Google Course Builder and the Tin Can API

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Google’s Course Builder is a great resource for the MOOC community. Open source, simple, and easily modified, a programming novice can go from download to a working MOOC ready for thousands of people in a short period of time. Rapidly authored and easily deployed MOOCs are fantastic, but create a new problem: data integration. Many people will participate in multiple MOOCs over time, and bringing that data together will deliver a lot of value for individuals and organizations.

So, I’ve added simple support for the Tin Can API (also known as the Experience API) to Google Course Builder. The Tin Can API is a modern standard for communicating learning data, intended for recording learning anywhere and everywhere it occurs. In a typical Tin Can ecosystem, learning applications and related systems, such as a Google Course Builder MOOC, produce ‘statements’ describing the learning ‘activities’ that ‘agents’ are engaging in, such as completing assessments, answering questions, and completing the course as a whole (I added support for all three of those). Those statements are then sent to a ‘Learning Record Store’ (LRS), where they can be sliced, diced, and generally considered.

Getting Started With Tin Can in 5 Steps

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We had a great time at DevLearn 2012, and although most of what happened in Vegas should stay in Vegas, I’d like to share some. As always, we met some great learning professionals doing some amazing real world work. I really appreciated attendee openness and willingness to embrace new technologies, especially one that flips our world upside down - the Tin Can API.

“There is no better time than right now to take the plunge into Tin Can, because it can solve real problems”

LMS or LRS?

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I love the Tin Can community. There is a wealth of information and support everywhere ranging from application and tool developers to various L&D professionals, and the ADL & Rustici (who have realized this new learning standard). We are all traveling down our own learning paths about the Tin Can API, and gaining our own excitement for how it will help us do amazing things that we could never do before. As awesome and simple as this sounds, learning professionals with practical reasons for implementing and unleashing the power of Tin Can will have to make some very important decisions.

“it’s not about the LRS or the LMS…this is not a technology competition”

What Can the Tin Can API Do for You?

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Saltbox is excited to offer the first webinar in our new micro-series about the Tin Can API. If you are interested or just learning about the Tin Can API and want to see it in action, this is for you.

We will cover topics from ranging from a basic introduction to seeing real analysis and visualizations of learning data in an LRS. There will be demos and plenty of time for Q&A, and the webinars will last about a half hour.

The first webinar is November 8th, 2012 at 10:00am Pacific Time.

Register Here!

We hope you can join us!

Technology-Enhanced Learning Architecture

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John Delano finalized his 3-part guest blog series with Intrepid Learning. Part 3 outlines the technology architecture required to enable a successful Un-Training Non-Governance Support Model for Enterprise L&D. If you missed part 2, you can read it here.

Here’s an excerpt from Technology-Enhanced Learning Architecture:

“Combining data about learning and the learning penumbra makes analytics easier. Analytics will not take the place of human observation and analysis, but they will contribute to better decision-making and learning experience design.”

Read the full blog post here.

An Un-Training Non-Governance Support Model for Enterprise L&D

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John continues his discussion about the pros and cons of current training governance models. Part 2 introduces a new model to support informal learning. If you missed part 1, you can read it here.

Here’s an excerpt from An Un-Training Non-Governance Support Model for Enterprise L&D:

“Developing this new model will not be easy. Our demanding business environment and natural resistance to change requires us to think differently and confront some deep-rooted beliefs about forms of organizational hierarchy in order to support the workforce with agility.”

Read the full blog post here.

Training Governance Models - Part 1

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John Delano received an invitation to write a blog post for Intrepid Learning this week. It is a three part series exploring the challenges and benefits of centralized and distributed training governance models. Here is an excerpt from part one of The Aging State of Training Governance Models:

“No matter how you cut it, traditonal training governance models fall into two basic categories, with some slight hybrid variations in between: centralized and distributed organizational structure.”

Centralized Training Governance:

The centralized governance model focuses responsibility on a large group of people in human resources or learning and development (L&D). The group defines learning outcomes without much collaboration with the intended audience or business units. They create content, operate and administer in person or virtual training, and they make all the decisions (sometimes with a leadership council and/or CLO).”

Read the full blog post here.

Tin Can Patterns - Tagging People

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This is the first entry in a series on Tin Can patterns. Simple statements in Tin Can are easy, but the most power comes from rich structure. The Tin Can Patterns series will explore, suggest, and advocate options for expressing common use cases. I want it to be accessible, so the first time a Tin Can concept appears, I’ll gloss it, and try to describe everything in minimal technical detail.

Tin Can means streams of statements, mostly about activities. The core of a statement is “person verbed object”, with added structure to say “with result , in context, according to authority” and more. Tin Can works for traditional learning data, but it also works for social learning data, and there’s already a tool making an impact in that space, Tappestry.

“Complicated ideas sometimes require multiple statements, and the goals of a learning standard are not the same as the goals of a social network like Facebook”

Tin Can - One Possible Future

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Authoring tools will speak Tin Can. Data from the learning penumbra, such as content views, job performance, and social interactions will be turned into Tin Can statements. Data will flow into Learning Record Stores, but what will the dynamics of that ecosystem be?

Organization needs will be met modularly, without complicated configuration or data translation.