Are you leaning too hard on video for your employee training? In this episode, we dive deep into insights from techstructional to debunk the myth that every training need requires a video, a course, and a job aid. While video is a popular go-to medium—often because it is perceived as easier or cheaper—it is not always the most effective way for employees to learn new software.

We explore how to let your content dictate the training format rather than following trends. We’ll break down the specific types of software training videos that actually improve workplace technical skills and when you should ditch the camera in favor of a job aid or an interactive simulation.

In this episode, you’ll learn:

• The Four Most Effective Video Types: We examine Short Task-Based Demos for quick troubleshooting, Microlearning Clips for "moments of need", Scenario-Based Videos to sitatuate tasks in real-world work, and Animated Videos for explaining conceptual logic and data flow.
• The Pitfalls of Passive Learning: Why long recordings and foundational lectures are poor fits for corporate video training and how they can lead to lower engagement and retention.
• Performance Support vs. Deep Learning: Understanding that videos are best for showing short tasks to users who already have foundational skills, while structured courses and written artifacts are superior for deep learning or repeated reference.
• The Decision Framework: How to use a visual guide to determine if a task should be a video, an infographic, or a self-paced course based on its complexity and length.

Key Takeaway: The best software training is short, direct, and designed to help an employee perform one task correctly, right now. Stop forcing users to sit through dozens of steps in one continuous recording and start providing targeted resources that increase proficiency and reduce errors.

Resources Mentioned:

• The techstructional Guide to Effective Software Training Videos
• The "Choosing the Right Training Video Type" Flowchart

Note: This podcast was created using Google's NotebookLM. The voices and podcast are all AI-generated and synthesized based on the blog post linked above.