Learning Theory · Emerging Technologies · UMGC LDTC 620
Social & Collaborative
Learning Spaces
An applied analysis of Bandura's Social Learning Theory across emerging technologies and online platforms — examining how GitHub, Slack, Discord, AI coding tools, LinkedIn Learning, and YouTube each activate the conditions adult learners need to observe, retain, and reproduce new skills.
The Assignment
Learning theory meets professional tools
The Unit 4 assignment for LDTC 620 — Next Generation Design: Emerging Technology, Gamification, and AI in Learning Design at UMGC — asked students to analyze how a foundational learning theory applies to emerging technologies and social platforms used in online collaborative learning environments.
I chose Bandura's Social Learning Theory as the analytical framework, then evaluated five platforms and tools from my own professional practice: GitHub, Slack and Discord, AI coding tools (GitHub Copilot and Claude), LinkedIn Learning, and YouTube. The analysis drew directly on experience teaching web development at the University of Washington, Chegg Skills, Udacity, OpenClassrooms, Springboard, and Upright Education.
The goal was not a theoretical overview — it was a practical design brief connecting learning science to the specific tools adult learners and instructors actually use.
LDTC
620
UMGC
Unit 4 Assignment
Social & Collaborative Learning Spaces
Next Generation Design: Emerging Technology,
Gamification, and AI in Learning Design
The Framework
Bandura's four mediational processes
Social Learning Theory holds that people learn by observing others — but only when four conditions are met. These processes map directly onto how adults learn new tools and technologies.
Attention
The learner must notice the behavior and find it worth imitating. In technical training, this means demonstrations need to be visible, relevant, and performed by a credible model — a peer, an instructor, or an expert the learner aspires to become.
Retention
Observed behavior must be stored in memory to be recalled later. Searchable Slack threads, recorded screencasts, and GitHub commit histories all serve as external retention systems — extending what a single learning session can hold.
Reproduction
The learner must be capable of replicating what they observed. Peer code review, hands-on exercises, and pull request workflows create the low-stakes reproduction opportunities that bridge observation and independent performance.
Motivation
A perceived positive outcome drives the learner to imitate the behavior. Using industry-standard tools from day one — tools that appear on job listings and employer profiles — gives learners a concrete reason to engage that extends beyond the course.
The Tools
Five platforms analyzed through an SLT lens
Each tool evaluated for which observational models it activates, where it supports or limits the four mediational processes, and how it connects to real instructional practice.
GitHub
Activates all three observational models simultaneously — commit histories as symbolic models, README documentation as verbal instruction, and pull request workflows as motor reproduction. Industry-authentic and motivating by design.
Slack & Discord
Primary verbal instructional modeling platforms. Threaded Q&A, searchable message history, and real-time peer channels create persistent knowledge structures that support retention across a cohort. Used across Springboard and Upright Education.
AI Coding Tools
GitHub Copilot and Claude function as always-available symbolic models — learners observe AI-generated code, evaluate it critically, and reproduce or refine it. The instructional design challenge: building critical evaluation alongside AI fluency.
LinkedIn Learning
A large-scale symbolic modeling environment where professional credentialing activates motivation directly. Visible badges and peer endorsements create social proof that learning has career value — driving imitation through perceived consequence.
YouTube
The most accessible symbolic modeling platform in existence — and free. For technical learners, live-coding streams and code review walkthroughs deliver expert-level observational learning at scale. Comment sections add a peer verbal modeling layer.
Best Practices
Designing for social learning in technical environments
The most effective online collaborative learning environments don't rely on a single platform — they layer tools so that each one handles a distinct SLT function. A learner might observe a skill on YouTube (symbolic model), ask a clarifying question in a Discord channel (verbal instruction), then reproduce it in a GitHub repository (motor reproduction) — all in one learning session.
This layered approach is what I've applied across every technical teaching role. At Upright Education, Discord and GitHub worked as a system: learners observed solutions in Discord and immediately reproduced them in their repositories. That combination covered all four mediational processes in a single session.
The same principle applies directly to AI tool adoption in the workplace. Employees learning to use AI in their daily workflows need the same conditions: a credible model to observe, a low-stakes environment to reproduce, and a visible reason to try.
Model Explicitly
Use Zoom screensharing, Loom recordings, or GitHub commits to demonstrate skills in real time before asking learners to reproduce them.
Layer Your Tools
Combine platforms intentionally — GitHub for reproduction, Slack/Discord for verbal modeling, YouTube for symbolic modeling. Each tool serves a different SLT function.
Build in Peer Observation
Design peer code review, discussion boards, and collaborative projects so learners observe and learn from each other, not just from the instructor.
Motivate with Authenticity
Use industry-standard tools from day one. When learners know their GitHub profile is visible to employers, motivation increases — because the consequence is real.
Presentation Recording
Watch the Presentation
Full recorded presentation — "Social & Collaborative Learning Spaces: Applying Bandura's Social Learning Theory to Emerging Technologies" — UMGC LDTC 620, Unit 4.
Reflection
The same theory that explains how developers learn explains how employees learn AI
What this analysis reinforced for me is how consistently Bandura's framework holds across different technical learning contexts — from a beginner learning CSS positioning in a UW bootcamp classroom to an employee learning to write their first AI prompt in a corporate onboarding program. The conditions for observational learning don't change based on the tool being learned. They change based on how well the learning environment is designed to meet them.
AI adoption training is the current version of a familiar challenge: helping adults build fluency with a new technical tool that is evolving faster than formal curriculum can keep up with. The answer is the same as it's always been — credible models, low-stakes reproduction opportunities, peer learning structures, and authentic motivation. The platforms are different. The theory is the same.
UMGC LDTC 620 — Next Generation Design: Emerging Technology, Gamification, and AI in Learning Design, Unit 4, 2026.
2026
UMGC
LDTC 620
Next Generation Design
Emerging Technology, Gamification & AI
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Interested in working together?
I'm open to remote opportunities in instructional design and technical learning experience design.