text responses, exact timestamps as identifiers.
Use caseWorkshop design, pre-primer facilitation, misconception clinic, literature comparison, and stakeholder briefing.