Experience API (Part 5)

Tin Can API/Experience API Concept

So, issues of nomenclature not withstanding, what were some of the key elements Rustici introduced with its Tin Can API? (For a copy of a slightly modified version of the Rustici deliverable to ADL [Tin Can API], cut and paste this link to your web browser: https://www.adlnet.gov/public/uploads/Experience-API-Release-v0.95.pdf )

Rustici addressed the reality and the administrative needs that exist in our increasing complex, disaggregated, and de-centralized technological world. As noted, yes, there are so many different types of technologies, and, yes, there are so many different types of platforms, and, yes, there are so many different sources of information. Moreover, not all of these learning experiences take place online. So how to capture the range of these “experiences” for modern learner . . .

The key concept and innovation for Tin Can API and Experience API is as follows.

Whenever a learning moment has to be recorded, documentation of this experience is sent to a Learning Record Store (LRS) using the following format:

The basic notion is “I did this.” This format permits administrators to track when learners begin educational courses/modules, review a given page, answer a question, and/or finish (or fail) a given course of study. While the information might have originated within a proprietary Learning Management System (LMS), the data ultimately is routed to an independent LRS, which then, in theory, could be accessed by other parties and software applications.

Since a LRS is the ultimate destination for learning data, individuals can learn off-line and simply upload their learning data once given an Internet connection. Now this does not mean that a learner could be reading a hardback book and an article on a PDF reader and magically that information is transmitted to the LRS. Rather, the learning still needs to take place through a digital format that tracks steps taken by the learner. (The reading of a hardback book, in fact, could be added to the LRS, but this would simply need to be documented and inputted by an administrator.)

Let’s look at some examples:

  1. Peter began the intermediate course for sommeliers
  2. Peter read module 1
  3. Peter scored 50% on module 1 questions
    1. Peter scored 100% on module 1 questions about white wine
    2. Peter scored 0% on module 1 questions about red wine
  4. Peter read a refresher on red wine for module 1
  5. Peter scored 95% on module 1 questions

     .  .  .  .  .  .

     27. Peter achieved competency in Burgundy style wines

This information could have originated from a cell phone, a tablet app, a desktop computer at home, or a school-based workstation. Imagine Peter began the class as an outside student at the US Department of Agriculture, and then received a job working at the U.S. Food and Drug Administration. While at FDA, he continued his study as a sommelier though using a different LMS. His old learning records are still accessible even though the FDA is using a new LMS, since the records are stored in a LRS that is universally accessible using protocols developed by Tin Can API/Experience API.

Another important feature of Experience API is that it can record learning data derived from simulations and virtual reality environments. This data, of course, is qualitatively different from other data given its dynamic nature. In this regard, too, Experience API can record data from “groups,” as distinct from individuals, that participate in a learning process. For example, ADL highlights one related element in its portfolio: Hyper-Personalized Intelligent Tutor (HPIT), which “is able to detect non-cognitive factors (e.g., determination, boredom, motivation) in a learner . . .”

(https://www.adlnet.gov/hpit).

Similarly, SAVE (Semantically Automated Assessment in Virtual Environments) “provides a framework for learning procedural skills (e.g., repairing a car, flying an airplane, or shooting/maintaining a weapon system) through simulation.

(https://www.adlnet.gov/save)

Apart from the sleek, sexy uses of xAPI [note the devolution into an abbreviation], there are basic, fundamental uses of value regardless of whether or not or an organization employs novel gaming training or the like. Welcome to the ADL/DOE Learning Registry (LR) Project. (https://www.adlnet.gov/learning-registry) There is a huge need for a tool like this—especially within the government or other large and multi-faceted organizations. Imagine an organization having a simple need, say, developing an emergency building evacuation training. Divisions on the east coast may have completely different missions and operations from divisions on the west coast, however the character of their building evacuation plans will likely be fairly similar discounting local elements. A training that one division develops can then be used and, perhaps, improved upon by another division. Maintaining a central LR valuable to leverage corporate expertise and intellect and minimize waste in expenses and time. In fact, many corporations have developed positions specifically for this function: Chief Knowledge Curators.

(http://www.clomedia.com/2017/05/22/organizations-need-chief-knowledge-curators/)

Credentialing increasingly is becoming an important element that is facilitated through xAPI, especially in government service. Witness the birth of MIL-CRED (Military Micro-Credentials), which is designed to create “a fully vetted, fully automated, personally controlled digital resume.” This project was developed to ease the transition from military to “civilian careers and educational opportunites.”

(https://www.adlnet.gov/mil-cred)

Administrators using xAPI can generate meta-data drawn across different groups of students over periods of time. This can be valuable in terms of fine-tuning elements of educational content and course focus. Ultimately, xAPI was built to document a relationship between training and job performance, which for administrators, managers, and supervisors is a key if not the key element in any program of workplace development.

Next Step: Actually, next and final step, is to look at the future of Experience API (xAPI) and the current collaborations and research initiatives of the ADL.

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