Data Detective: Make Learning Engaging 

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Data Simulation

Data Detective: Transforming Static SME Guidance Into an Interactive Data Integrity Simulation

Articulate Storyline Scenario Design Microlearning SAM

Project Overview

Organizations often submit grant reports under tight deadlines, using data pulled from multiple sites with inconsistent formats. Small reporting errors—like outliers, missing values, or inconsistent categorization—can distort funding narratives and jeopardize credibility.

The Goal

Teach data integrity without overwhelming learners with spreadsheets or technical jargon?

The Problem

Staff feel overwelmed with the the technical jargon that comes with data reporting:

  • Staff lacked formal data-cleaning training
  • Reporting inconsistencies risked funding accuracy
  • Teams needed practical decision-making practice—not theory

The Solution

I created an engaging module/interactive simulation where the learner is able to engage with different data cleaning situations and learn more about the correct and incorrect ways to report data for common financial situations.

My Process

1. Analyze SME's Content

Extracted: The five core error types. The underlying decision logic. And the ethical implications of each choice. Shifted focus from “tips” to “why each decision was made to aggregate the data”

2. Determine Structure of Simulation and Create Storyboard

This ensured each concept moved from explanation → application.

3. Vary Interaction Types

Instead of five multiple-choice questions, I intentionally varied cognitive demands:

1) Multiple choice for conceptual reasoning

2) Click-to-investigate for missing data

3) Text entry for numeric cleaning

4) Slider interaction to visualize bias

5) Visual average recalculation for outliers

This prevented pattern fatigue and reinforced skill transfer.

4. User Test & Refine w/SMEs Feedback

Collected qualitative feedback to improve clarity, tone, flow, and scenario relevance.

Tools Used

Articulate Storyline Google Gemini Google Suites for Collaboration Github

Results

High Engagement

Learners did more than read a table; they actively interacted with the content to internalize the logic behind specific data cleaning decisions.

Fast Skill Uptake

Clear, simple examples helped learners apply skills the same day.