bridging the learning analytics gap with xapi · technology, people’s avenues of obtaining...

AbstractWith Experience Application Program Interface (xAPI), we are now able to connect the different types of learning people obtain through their daily activities. By leveraging Learning Record Stores (LRS) and corresponding reporting and analytics tools, we can now view, store, and create comprehensible reports of the data provided by each individual’s learning activity stream through xAPI. Moreover, xAPI enables us to visualize reports from different data sources across different platforms. Great ideas, sure. But, what does this really mean to the average learner and the average learning provider? And, how does this impact the organization’s financial “bottom line”?

Mison Riggins

Bridging the Learning Analytics Gap with xAPI An xAPI Primer for Learning Professionals

WHITE PAPER

November 2016

Bridging the Learning Analytics Gap with xAPI Mison Riggins

© 2016, Inspired eLearning, LLC. All Rights Reserved.

I would like to thank the IeL Product and Engineering team for going over the technical aspects of this paper with a fine tooth comb. Lee, James, Dan, Yogesh, and Emre, I appreciate your insight and frank commentary. You have helped shape this overview on xAPI.

I would also like to thank John, Matt, and the Production team for their efforts in making this paper presentable.

Thank you.

Acknowledgements


© 2016, Inspired eLearning, LLC. All Rights Reserved.

Table of Contents

Introduction ................................................................................................. 1

Origins of xAPI ............................................................................................. 3

Limitations of SCORM/AICC .................................................................... 4

Breaking the SCORM Barrier .................................................................. 5

The Role of LRS ........................................................................................... 7

The Role of Analytics ............................................................................... 8

Building and Licensing Reports ........................................................... 10

Thought Leadership: Where Learning and xAPI ............................ 12

Appendix A – Key Terms in the Realm of xAPI ............................... 14

Resources ................................................................................................... 16

Works Cited ............................................................................................... 17


1 © 2016, Inspired eLearning, LLC. All Rights Reserved.

We are in the business of learning. Our primary goal is to provide effective learning solutions that meet our clients’ training objectives. Our success in meeting and surpassing our bottom line gives us the capital and resources to reinvest in R&D to further develop and improve our course offerings and eLearning platforms, directly affecting our return on investment (ROI). By capturing the various methods of how people obtain working knowledge and being able to record and analyze it, we as learning providers will shape the learning experience.

With more aspects of our daily lives being integrated with technology, people’s avenues of obtaining knowledge and recording that data have become key to the eLearning industry today. Through the SCORM/AICC compliant online course offerings of our current learner centers, we may no longer be confined to the “brick-and-mortar” classroom. Nonetheless, we are still enmeshed under the confines of siloed, structured lessons within the above mentioned systems of formal learning. Here, we rather propose to embrace all the ways of learning a person can experience. With Experience Application Program Interface (xAPI), we are now able to capture activity streams related to learning experiences: who learned what, when, and how. Perhaps, we can even throw in the why. This ability enables learners to keep track of their own progress as well as provide learning providers with the tools to shape learning more effectively.

Introduction

Treat people as if they were what they ought to be and you help them become what they are capable of becoming.

- Goethe



By leveraging a large data store (Learning Record Store) and corresponding reporting and analytics tools, we will be able to store, view, and create comprehensible reports of the data provided by each individual’s learning activity stream through xAPI. Also, xAPI enables us to visualize reports from different data sources across different platforms. Great ideas, sure. But, what does this really mean to the average learner and the learning provider? Moreover, how does this impact the organization’s financial “bottom line”?

Figure 1 Graphics and design by Rustici Software, www.tincanapi.com




Origins of xAPI

Before we address the above questions, let’s delve deeper into what xAPI actually is. During its development stages, the project was called “Project Tin Can”. As Rustici Software, the developers of the xAPI specification, concentrated on the communicable aspects of the innovative product, and the name Tin Can API was born (Chris Tompkins, personal interview, October 12, 2016). ADL, the company that commissioned and owns this specification, changed the name to Experience API to emphasize the ability to harvest learners’ experiences. Foreman (2013) explains simply, “[The name was changed] because the Experience API enables us to design learning programs that incorporate, not just formal lessons, quizzes, and tests, but all sorts of experiences where learning may occur.”

Basically, xAPI is the language used to track activity streams, things people do. This idea of activity streams emerged from social networking sites such as Facebook, Twitter, and Google Plus (Foreman 2013). Just as these social media sites track users’ activities, including who they chat with, what they viewed or liked, or what they posted, xAPI tracks learners’ activities.

To give you an idea of what this implies, ADL (2013) summarizes it below:

xAPI applies human (and machine) readable “activity streams” to tracking data and provides sub-APIs to access and store information about state and content. This enables nearly dynamic tracking of activities from any platform or software system—from traditional Learning Management Systems (LMSs) to mobile devices, simulations, wearables, physical beacons, and more. The possibilities make for an endless combination of connections only limited to our own creativity and designs.



Limitations of SCORM/AICC

When SCORM/AICC compliance requirements were established, the brick classroom expanded into the virtual classroom. Formalized online course offerings enabled learners to access their materials and courseware from anywhere. Nonetheless, SCORM/AICC courses have remained limited in their ability to encompass all the varied potential learning that occurs for each individual. A widely accepted Learning Concept model1 proposes that formal, structured learning only amounts to 10% of what today’s average employee learns in the workplace.

In fact, structured learning, as well as on-the-job training, troubleshooting, mentoring, social media, and self-motivated research account for just some of the many ways people experience learning in today’s workplace.

Learning Management Systems (LMSs) that rely solely on SCORM/AICC compliant content limit the types of activities that can be tracked and applied, because they cannot communicate with each other nor with other devices, data systems, or media centers. For example, a company may pay x amount for security awareness training for their employees on a hosted LMS. In addition to the online security course, the company also encourages all personnel to take part in an offline peer-to-peer session, brainstorming ways to change their cyber security culture. In this scenario, the LMS is already recording SCORM/AICC data and will issue a certificate at the end of the course. However, the LMS will have no record of the offline activity and its participants. It has no way to communicate with company logs or training data records. In short, there is no common language between these systems. Thus, as organizations invest heavily in formal standardized training, the returns on investment calculations may be skewed as other forms of learning, like mentorships or peer-to-peer training, are not being accounted for in those calculations.

1 “The 70:20:10 Model for Learning and Development is a commonly used formula within the training profession to describe the optimal sources of learning by successful managers. It holds that individuals obtain 70 percent of their knowledge from job-related experiences, 20 percent from interactions with others, and 10 percent from formal educational events” (Training Industry, 2016). This concept was developed by Morgan McCall, Robert W. Eichinger, and Michael M. Lombardo at the Center for Creative Leadership and is specifically mentioned in The Career Architect Development Planner 3rd edition by Michael M. Lombardo and Robert W. Eichinger.



Breaking the SCORM Barrier

Though SCORM/AICC courses will still carry weight in the formal education industry, xAPI allows us to go beyond the confines of SCORM limitations. The beauty of xAPI is that it enables the necessary interoperability essential in today’s world of connectedness. Simply put, it is a language all systems can share. What learners do on their tablets carry over to their mobile devices and their laptops. When learners consume an educational presentation on their tablets, xAPI statements can take the what, how, when, who of that activity and add it to the learners’ records, or eProfiles. When learners participate in impromptu stand-up meetings and learn a new aspect of their jobs from their mentors or peers, they can click into their mobile eProfiles and note that experience. When learners conduct online research about a topic of interest to broaden their scope of knowledge, they can include it in their eProfiles with a single click. In short, xAPI takes learners out of the SCORM/AICC box and enables them to experience and record their learning in whatever avenue they pursue.

Learning providers are now able to track, analyze, and modify their training and management approaches based on a holistic view of the set of data surrounding learners. Moreover, they will be able to take that data and calculate internal training/education ROI based on performance results.

For example, Jane Doe from Company Y records informal training from peers into her eProfile; then she takes a pretest for a mandatory training module on a related subject. Her scores allow her to forgo the course entirely and move on to the next level of training. Company Y has just earned x amount of dollars on time saved, cost of an unused course seat, and has also boosted employee morale—a priceless achievement. With properly written and coded xAPI statements, we can now view the path learners are taking and calculate ROI accordingly.



In addition, we can now integrate data from different sources and compile them into one LRS. Let’s take CBT2 data vs Simulation data. CBT data is obtained from an LMS when a learner views a SCORM/AICC course. Whereas, simulation data is obtained from a simulation application, like PhishProof3, when a learner participates by either reporting the phishing attempt or clicking on a link within the simulated phishing email. Both the iLMS and PhishProof record the SCORM and simulation data, respectively, on the learner’s aptitude for the activity.

However, what’s missing is any context between the two data collecting systems. xAPI, on the other hand, can bridge that gap and allow a seamless integration of data collated in one LRS containing CBT data points along with simulation data in a single report. As xAPI bridges the gap between various platforms allowing them to communicate with others, learning providers can now pull data and formulate reports with both data points combined in order to give:

a more complete picture of how their employees are learning, the success of their current learning/training programs, and what the next steps are in their learning trajectory.

Namely, “…xAPI liberates learning design. More precisely, it liberates us as learning designers and developers to stretch our design skills and creativity well beyond the constraints imposed on us by earlier specifications (such as SCORM)” (Peter Berking 2014, p3). Thus, we can escape the confines of an LMS Learner Center through xAPI by incorporating an LRS—a hub of activity streams—and well-developed learning analytics and reporting tools.

Not only will we be able to connect devices, platforms, and different data sources that learners access in their day to day lives, but also gain insight into the correlation between learning and job performance.

2 The Computer Based Training (CBT) data is the SCORM data that is collected after a course has been opened, viewed, and completed.3 PhishProof is Inspired eLearning’s solution for promoting awareness among end users against phishing attacks through simulation activities.



xAPI has no value without a Learning Record Store (LRS) and an analytics engine. Think of the LRS as a database of xAPI action statements. For the tech savvy, xAPI uses json4 statements to communicate, record, and track these action statements. Simply put, xAPI action statements translate learners’ activities in the most basic human terms; the LRS collects them; and the analytics tool works with the reporting platform to display them in a user-friendly interface (Lee Martin, personal interview, June 14, 2016).

With backend layering, the myriad pieces of data tracking actions are housed in one local storage area in order to consolidate all the different learning experiences a person can accumulate. Once the data is imported, the next step is to be able to access this data in some form of user interface. Foreman (2013) sums it up nicely: “A Learning Record Store (LRS) collects people’s learning experiences from many different sources and ties them together in one data mining space.” To enable this “data mining space” to be effective, well-thought out, and planned, analytics is a key element in driving the data traffic.

4 “An acronym for JavaScript Object Notation. “JSON is by far the most common API format, thanks to its simplicity to generate and parse” (Jeff Carr, 2016, p.3).

The Role of LRS

Figure 2 xAPI Concept Model created by James Burt. Example xAPI statement provided by Rustici Software, www.tincanapi.com

What is xAPI? – An leL Course Example

User interaction creates an "xAPI Statement" designed by us that meets our needs.

Application such as the iLMS retrieve data stored in the LRS to update a user's transcript.

Application such as the Reporting Platform could use the fact that Joe Learner launched this video to take other actions like enabling other course

content or notifying a manager.

S-141: Security Awareness Page 1, Module 2

iLMS Learner Center Transcript

Reporting Platform

xAPI Statements get stored in an LRS

"actor":{ "name": "Joe Learner",

"mbox": "mailto:[email protected]" },

"verb": { "id": " http://adlnet.gov/expapi/verbs/launched",

"display": { "en-US": "launched" } },

"object": { "id":

"http://inspiredlms.com/activities/passwordVideo", "definition": {

"name": { "en-US": "Password Video" }



So, now that we have xAPI action statements and an LRS data mining space, what do we do next? The reason for gathering data is to analyze it, predict trends, and facilitate conclusions. Thus, analytics play a critical role in successfully incorporating xAPI to capture learners’ experiences.

In short, analytics becomes the product that joins unrelated data to provide business intelligence and ROI realization (Martin, personal interview, June 14, 2016).

In a presentation, Kirsty Kitto (2016) shares her insight as well as some levity on how to approach the building of an analytics program from her experiences with SoLAR’s Content Learning Analytics (CLA) tool kit. Her lessons learned are as follows:

• Context is not optional; the more details, the better for learning analytics—it gets complicated!

• Recipes are essential for keeping context under control and reusable.

• Same thing with timestamps (time series common in LA). (Slide 18)

Kitto is currently working on CL Recipes for the CLA tool kit to mimic social media analytics. By having a set of rules to govern how we format our statements, the action verbs we use, etc., we can ensure a level of cross-platform continuity when mining the data in the LRS and analyzing it in an analytics engine.

On the other side of the analytics spectrum, [SaLTBOX] has introduced the concept of Experience Paths Analysis with their Wax LRS. Their goal is to provide analysis and visualizations for workplace learning in order to reduce the technological barriers that entrap learning. Ali Shahrazad (2015) describes the impact of this new approach as having the potential to be far reaching and fruitful. He states, “Companies can improve employee learning experiences where there’s sufficient confusion or offer supplemental training and resources to people who are taking a certain path.” Predicted Experience Paths, through time, can also allow for forecasting what paths learners may choose for themselves.

The Role of Analytics



On the flip side, the Wax LRS also accounts for “people taking exemplary or unexpected paths” as well through Experience Paths Analysis. Such individuals can be “identified and recognized or better supported.” Moreover, this type of analysis enables learning providers to “answer specific questions about people’s learning experiences in much greater detail and with higher certainty.” (Shahrazad, 2015). Once armed with these answers, learning providers can then better project internal training budgets as well as gear employees up by providing a plethora of successful learning avenues.

How does this work? The unique factor of Experience Paths is that the Wax LRS searches out patterns among the collected xAPI statements from the actual clicks, or path, the learner takes while going through the course content. Shahrazad (2015) explains, “As participants engage in learning experiences, Wax LRS captures their activity and illustrates the natural flow through content. It detects backtracking, optional content that is rarely used, the access of embedded resources, and unexpected learning barriers.” With these kinds of analytics, xAPI and the LRS can provide a more complete picture of the learner, of what is working, what is not, and what content areas may need improvement.

“In short, it empowers you to design more effective learning experiences” (Shahrazad, 2015) which if followed to success will help build a strong ROI surrounding investments in employee training. Now that we have interoperability, communication between platforms, a data mining warehouse, and an analytics engine, what kind of outputs are we to expect?

“In short, it empowers you to design more effective learning experiences.” (Shahrazad, 2015)



Critically, what kind of analytics will provide the kind of insight needed to further equip learners in their endeavors to be more productive in their work flow? On the flip side, how will learning providers account for ROI through such an overhaul of their systems?

Before we can address the two questions above, we first need to delve a little deeper into what kinds of analytics best suit both parties. Jeff Carr (2016) points out that unleashed xAPI is no more than a wild jungle of action statements. Although xAPI allows us to depart from the rigid SQL5 statements of CBT data that lack the flexibility for data to communicate across platforms, we need to lay some tracks or ground rules for these action statements.

Thus, when aggregating such action statements (i.e. the data) into useful analytic reports, some semblance of unity is necessary. Carr (2016) explains that the use of an abstract data model containing maps, arrays, references, and a variety of common atomic types (p15) can aid in unifying multiple data models. Carr continues by enumerating,

“The eight characteristics which collectively enable this system are as follows:

1. A generic data model capable of abstracting across a wide range of data models 2. Reflecting back a lossless view of the data

3. Supporting setlevel operations on arbitrary nested dimensions 4. Allowing arbitrary analytics on “schema” 5. Supporting all the relational operators (including joins) 6. Allowing queries across structurally polymorphic data 7. Enabling dynamic type identification and conversion 8. Supporting multi-dimensional pattern matching” (p15).

With these basic unifying factors, the analytics engine can work better to harness the data of xAPI statements gathered in the LRS. “Together, these characteristics form a robust, capabilities-based definition of what it means for a system to support generalized [data] analytics” (Carr, p16). Moreover, this list is a baseline and can “serve as a guide for companies to evaluate competing approaches to analytics” (Carr, p16). Now that we have established a framework, what do we use for output, reporting, statistics, etc.?

Building and Licensing Reports

5 An acronym for Structured Query Language, which is a code based language for managing data.



Systems, Applications & Products (SAP), a widely used global company, offers a dynamic solution for analytics output in their reports generator, Crystal Reports. “A robust production reporting tool, SAP Crystal Reports turns almost any data source into interactive, actionable information that can be accessed offline or online, from applications, portals and mobile devices” (SAP Crystal Reports, accessed July 2016). Not only does this reporting application pool data from a variety of data sources, but it also empowers Report Developers with time-saving and easily manipulated controls to create pixel perfect graphical reports (Archana Mankar, 2012). Moreover, “End users will benefit from the improved interactivity and functionality” (Ryan Oliver, 2016). With SAP Crystal Reports, we can access data with the following predominantly used programs:

• Online analytical processing (OLAP) data access

• Web Services data driver

• Salesforce.com data driver

• DataDirect drivers (Oliver, 2016).

Mankar (2012) recommends using SAP’s Web-Intelligence (Web-I) in conjunction with Crystal Reports in order to achieve the best reporting results. He explains, “Crystal Reports will probably be the primary tool for generating the pre-formatted [reports] delivered throughout the company.” Whereas, “Web-I gives business analysts and

management the tools they need to drill down into the data they need for making daily decisions.” Now, we have all the different components to guarantee success in our endeavors to create the optimal learning environment:

xAPI (communication across platforms and sources),

LRS (a data-mining hub),

Analytics (a tool to organize data), and

Reporting platform (a graphical interface for that data)

With these components, learning providers are equipped with the means to promote holistic learning, analyze learning paths, calculate ROI on formal/informal employee education, and formulate future learning goals.

Convenient on-report sorting buttons enable users to explore information interactively

Figure 3 SAP Crystal Dashboard Design Sample, www.crystalreports.com/dashboard/



What does the future hold for learning providers and learners alike? Although we are on the cusp of a new frontier of eLearning where our only limitations are our own ideas and creativity, there is a pressing need for some sort of framework to ensure the integrity of interoperability. Therefore, our first steps in ensuring the success of eLearning through xAPI is to broaden our scope of what “learning” really entails. Foreman (2013) expresses this need— “In order to take greatest advantage of the Experience API, we will have to expand our notions of what effective learning is (i.e., not just formal training), and incorporate these new ideas into our instructional design and work processes.”

Once the learning professionals are on board, our next hurdle may be to “sell” this new type of training to employers by showing how effective it is and how their businesses can reap the benefits.

Moreover, our next steps include “a unified direction, process, and governance strategy for xAPI vocabularies” surrounding action statements. This type of strategy “is desperately needed for the long-term success and overall maturity of both the structural and semantic interoperability of the xAPI” (ADL Technical Team, 2015, p6). Meaning, “In addition to structural data interoperability (the ability of two or more applications or

agents to exchange information), semantic interoperability is needed to automatically interpret the information exchanged meaningfully and accurately in order to produce useful and consistent results” (ADL Technical Team, 2015, p6). Currently, ADL is working to propagate the use of CMI-56 as an xAPI profile that will serve as the railroad tracks for future connectivity.

Thought Leadership: Where Learning and xAPI Are Headed…

6 For more information, please see Art Werkenthin’s article, “Experience API, cmi5, and Future SCORM,” The Learning Solutions Magazine. (May 21, 2015). http://www.learningsolutionsmag.com/articles/1697/experience-api-cmi5-and-future-scorm

“In order to take greatest advantage of the Experience API, we will have to expand our notions of what effective learning is (i.e., not just formal training), and incorporate these new ideas into our instructional design and work processes.”



All in all, xAPI, the LRS, and Reporting and Analytics tools will open the doors to “greater training and HR collaboration to create stronger linkages between learning and talent management” (Foreman, 2015). When it comes to career planning, succession planning, and performance management, employee development is at the forefront and will maximize ROI. The essence of xAPI gives learning providers “the ability to formalize and track work accomplishments and job assignments along with formal learning programs” (Foreman, 2013). This paradigm shift in learning “offers new opportunities for synergy in combining methods for developing employees” (Foreman, 2013).

By tracking that development in a more well-rounded method, learning providers and learners themselves will better understand where further instruction is needed and where learning might be lacking.

Once eLearning professionals fully grasp the potential of xAPI’s impact on the LMS world and embrace the willingness to invest in xAPI conversion, then they are bound to witness a strong ROI.

This paradigm shift in learning “offers new opportunities for synergy in combining methods for developing employees.” (Foreman, 2013)



Appendix Source – https://github.com/adlnet/xAPI-Spec/blob/master/xAPI.md#definitions

Activity: An Activity is a type of Object making up the “this” in I did “this”; it is something with which an Actor interacted. It can be a unit of instruction, experience, or performance that is to be tracked in meaningful combination with a Verb. Interpretation of Activity is broad, meaning that Activities can even be tangible objects such as a chair (real or virtual). In the statement "Anna tried a cake recipe", the recipe constitutes the Activity in terms of the xAPI statement. Other examples of activities include a book, an e-learning course, a hike or a meeting. Activity Provider (AP): The software object that is communicating with the LRS to record information about a learning experience. May be similar to a SCORM package in that it is possible to bundle learning assets with the software object that performs this communication, but an Activity Provider may also be separate from the experience it is reporting about.

Actor: An identity or persona of an individual or group tracked using Statements as doing an action (Verb) within an Activity.

Authentication: The concept of verifying the identity of a user or system. Authentication allows interactions between the two "trusted" parties.

Authorization: The affordance of permissions based on a user or system's role; the process of making one user or system "trusted" by another.

Base Endpoint: The maximal path under all Experience API endpoints, including a slash. E.g. an LRS with a statements endpoint of http://example.com/xAPI/statements would have a Base Endpoint of http://example.com/xAPI/"

Client: Refers to any entity that may interact with an LRS. A Client can be an Activity Provider, reporting tool, an LMS, or another LRS.

Community of Practice: A group, usually connected by a common cause, role or purpose, which operates in a common modality.

Computer Managed Instruction (CMI-5): The bridge between SCORM and the Experience API (xAPI). An easily implementable specification that provides structure and rules for those using a LMS with xAPI. It is the communication piece that provides data about the formal learning occurring in the LMS to the LRS.

Experience API (xAPI): The API defined in this document, the product of "Project Tin Can". A simple, lightweight way for any permitted Actor to store and retrieve extensible learning records, learner and learning experience profiles, regardless of platform.

Immutable: Adjective used to describe things which cannot be changed. With some exceptions, Statements in the xAPI are immutable. This ensures that when Statements are shared between LRSs, multiple copies of the Statement remain the same.

Internationalized Resource Identifier (IRI): A unique identifier which may be an IRL. In the xAPI, all IRIs should be a full absolute IRIs including a scheme. Relative IRIs should not be used. IRLs should be defined within a domain controlled by the person creating the IRL.

Appendix A – Key Terms in the Realm of xAPI



Internationalized Resource Locator (IRL): In the context of this document, an IRL is an IRI that when translated into a URI (per the IRI to URI rules), is a URL. Some communities of practice simply use URL even if they use IRIs, which isn't as technically correct within xAPI.

Inverse Functional Identifier: An identifier which is unique to a particular persona or group. Used to identify Agents and Groups.

Learning Management System (LMS): "A software package used to administer one or more courses to one or more learners. An LMS is typically a web-based system that allows learners to authenticate themselves, register for courses, complete courses and take assessments" (Learning Systems Architecture Lab definition). In this document the term will be used in the context of existing systems implementing learning standards.

Learning Record Store (LRS): A system that stores learning information. Prior to the xAPI most LRSs were Learning Management Systems (LMSs); however this document uses the term LRS to be clear that a full LMS is not necessary to implement the xAPI. The xAPI is dependent on an LRS to function.

MUST / SHOULD / MAY: Three levels of obligation with regards to conformance to the xAPI specification. A system that fails to implement a MUST (or a MUST NOT) requirement is non-conformant. Failing to meet a SHOULD requirement is not a violation of conformity, but goes against best practices. MAY indicates an option, to be decided by the developer with no consequences for conformity.

Profile: A construct where information about the learner or activity is kept, typically in name/document pairs that have meaning to an instructional system component.

Registration: An instance of a learner experiencing a particular Activity.

Representational State Transfer (REST): An architecture for designing networked web Services. It relies on HTTP methods and uses current web best practices.

Service: A software component responsible for one or more aspects of the distributed learning process. An LMS typically combines many services to design a complete learning experience.

Statement (or Action Statement): A simple construct consisting of <actor (learner)> <verb> <object>, with <result>, in <context> to track an aspect of a learning experience. A set of several Statements may be used to track complete details about a learning experience.

Tin Can API (TCAPI): The previous name of the API defined in this document, often used in informal references to the Experience API.

Verb: Defines the action being done by the Actor within the Activity within a Statement.

Appendix A (continued)



Advanced Distributed Learning (ADL) – https://www.adlnet.gov/xapi/

Advanced Distributed Learning (ADL) – https://www.adlnet.gov/adl-research/performance-tracking-analysis/experience-api/

Adlnet / xAPI-Spec – https://github.com/adlnet/xAPI-Spec/blob/master/xAPI.md

Rustici Software, Tin Can API – www.tincanapi.com

[SaLTBOX] Experience API Resources – http://www.saltbox.com/experience-api-resources.html

SAP Crystal Reports – http://go.sap.com/product/analytics/crystal-reports.html

Video – https://youtu.be/t0_XHzX0ydQ

Resources



Advanced Distributed Learning (ADL) Co-Laboratories. 2013. Experience API. Accessed June 14, 2016. https://github.com/adlnet/xAPI-Spec/blob/master/xAPI.md

ADL Technical Team. 2015. Controlled Vocabulary Considerations for the Experience API (xAPI). February. [White Paper]. Accessed June 8, 2016. https://docs.google.com/document/d/1zBPKryuF1tXHTI-AYjXd0ctdWoq4o4P-Uq9SAhJfus0/edit?pli=1#heading=h.gjdgxs

Berking, Peter, Steve Foreman, Jason Haag, and Craig Wiggins. 2014. “The Experience API—Liberating Learning Design.” The eLearning Guild. [White Paper]. Accessed June 25, 2016. http://www.elearningguild.com/insights/index.cfm?id=177&from=content&mode=filter&source=insights&topic=35%2C33%2C9

Carr, Jeff. 2016. “Characteristics of NoSQL Analytics Systems.” SLAMDATA. June. [White Paper]. Accessed June 14, 2016. http://slamdata.com/ Foreman, Steve. 2013. “The xAPI and the LMS: What Does the Future Hold?” Learning Solutions. October 14. [eMagazine]. Accessed June 7, 2016. http://www.learningsolutionsmag.com/articles/1271/the-xapi-and-the-lms-what-does-the-future-hold

—. 2015. “Survey: Learning and Performance Ecosystems: Current State and Challenges.” Research Insights Library. January 14. [White Paper]. Accessed June 25, 2016. http://www.elearningguild.com/insights/index.cfm?id=179&from=content&mode=filter&source=insights&topic=35%2C33%2C9

Kitto, Kirsty. 2016. “Data Interoperability for Learning Analytics and LifeLong Learning.” Queensland University of Technology. February 17. [Online Presentation]. Accessed June 22, 2016. http://www.slideshare.net/MeganBowe1/data-interoperability-for-learning-analytics-and-lifelong-learning

Mankar, Archana. 2012. “High level Differences between Crystal Reports and Web-I Reporting Tools.” Software, Applications, and Programs (SAP). June 6. Accessed July 14, 2016. http://scn.sap.com/docs/DOC-28904

Oliver, Ryan. 2016. “Top reasons for a .NET or Java developer to upgrade to SAP Crystal Reports 2013.” SAP. April 22. Accessed July 15, 2016. http://scn.sap.com/docs/DOC-27493

Rustici, Mike. 2015. “Tin Can API: Connecting Learning Experiences—Next Gen SCORM.” Rustici Software. August 11. Accessed May 24, 2016. https://tincanapi.com/wp-content/assets/Onion/Layers-of-the-Tin-Can-Onion.pdf

SAP Crystal Reports. “SAP Crystal Reports.” Capterra. Accessed July 14, 2016. http://www.capterra.com/reporting-software/spotlight/144572/SAP%20Crystal%20Reports/SAP%20Crystal%20Reports

Shahrazad, Ali. 2015. “Introducing Experience Paths.” SaLTBOX: Thoughts on Workplace Learning and Technology. October 16. Accessed June 15, 2016. http://blog.saltbox.com/blog/2015/10/16/introducing-experience-path-analysis-in-wax-lrs/

Training Industry (2016). “70:20:10 Model for Learning and Development” Accessed September 6, 2016. http://www.trainingindustry.com/wiki/entries/the-702010-model-for-learning-and-development.aspx

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