Online Course Redesign
Python for Data Analysis
Python is a popular programming language that powers many applications we use every day. But it's also widely used for data analysis. The learning journey for people who want to get into data science often starts with Python.
About the Project
TaskRedesign the Python fundamentals modules of the online Python for Data Analysis course
TeamCollaboration with three subject-matter experts and the Data Science program staff
Target Audience25 - 45 year olds who want to use Python programming in data analysis
My RoleInstructional designer and course producer
ClientSkillFactory, an IT-focused continuing education school
ID ApproachBackward Design model with elements of ADDIE and Four Component Instructional Design (4C/ID)
Why redesign?
Even though Python for Data Analysis was a very popular online course, I identified several issues during the course audit.
- Irrelevant Course ContentThe old course content had been developed for a different purpose and didn't fully meet the needs of learners.
- No Recognition of Prior LearningLearners who already knew Python fundamentals couldn't opt out of introductory modules.
- One Difficulty LevelOne-size-fits-all course content didn't suit learners with different knowledge and ability levels.
- Inadequate PracticeThere wasn't enough practice; assignments were disconnected from real-world data analysis.
Analysis and Design
I identified key skills learners needed to develop in this course and organized these skills by module. I reviewed the course curriculum in the context of the Data Science certificate program needs, student feedback, and competitive offerings.
To check the required understanding and skill proficiency, my team and I planned prior learning recognition as well as formative and summative assessments for every module.
We chose learning experiences based on the learning outcomes, learner needs, and available resources. Our plan included self-paced instruction, multi-media content, practice with automated verification, mentoring through webinars, and social learning in Slack.
I found three subject-matter experts who created the content and assessments for five course modules. I also hired a new webinar instructor who supported learners through weekly Zoom sessions.
Development and Implementation
Assessments
Here's how we addressed the need for prior learning recognition, learners' varying skill levels, and their request for more practice.
- 15questions in prior knowledge assessment tests at the beginning of every module.
- 450formative assignments of three difficulty levels in five modules.
- 47summative assignments in five real-life projects at the end of every module.
Instructional Content
We expanded and reworked the course content to support knowledge acquisition and skill development in accordance with the learning outcomes.
Step-by-step Tutorials
We created explanations of Python fundamentals that were easy for beginners to follow.
Video Lessons
We recorded 15 screencasts to explain more difficult topics in the five modules.
Additional Resources
We shared links to external resources, so learners could dig deeper into difficult topics.
Result Evaluation
While I don't have the data to evaluate the new course effectiveness, I've received the following feedback:
I don't think we've had such a thoughtful approach to the design of any of our courses.
The modules are well structured and the assignments are interesting.
Compared to the new modules, the old ones seem confusing, without the necessary explanations or practice.
I liked the number of assignments and their variety; they helped me practice what I've learned.
I don't think we've had such a thoughtful approach to the design of any of our courses.
The modules are well structured and the assignments are interesting.
Compared to the new modules, the old ones seem confusing, without the necessary explanations or practice.
I liked the number of assignments and their variety; they helped me practice what I've learned.
Project Reflection
Transitioning from a one-size-fits-all approach to a course that recognized prior learning and offered practice at three difficulty levels was ambitious. Developing the 450 assignments took a lot of time.
Looking back, we should have started with a "standard" course at one difficulty level, collected feedback from learners and tutors, and then added the other levels. It was a valuable lesson learned.
Looking back, we should have started with a "standard" course at one difficulty level, collected feedback from learners and tutors, and then added the other levels. It was a valuable lesson learned.
Project Reflection
Transitioning from a one-size-fits-all approach to a course that recognized prior learning and offered practice at three difficulty levels was ambitious. Developing the 450 assignments took a lot of time.
Looking back, we should have started with a "standard" course at one difficulty level, collected feedback from learners and tutors, and then added the other levels. It was a valuable lesson learned.
Looking back, we should have started with a "standard" course at one difficulty level, collected feedback from learners and tutors, and then added the other levels. It was a valuable lesson learned.
What's Next?
I invite you to explore other projects I've worked on or contact me to discuss your e-learning idea.
Photos: Christina wocintechchat.com, Christopher Gower, Gordon Cowie, Kelly Sikkema, Theo Sunardjaya