Instructional Design Models

During the course USMx: LDT200x Instructional Design Models, I’ve submitted these weekly deliverable.

In this video
I introduced myself, gave a quick review of the three major learning theories (behavirosim, cognitivism, and constructivism), and ends with my personal philosophy on teaching and learning.

ADDIE is the most famous instructional model of them all. For good reasons. Here is a mindmap I created in week 2 of the course to summarise my own take on this model.

The submission requirement for this week is a Podcast, not a video presentation. That’s why there’s not much visual going on in this video. It was also a huge challenge for me to cover all the steps with example in the 3-minutes limit. I gave up on that and put the priority on information quality instead.

For this week, I submitted this doc as a requirement:

I really like this approach, I also created an illustration for it in my Daily Drawing Challenge.

Rapid Instructional Design is an umbrella term – as far as I can see. There is no single definition or process which are being agreed by experts and designers as the “standard”. Among various concepts, I really like Meier’s Four-Phase Learning cycle (Preparation, Creative Presentation, Practice, and Performance) which matches perfectly with my approach to offline training. In Online eLearning, Thiagi’s Four door concept (Library, Playground, Cafe and Evaluation Center) – it helped me to finally “get” the method on edx and some of the courses on Coursera.

That being said, the assignment for this week seems to focus only on Rapid eLearning with the use of rapid authoring tool. This is my submission:

For the final assignment of the course, I’d like to propose a 20-minutes eLearning module about how to not being tricked by misleading charts.

Here is the Signature Assignment document with details about Goals, Objectives, Tasks Analysis, and more:

And here is the video in which I introduce the course:

If you are interested, please register a spot here: . If more than 10 people show interest, I’ll develop the course for real, arriving on April 1, 2019.

What is Adaptive Learning 
Adaptive Learning is personalized learning at scale and focus on mastery before moving on. It provide the best-suited just-in-time content, assessment, feedback etc. to different students. Traditionally, educators knew that personalized teaching and supporting is the best way to ensure learning happen effectively, but it requires a dedicated connection from an educator to a few students and can not be delivered at large scale. The rise of technology with online delivering of content and personalization algorithm had open the door of possibility for Adaptive Learning to become a reality.

Potential Impact of Adaptive Learning 
Adaptive Learning can have a huge impact on both the learners and the teacher.

  • For learners, Adaptive Learning respect their prior knowledge and ensure they reach mastery of each concepts before moving on. It can both save time, boost the interest, and improve retention of knowledge.
  • For teachers, Adaptive Learning help them to reach a lot more students while still able to maximize learning outcomes and provide guidance to the ones who need it most. The learning progress of all learners can also be tracked and analyzed as a base for improving their teaching materials.

Outline of an Adaptive Learning Module for my Signature Assignment topic
 In my Signature Assignment topic about “Identify Misleading Charts”, I gave the general framework of (1) Identify the story in the Chart => (2) Double check the Visual Convention => (3) Verify the Chart Structure => (4) Verify the Chart Data. These are still very broad steps. In order to turn it into Adaptive Learning, each of the step needs to be broken down into granular Learning Objectives (LO) and then I also need to provide several probes to assess each of those LO plus the remediation in case learner failed to finish the assessment.

Here is an example of several micro LO related to Bar Chart Axis:

  • When given a bar chart with one primary Axis, the learner can identify the Axis, identify the minimum value, maximum value, and the scale.
  • The learner can verify if the scale of the axis is consistent
  • The learner can determine if the minimum and maximum value on the axis is appropriate in the context of the chart.

I also drew a note about how Adaptive Learning System determine the next unit of instruction or probe, here:

Also in the Instructional Design & Technology MicroMaster