Instructional Design Strategy Document

Course Overview

Course Title: Introduction to Data Analytics
Target Audience: New hires in the IT and business analytics departments, current employees seeking to upskill
Duration: 8 weeks
Mode of Delivery: Blended learning (online modules + live webinars)
Project Lead: Laena McCarthy
Date: February 15, 2024

1. Learning Objectives

1.1 Overall Goal

Equip learners with fundamental skills and knowledge in data analytics, enabling them to perform data-driven decision-making in their roles.

1.2 Specific Objectives

By the end of this course, learners will be able to:

  1. Understand core concepts of data analytics.

  2. Perform data collection and cleaning tasks.

  3. Conduct exploratory data analysis.

  4. Create effective data visualizations.

  5. Apply basic statistical analysis techniques.

  6. Use data analytics tools (Excel, Python, R) for analysis.

2. Instructional Approach

2.1 Teaching Methodologies

  • Blended Learning: Combines self-paced online modules with instructor-led live webinars.

  • Active Learning: Includes hands-on activities, projects, and case studies to reinforce learning.

  • Collaborative Learning: Utilizes discussion forums and group projects to foster peer interaction and collaboration.

  • Adaptive Learning: Adjusts the pace and content based on learner performance and feedback.

2.2 Content Delivery

  • Online Modules: Interactive lessons including videos, readings, and quizzes.

  • Live Webinars: Weekly sessions for in-depth discussions, Q&A, and real-time feedback.

  • Hands-on Projects: Practical assignments to apply learned concepts.

  • Discussion Forums: Platform for learners to share insights, ask questions, and collaborate.

2.3 Instructional Media and Tools

  • Learning Management System (LMS): Canvas

  • Video Creation: Camtasia, Adobe Premiere Pro

  • Interactive Tools: H5P, Quizlet

  • Data Analytics Tools: Excel, Python (Jupyter Notebooks), R (RStudio)

  • Collaboration Tools: Slack, Microsoft Teams

3. Assessment Strategy

3.1 Formative Assessments

  • Quizzes: Short quizzes at the end of each module to check understanding.

  • Discussions: Participation in forums and live webinars.

  • Assignments: Weekly tasks to apply concepts learned.

3.2 Summative Assessments

  • Mid-Term Project: Analyze a given dataset and present findings.

  • Final Project: Comprehensive project involving data collection, cleaning, analysis, and visualization.

  • Final Exam: Multiple-choice and short-answer questions covering the entire course content.

3.3 Feedback Mechanisms

  • Automated Feedback: Instant feedback on quizzes and assignments.

  • Instructor Feedback: Detailed feedback on projects and participation.

  • Peer Feedback: Collaborative review sessions for group projects.

4. Engagement and Motivation

4.1 Gamification

  • Points and Badges: Earn points and badges for completing modules, participating in discussions, and achieving high scores on assessments.

  • Leaderboards: Display top performers to encourage friendly competition.

4.2 Interactive Elements

  • Interactive Videos: Videos with embedded questions and clickable elements.

  • Simulations: Real-world scenarios and data simulations to practice skills.

4.3 Community Building

  • Ice-Breaker Activities: Initial activities to build rapport among learners.

  • Group Projects: Collaborative projects to build teamwork skills.

  • Social Media Integration: Use of private social media groups to foster informal interactions.

5. Accessibility and Inclusivity

5.1 Accessibility Standards

  • WCAG 2.2 AA Compliance: Ensure all content meets accessibility standards.

  • Closed Captions: Provide captions for all video content.

  • Alternative Text: Include alt text for all images and graphics.

5.2 Inclusivity

  • Diverse Examples: Use examples and case studies from a variety of industries and demographics.

  • Flexible Learning Paths: Allow learners to choose their learning paths based on their interests and needs.

6. Evaluation and Improvement

6.1 Course Evaluation

  • Learner Feedback: Collect feedback through surveys and focus groups.

  • Performance Metrics: Analyze data on course completion rates, assessment scores, and learner engagement.

6.2 Continuous Improvement

  • Iterative Updates: Regularly update content based on feedback and new developments in the field.

  • Instructor Training: Provide ongoing training for instructors to ensure effective course delivery.

  • Technology Updates: Integrate new tools and technologies to enhance the learning experience.

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[This sample is adapted with permission from a client project,]

This instructional design strategy document outlines the comprehensive approach to designing and delivering the "Introduction to Data Analytics" course, ensuring that it meets the learning needs of the target audience and achieves the desired educational outcomes.