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Evidence-based e-Learning design. Learning science, technology, and digital strategy.
Evidence-based e-Learning design. Learning science, technology, and digital strategy.
DEPDC/GMS
(NGO, Thailand)
DEPDC/GMS
(NGO, Thailand)
e-Learning Prototype (DEPDC)
e-Learning Prototype (DEPDC)
For context and background on the learning need, please see the full case study: DEPDC Case Study
Click the image to view the Prototype in HTML.
Overview
Overview
This prototype represents an early build created to explore Lectora as an ePublishing, responsive course design, and WCAG accessibility features (alt text, CC, focus order, etc.). The focus was on learning the platform, structuring content with variables and condition logic, and organizing the Project Explorer with scalability in mind (rather than producing a fully developed course.)
Process
Process
Lectora (eLearning development)
NightCafe (AI-generated custom graphics that are still undergoing development for standardization)
Canva (graphic editing and layout refinement)
Google AI Studio (AI voiceover)
Manual SRT file creation (for custom closed captions)
Project Title: Scalable Single-Slide Logic Engine Tools Used: Articulate Storyline 360, Advanced Variables & Triggers.
Project Title: Scalable Single-Slide Logic Engine Tools Used: Articulate Storyline 360, Advanced Variables & Triggers.
The Challenge: Standard e-learning assessments often result in bloated project files that increases file size, load times, and makes future maintenance tedious, as updates must be applied manually to every individual slide.
The Solution: I engineered a Dynamic Single-Slide Logic Engine. By utilizing a "State Machine" architecture, I programmed a single interface to serve as an unlimited question bank.
Variable Logic: A master variable (QuestionCount) tracks learner progress.
State Listeners: The base layer dynamically updates content (text and graphics) based on the variable's current value.
Conditional Triggers: "Smart Buttons" evaluate user input against a logic matrix to determine correctness for the specific active question.
The Outcome:
Efficiency: Reduced slide count by 90% (1 slide doing the work of 10+).
Scalability: New questions can be added simply by updating the state list and trigger logic, without duplicating layouts.
Performance: Created a seamless user experience with zero slide-load latency between questions.
DPD Framework
(United States)
DPD Framework
(United States)
e-Learning MVP (DPD)
e-Learning MVP (DPD)
For context and background on the learning need, please see the full case study: DPD Framework Case Study
Click the image to view the MVP in HTML.
Overview
Overview
This prototype (TBC)
Process
Process
iSpring & MS PPTX (eLearning development)
ChatGPT (AI-generated custom graphics)
Canva (graphic editing and layout refinement)
Google AI Studio (AI voiceover)
Manual SRT file creation (for custom closed captions)
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Google Sites
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