Don’t Eat the Elephant

By Myra Travin (Educational Futurist)

Don't Eat the Elephant

It’s no surprise that the first product adopters to implement Experience API were eLearning authoring tools, with a quick follow of mobile learning apps. It’s not surprising because the one factor that drives most everything in the learning space has less to do with learners than authors: expediency. We are designing under deadline and budget constraints and as learning specialists—we are where the buck really stops.

If learners aren’t learning and affecting the bottom line in a positive manner, all the ideas in the world won’t matter. eLearning and now mLearning tools and LMS that allow for tracking and interoperability help us get things done. Although xAPI is not simply “SCORM 2.0,” it is a bridge from the concept of one vision of interoperability to a newer and more expansive concept of it in learning, meaning, and performance outcomes. The heart of the matter is to decide something very powerful about learning: What does meaning actually mean, and who decides it? If there ever was a complex issue, this is it.

Don’t eat the elephant

Certainly, the ability of an LMS to track its learners is the first way to see into the puzzle, but as we determine relevant standards and the technology that will be utilized to express them, we are in the midst of an era of change and disruption that has put the concept of learner control into hyper drive. xAPI supports a much more expansive concept of curated learning experiences which can be incorporated into a learning ecosystem, such as the framework I designed in 2015. There is no going backward in this new frontier. Based on the critical factors of learner access, it is akin to the Protestant Reformation in 1517 with Martin Luther’s critique of doctrinal principles that suggested that the experience of inspiration should not be secondary, but rather primary and controlled by the individuals themselves. The consumer revolution in mobile device access has given the keys to the kingdom to the rightful owners, in my opinion: the learners. But in an industry that is dealing with so much change—theory, globalization, technology, information security, privacy, machine learning, and the implications of the cloud data storage—it is no wonder if we, as learning and development professionals, feel a bit overwhelmed. Just when is this boat going to get to a safe harbor?

Never. The world according to learners is like a kaleidoscope, constantly changing around us while we view an ever-moving landscape and work to make real-world decisions about products, standards, and adoption. As one of those who is making these decisions and looking to the future to determine the choices that need to be made in the present, I can say this: Don’t eat the whole elephant at once. It’s not as if we personally have to solve the entire issue of standards adoption, or elect a single product to utilize them, before we make our first move. I may be just like you: I am just trying to get something done that works. There is no doubt that it is risky to be an early adopter, but it is also equally risky to be a late adopter in the learning sphere.

There is no perfect moment

To implement an xAPI project, one does not have to wait for the perfect moment to begin. In a global pilot project at a Fortune 50 multinational technology corporation, I utilized a target group of more than 150 participants in several locations for a Mentorship program design for Science, Technology, Engineering, and Mathematics (STEM) leadership training. Mentors were chosen from a core group of managers, and the matching process was initiated by the mentees themselves, choosing from a list of willing mentors based on descriptions of their values, technical expertise, and approaches.

The program design was based on best practices of mentoring within the organization and new benchmarks that would lead to greater global partnerships, as well as a just-in-time persistent chat strategy of problem intervention at the point of need. The evaluation and assessment strategies were founded upon the Brinkerhoff model, and protocols were developed in accordance with the success-case model practices.

The fundamentals of the method require an identifiable business goal or set of achievable metrics, a representative sample of both high- and low-impact participants, expert models from successful candidate interviews, identification of high-performance factors, documentation of performance obstacles, and the creation of a narrative that outlines the success metrics for the target group.

Global communities of practice were formed to meet program requirements. A feedback loop for assignments and frequency of communication data was created, as well as chat protocols, YouTube channels, video- and Web-based conferencing, and curated resources based on learner and manager preferences. This data design was created with placeholders that could be used in the organizational implementation of LRS data from the outset.

Expect changes will happen

A Fortune 500 technology company with a global crowdsource training program was chosen for a six-month pilot phase evaluation with the intent to combine mentorship with desired training outcomes and performance standards to improve both individual learner performance and the technical leadership capabilities of the program managers. At the start of the program, learners in five countries participated in an eLearning-based modular curriculum with very little interaction with managers, trainers, or participants.

The design was centered on a virtual lab environment, which accommodated the differences in time zones and participation. At the start of the pilot there was very little data available on the competency levels of individual managers or trainers in a management or coaching position. The learning and development group performed standardized Kirkpatrick Level 2 knowledge-based testing, and the clients carried out performance support real-time testing during the duration of the training phase. At the end of the training phase, learners who passed gating testing went into a production environment, where they continued to be tested within the work environment. Unfortunately, the client data was gathered in such a way that the implications of the analysis were unclear in regard to the technical managers’ role or effectiveness in their process. Clearly, a mentorship model that trained managers to assist learners as they began their roles in the organization was an effective intervention.

Mentoring software was used to house the email, chat, curriculum, and real-time interactions so that all communications would be available on mobile and multi-device platforms when the learners and/or managers needed to communicate. All communications were archived and searchable for words, people, documents, assignments, or dates. Again, the focus on the current technology, LMS, and xAPI conformance were built into the design as the organization chose a new LRS.

The new normal

What this new normal has brought with it is a cathedral of acronyms. We are trying to make sense of new perceptions of who our learners are and how to track them through their process. We as designers must see that our role has changed—we must become data citizens. As Andy Hargreaves suggests, “Teachers will need to be the drivers, not the driven” (2007). We need to be a part of the solution, not just consumers of others’ ideas.

You must become a part of the conversation and a contributor to the field in your pilot projects, as this will help you to deeply understand the implementation issues involved. Start small. Do the research. Start somewhere—but start. Find a place in your organization where xAPI can be piloted, and depend upon the good counsel of those who have proved themselves worthy to you. If you aren’t a part of the discussion, lending your experience and learning along with innovators, you may not ever find a safe harbor, but you will most certainly find that you have been left stranded upon the shore.

Why, if these types of projects can be accomplished with a focus on three areas—technology, strategy, and standards at the most feasible point of entry within an organization—do we wait for the perfect opportunity that will never arise?

In the Global Survey on Knowledge Management written by McKinsey in 2001 (which is eerily prescient about the value of social and personal data), they outline a critical argument: “They argue that knowledge management is much more than simply installing a new database and can only be successful when it is at the heart of everyday personal exchanges, personal incentives, and personal responsibilities at every level of the firm.”

Ready to start

In 2001, we knew the value of data and the essential aspect of recording it in our systems. How is it we are still waiting to adopt this reality? As unsettling as it may be to be an early adopter, it is clearly devastating to be a late one. We pay a steep price for waiting. That price is the momentum of our organizations, the cutting-edge knowledge and skills we need to be the drivers of innovation in our careers. There is no perfect place to start, only the determination and courage to move forward and live within the perpetual beta of change that will require an agile and ongoing culture of responsiveness.

The industries that profit from talent management have a vested interest in keeping things digestible for you: You may buy an easy button to leave all the complexity behind, but as systems and software have taught us, abdicating a personal relationship with these concepts can result in useless forms data, lifeless online collaborations, and dreadful eLearning. As a futurist, it is my job to look at what exists today and prepare for what is happening tomorrow at the same time. The projects I do reflect the reality of preparation and do not expect any one system or standard to be my only answer. As we move into adaptive learning engines, the fun has just begun for us. I only know that I need to be an active participant in the community and add to best practices. We must be the change we wish to see in the world, as Mahatma Gandhi said. He was not referring to technology and practice, but he might as well have been.


Farrand, W., & Travin, M. (2015). Global Perspectives on Mentorship Design and Practice for Technical Leadership, Unpublished Manuscript, UNM Mentorship Institute Conference Proceedings.

Kluge, J., Stein, W., & Licht, T. (2001). Knowledge Unplugged: The Mckinsey & Company Global Survey on Knowledge Management. New York: Palgrave.

MYRA TRAVIN (Educational Futurist)

16ef661e45a0-Myra_009In the equation of technology and learner, Myra Travin believes the key element is how humans develop a relationship with the tool they are using and how together they co-create a future state of resourcefulness. She believes that the music is not in the piano, but in the interaction between user and technology. In other words: Learners First. People are the key to innovation, she believes, not simply the systems they use—but systems can reveal the true nature of people through algorithmic predictive data strategies. In her new Learner Personalization Driven Ecology design, she brings all elements of personalization, choice, and access into self-supporting environments. She is an Innovative LX Designer and Educational Futurist with significant experience in user research, use cases, user journeys, surveys, academic papers, data collection, analysis, and user testing within project implementations of Information Security, Organizational Development, Mentorship, UX/UI Design, Predictive Analytics Design, mLearning Design and Delivery, Change Management, Stakeholder Communications Strategies, and CRM projects. Myra has worked in disciplines ranging from higher education with universities to such Fortune 500 companies as Microsoft, Hewlett-Packard, BP, Walgreens, PwC, and SPSS Inc., and public sector agencies such as Los Alamos National Labs, The Bill & Melinda Gates Foundation, and The Ministry of Forests in Canada. She has a deep interest in and a commitment to relationship and interaction within learning environments and assisting individuals in understanding their nature, behavior, and motivations to significantly increase their performance.

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