LS-DX on-demand sessions are recorded videos you can access starting at this date and time, and watch at your own convenience.

Learning content teams often lack a solid rationale for forecasting development effort for varying complexity. There may be pressure to lower estimates over fear of sticker shock, leading to a danger of wishful thinking and modifying estimates and then having cost often equalling  the original estimate: leading to overrun. Stakeholders may be concerned that  estimates are not anchored in reality: just truisms and internal lore. What is needed is a cost model that can be trusted by stakeholders and that will minimize risk by increasing accuracy of forecast, based on real-world data.

In this session you will  learn how to develop effort estimates for online learning of varying degrees of interactivity. You will about the Interactive Multimedia Instruction (IMI) framework, what contributes to higher interactivity in online learning, and how varying levels of interactivity affect total team development effort. Industry benchmarks will be presented which will give you a data-based anchor to develop and defend effort and schedule estimates. You will further learn about estimating student seat time and total run time to get an initial rough estimate. You will learn how to refine initial estimates by looking at a more granular breakdown of development effort components to determine what is relevant, what is out of scope, and what the sensitivity of the project is to changes in tools, media requirements or staffing.‬‬

In this session, you will learn:

• Why a data-based approach to estimation can protect your budget, your schedule, and your team
• What the IMI Framework is, and what criteria determine the interactivity of training
• Industry benchmark studies for varying complexity of online learning
• How to account for all effort elements in your development process
• How to customize this for your team and gain immediate benfit

Technology discussed:

Page turner CBT (IMI 1), emulated equipment with guided tasks (IMI 2), simulated free-play environment (IMI 3)