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The top consumer technology transformations were revealed by Dr. Shawn Dubravac, Research Director, Consumer Technology Association. These trends will impact enterprise technology over the coming months and years.

1. The Next Computer Interface is Voice

The word error rate is now at human parity, meaning the graphic user interface will disappear ushering in an era of faceless computing. Voice will be the command function for digital devices including robotics, A.I., etc.

2. Increasing Intelligent Systems  will Connect Diverse Objects

Software is now found in hardware out of the box, and hard- ware is eating software. Alexa is found in refrigerators and automobiles. While google’s newest smartphone is embedded with AR/VR software out of the box.  “This creates a ‘physical manifestation of data’ in our lives,” says Dubravac.

3. Transportation Transformation

The self-driving car was the catalyst for intelligent systems. With connected systems reporting to other cars, the vehicles can respond and react without human intervention. This is a model of many “robotic” type activities that may complement or replace human interactions.

4. A.I.’s Infusion into Real Life

Blending data from diverse devices is improving signal, functionality, and recommendations for users to follow. Hub de- vices will be used for vocal computing. A.I. will boost informa- tion processing geometrically. For example, Google cars have already driven more miles than a human can in 75 years. The speed, experiences, data collection and sharing has increased geometrically.

5. Digitizing the Consumer Experience

From wearables to smart home, online and mobile characterizes consumers tastes. Drone purchases reached 1.1 million units in 2016; VR 700,000 units; smart watches 5.5 million units and fitness trackers 12.6 million.

—Source: CTA.tech  bit.ly/CES2017TRENDS

Published in Insights

BY PRADEEP KHANNA

The other day, I went to meet someone in downtown Sydney, Australia. On my way, back on the local train, I looked at my mobile to check my emails and found a message asking me whether I would like to meet the person I had just connected with on my LinkedIn network. So, was this some form of artificial intelligence (AI) at play?

Yes! We now live in a brave new world where AI is the next frontier. We keep hearing about bots, chatbots, teacherbots, digital assistants, machine learning, deep learning and many more such words and often wonder what do they mean.

Just like virtual reality (VR), AI has been around for quite some time. In fact, I remember taking AI as a subject when doing my second master’s degree in computer science at University of Technology in Sydney 17 years ago. So, why so much fuss about AI now? AI will reshape how we live and work, but will AI also reshape the way we learn?

ABOUT BOTS, CHATBOTS, TEACHERBOTS AND A.I.

A bot is software that is designed to automate repetitive tasks. Bots have been around for quite some time. An example is use of bots for searching and cataloguing Web pages for search engines. Another example is shopping bots which pull out prices of an item from different vendors from the Internet. Some recent examples are bots making dinner reservations, adding an appointment to the calendar, or fetching and displaying information.

Chatbots are bots that conduct a conversation mirroring potentially a real-life conversation. Chatbots can either be simple rule-based or more sophisticated AI-based. AI-based chatbots get smarter as more interactions take place. The popularity of messaging apps has been lifting the demand for chatbots. Another way to look at the rise of chatbots is the user migration from Web to apps and now from apps to chatbots.

What are teacherbots? Just like a bot or a chatbot, a teacherbot can be a simple rule-based or smart AI-based. Simple rule-based teacherbots can automate simple teaching tasks whereas a smart AI-based teacherbot can become a teaching assistant (TA). A yet smarter AI-based teacherbot can also be personal tutor.

TEACHERBOTS AT WORK

There are two well known instances of teacherbot pilot projects at the University of Edinburgh in the U.K. and Georgia Tech in the U.S.A. The University of Edinburgh teacherbot project was led by its School of Education in collaboration with the School of Informatics and the Edinburgh College of Art. It was launched in April 2015 by Siân Bayne, professor of Digital Education. “Botty,” as this teacherbot was affectionately called, was created to engage on Twitter with students of Edinburgh’s e-learning and Digital Cultures MOOC on Coursera. This MOOC has had 70,000 enrollments across three course runs. The teacherbot’s primary role was to act like a TA. It could answer simple questions on deadlines, course content, etc. It was also able to answer some complex questions as well, based on AI that had been developed on stored tweets with Twitter hashtag #edcmooc. In “Botty’s” case, the students were aware that a teacherbot and not a human being was answering their questions.

Georgia Tech’s teacherbot was developed by Ashok Goel, a professor of Artificial Intelligence at Georgia Tech. Typically, the 300 students at Georgia Tech’s online AI course posted around 10,000 messages in online forums during a semester. Many of these questions were repetitive in nature. This was enough of a driver for Ashok to initiate work on the teacherbot. Leveraging IBM Watson’s technology platform and a databank of 40,000 questions and answers from past semesters, Ashok developed the smart AI overlay for the teacherbot, calling it “Jill Watson. The students were not told that TA “Jill Watson” was a teacherbot.

“Jill Watson” was launched in Jan 2016. As expected, its responses were not very accurate in the beginning, so responses were moderated by the human TAs before posting in the online forums. But by April, it had become sufficiently “intelligent” to answer the questions without human intervention.

The table on the following page compares the two pilot teacherbot projects.

ELM March A.I. Already Reshaping

Many factors determine the accuracy level of any AI project, including the AI technology layer at the infrastructure level, the size of the database, and the contextualizing smart AI layer. Looking at the above comparison between the two projects, the Georgia Tech project does have an advantage of using IBM Watson as a technology platform and having a database of 40,000 questions and answers from previous courses. No wonder, it performed better.

AI NEXT IN LEARNING?

The potential of AI to disrupt education and skills training sectors is immense. As Microsoft’s Bill Gates remarked sometime back, we already have online tutoring services where humans provide the services while the platform is online. Smart AI-based teacherbots can replace the humans to provide personalized learning. This has special relevance in lifelong learning scenarios where we will be dipping in and out the learning continuum all through our life.

Automated assessments are a natural application of AI in education and skills training. This application gets further amplified when  large number of assessments are being done in an online environment. Use of technologies like Facebook’s facial recognition technology and proctoring are classic examples.

Are we already there in the brave new world where AI is reshaping the way learn? IN these early days, where we are seeing AI-based projects being rolled out in different parts of the world. In the first instance, the focus appears to be on automating routine teaching tasks. This is akin to the Robotic and Process Automation (RPA) implementation onslaught we are seeing in other industries.

“Jill Watson” is estimated to have taken 1500 hours to develop. When many “Jill Watsons” are produced in 15 hours is when we will see real disruption in education and skills training.

Developments in AI in education and skills training will to an extent follow the developments in AI in general. With all major technology innovators investing heavily in AI, it appears certain that our working and learning will get reshaped by AI in future.

—Pradeep Khanna is founder & CEO of Global Mindset (www.globalmindset.com.au) and Technology-enabled Innovations in Learning & Teaching (TILT). He works on enhancing collaborative learning across boundaries and by leveraging technology. Khanna has also been Global Delivery Leader for IBM GBS Australia/New Zealand. He lives in Sydney, Australia, and can be contacted via email at This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Published in Insights

6 STRATEGIES FOR MAKING YOUR TRAINING STICK - BY DEAN PICHEE, CEO, BIZLIBRARY, INC.

THE SCIENCE OF LEARNING

Many professions use science to improve the outcomes of their work. For example, architects use the principles of physics and math to design buildings that will function safely and last decades or even centuries. Architecture is often equated with art, but it's the science behind the art that truly makes it work.

In much the same way, we as learning and HR professionals need to understand and use our knowledge of the science of learning to improve the outcomes of our efforts in training employees. What does science tell us we should do to improve the way employees learn?

Here are six things you can start doing today:

1. Make learning bite-sized. Use short, relevant, video-based training (microlearning) focused on individual concepts.

2. Space training out over time. Employees should use the information they learn during training within the first 24 hours after the training event and in the days and weeks to come. Time is on our side here!

3. Add post-training reinforcement. Use quizzes, polls, videos and other resources to reinforce key concepts.

4. Mix it up. Combine training of multiple related skills rather than focusing on one skill at a time. Scientists call this learning concept interleaving.

5. Make it difficult. Resist the temptation to make training easy for learners. Challenging them actually increases the learning impact. One of the ways to make it more difficult is to increase the amount of time between testing and retrieval opportunities.

6. Write to remember. Your brain will recognize more of what's important when you write what you learn.

WHAT WORKS?

We call microlearning and post-training reinforcement "Burst and Boost Training." Using a combination of "bursts and boosts" is a proven way to get more ROI from your employee training program. Bursts of microlearning have been proven by cognitive psychology to be the most effective way learners receive information. Cognitive load theory states that we have mental "bandwidth" restrictions. The brain can only process a certain amount of information before reaching overload. To improve training content, chunk it down into bite-sized bursts to lower the cognitive load. Microlearning is very popular today and is a key component of BizLibrary's online training solutions.

Boosts, or post-training reinforcement, has been shown to increase long-term memory. Testing can actually INCREASE learning more than any other study method. Scientists call this idea "The Testing Effect," and numerous studies have shown that long-term memory is increased when some of the learning time is devoted to retrieving the to-be-remembered information. Incorporating tests and quizzes into employee training programs is more than just measuring the amount of learning that has taken place ... it's a critical part of the learning itself. Resist the temptation to skip testing and boost learning!

THE GREAT TRAINING ROBBERY

Stop the great training robbery that occurs when we deliver programs that are too long, too boring and easily forgotten. Microlearning is the first step. It's also crucial to add on-going reinforcement. Think of post- training reinforcement as the deadbolt on the door of your house, keeping the valuable information you're delivering to your employees from being forgotten and ultimately, maximizing the ROI of your program.

Published in Insights

Online learning continued to grow ex- ponentially, partially fueled by com- panies like Udemy, Lynda.com and Coursera. With employers more willing to accept that this type of courseware is necessary, we expect other related trends to emerge. The top five learning predictions for this year are:

1. EDUCATION HACKING

The churn in technology advancement - both software and hardware - leaves a lot of traditional educational facilities in a tough spot. Most times, universities and colleges find that their course- ware is being rapidly obsolesced by new advancements that occur in 9-12 month increments.

An example of this rapid obsolescence can be seen with some of the new cloud computing companies. Amazon Web Services boasted that it has over 700 significant changes to their cloud computing infra- structure each year. That means that if you’re going to participate in that arena, you can’t expect to find that content in traditional degree courses.

2. TECHNOLOGY BOOT CAMPS

These are coding boot camps that compress the learning process into weeks instead of semesters. Their popularity has spread quickly with venues like General Assembly, which has opened up campuses throughout the country to meet demand. 

But don’t count the universities out just yet. Many entities are expected to announce their own versions of these technology boot camps, which offer professional courses versus credential courses to their students. The University of Phoenix has launched one such venture called Red Flint, in Las Vegas, Nevada. You can expect to see them increase that capability as they re-tool to be more responsive to current trends.

3. MICRO-CREDENTIALS

These are non-degree courses that offer expertise in niche areas like technology, but also other areas where there is a shortage of talent. These courses cost a fraction of typical education venues and can be stacked to create a customized educational experience, i.e., the “hacked” education venue.

With more employers warming to online certificates, and people changing jobs more often, expect this particular trend to grow exponentially. In an age where there is continuous change, the need for continuous learning is a foregone conclusion.

4. APPRENTICESHIPS

This is expected to be another area where we’ll see greater growth, as evidenced by the agreement between Amazon and the U.S. Department of Labor announced earlier this year. This particular program announced an apprenticeship program to train veterans for tech jobs at Amazon. One of the unique benefits of this type of program is that the veterans can earn a salary while learning the skills needed for the job. We expect other major software and technology companies to follow this trend.

5. BRICKS AND CLICKS

We see this as a more accepted venue as educators in the corporate space focus on the unique job roles that have to be brought up to speed across their enterprises and ecosystems. While it was pretty easy to dump everything into the classroom venue in the past, the huge economies of blending online training with classroom venues will continue to push this trend further.

We expect that the ultimate solution in the next few years will be the enactment of the 20/80 model. That model suggests that 20% of the training will occur in the classroom, while 80% of the training is being provided by a combination of online and embedded learning - the latter of which is training within an application, or like in the Amazon apprenticeship program, right on the warehouse floor where employees can access the training at the point of need.

Published in Ideas

Economists have predicted that a rapid period of innovation follows an economic downturn. We are in that innovation cycle. We once could count on an obsolescence cycle of 24 months (thanks to Moore’s law), which was condensed to six months (the life of a smartphone). Now, we are learning and evolving instantly thanks to A.I. and machine learning.

In 2017’s Annual E-learning User Study conducted by Elearning! magazine, 65.6% of respondents are using machine learning today, and 46.9% are planning to purchase. Artificial intelligence is deployed by 31.8% of respondents with 72.7% planning to deploy over the next 12 months, a 228% compounded annual growth rate (CAGR). Augmented reality and virtual reality are close behind with 68.6% and 67.6% planning to deploy. (See article E-learning User Study.)

These advancements are transforming our practices, ecosystems and knowledge base. In the article titled, “Three Disruptive Macro Trends Shaping Enterprise Technology,” we tapped Gartner and leading learning technologists to share their insights and implications (see article Disruptions in Enterprise Technology). Dr. Shawn Dubravac from Consumer Technology Association also identified five transformational technology trends (see article 5 Transformational Technology Trends). Pradeep Khanna also shares his views on A.I. in learning (see article The AI Effect: Are You Ready), and Joe DiDonato makes five learning predictions for 2017 (see Top 5 Learning Predictions for 2017). All conclude that technology’s rapid evolution is spurring transformation at home and at work.

Nothing is gained without the steadfast commitment by our peers, partners and technologists. Elearning! magazine recognizes 28 Learning! Champions who have made extraordinary contributions to the learning industry. Three professionals earned our Lifetime Achievement Award: Elliot Masie, Kevin Oakes and Joe DiDonato. We are honored to feature all 28 thought leaders, trail blazers, innovators, mentors, and high performers inside (see article 2017 Learning! Champion Awards). You will hear from these champions across the year via articles, conferences, Web seminars and blogs. The 2016 Learning! Champion, Dr. Christopher L. Washington, shares his article titled “The Evolution of E-learning and Learning Analytics” on article The Evolution of E-learning and Learning Analytics  .

Thank you to all the learning professionals, technologists and colleagues who continue to advance learning everywhere.

Let’s keep learning!

—Catherine Upton, Group Publisher

Published in Ideas

Every day the enterprise learning ecosystem becomes more complex making a few questions even more important for learning and development leaders. What is the current state of the training function in your organization large or small? How do you evaluate the effectiveness of your training?

Only 8% of CEO’s in LinkedIn’s 2017 Workplace Learning Report say they can see a measurable impact from their company’s Learning and Development. These CEO’s are getting quantifiable activity data from other business functions, so why not L&D?

Chances are, your learning has now spilled out of the confines of an LMS, and touches a TMS, HRIS etc. You may have many of these systems in your organization along with new 3rd party providers, self-directed learning, or apps and portals available to your learners. You are probably spending L&D budget on micro-learning, self- paced learning, gamification, mobile, and more. Surveying aside, how effective are those new initiatives and training techniques? Are you able to track anything more than completions? Are you even able to track completions?

The first step to providing measurable impact is to baseline the effectiveness of your current training by getting better interaction data wherever learning occurs. You can baseline ALL of your current training across multiple learning technologies and you can start today.

It is relatively easy to get all of your training initiatives reporting better learning activity data in the form of Experience API (xAPI) activity streams to a Learning Record Store (LRS). Think of xAPI as a digital mesh that will get all of your proprietary learning technologies talking in the same analytics language. You can mine xAPI activity streams for patterns and react to them. You can keep your LRS data totally anonymous if you would like. xAPI is also technology agnostic so when you add new technologies or remove technologies within your ecosystem it is non- disruptive to your learning activity reporting. But most importantly, an LRS will provide you the learner activity data for formative and summative evaluation.

BENEFITS OF LRS:

1. Baseline your current training with better evaluation data.

2. Begin to build learner competency and performance profiles.

3.  The proper implementation of xAPI/LRS is the first step toward:

  1. Intelligent/Automated Tutoring
  2. Adaptive Learning
  3. Predictive Analysis
  4. Sustainment and Improvement of Training Systems

 

The path to modern training technology and the future of learning starts with xAPI and the implementation of a Learning Record Store. At Riptide, we have been working and engineering learning technology using xAPI since just after it’s inception. Before it was even called xAPI we were generating activity streams to early versions of our LRS, which is now our Storepoints LRS product. We are on the workgroup that created xAPI 1.0 and we are working with it daily.

Interested in learning more on how a Learning Record Store would work within your unique learning ecosystem? Visit www.RiptideLearning.com and request a free consultation today!

—Nick Washburn is Director of Learning at Riptide Software. Contact him at This email address is being protected from spambots. You need JavaScript enabled to view it.

Published in Ideas

10 Leadership Lessons from Higher Education - BY CHRISTOPHER L.  WASHINGTON

In ancient mythology, Janus was the Roman god of change and transition. Artistically depicted as having two faces positioned in opposite directions, Janus possessed the ability to see both into the past and into the future. Today, data analytics, which encom- pass the processes of extracting, compiling and modeling data, enable modern man to discover truths about the past and to render forecasts about the future.

I have found that learning analytics, the educational application of data analytics, hold the potential to magnify the view into how teachers teach and how students learn. They also illuminate the environmental conditions under which learning occurs. With learning analytics, I am able to debunk myths, supplant hunches, and confirm or disconfirm intuitions about teaching and learning. Decisions informed by learning analytics have led to a substantial rethinking of instructional methods and their benefits. Additionally, there has been a change in organizational culture from one in which quality is implied by inputs such as faculty credentials, to one that supports systemic assessment, continuous improvement, and greater accountability to stakeholders based on learning outcomes. I present 10 leadership lessons learned from my experience as a Learning! Champion.

LESSON 1: There is a symbiotic and co-evolutionary relationship between e-learning and  learning analytics.

While nearly every other profession outside of the academics is required to prove their effectiveness, up until the turn of the 21st century, higher education was largely exempt from external accountability. With an increase in public demands for greater access, lower cost and higher quality education, there was an increase in institutional pressure to demonstrate accountability.

To determine if e-learning methods are as effective as traditional face-to-face modes of instruction, circa 2000 the U.S. Department of Education (DOE) established a pilot project. At the time, there were strict rules limiting colleges and universities from offering more than 50 percent of a program’s courses in any form of distance education. To remove its restrictions on distance education programs, there had to be sufficient justification.

Franklin University was one of the higher education institutions selected to participate in the pilot project. Pilot program participants gathered and analyzed the data, reported it publicly, and noted how the results were used to improve educational processes and practices. This expectation is now a standard for academic quality review in higher education. Based on the data presented as evidence of instructional equivalency, colleges and universities are now able to offer distance education programs and to disburse federal financial aid to students who enroll in them. 

Fast forward to 2017. A lot has changed in the past 15 years since the DOE’s pilot project established a foundational framework for the use of data as evidence in determining the effectiveness of e-learning methods compared to traditional face-to-face modes of instruction. Figure 1 reflects some of the contemporary data sources used today to shed light on the effectiveness of curriculum and instruction, students, faculty and the learning environment.

ELM march The Evolution 1

Academic leaders today recognize that student learning experiences both influence and are influenced by factors in and outside of the classroom. Consequently, data is now being collected across multiple systems and treated and analyzed in a more integrated way. At Franklin University, we’ve moved beyond student attitude surveys of faculty members and courses, to an examination of student “clicks” on media, time spent viewing videos through the LMS, or pages read of assigned e-text through the library. We can examine spikes in tutoring requests and send early alerts to academic advisors when students are falling behind on assignments. We can see if faculty have participated in faculty development workshops, and begin to correlate faculty development data with student success data. The activity of our students in relation to interactive media now signal needed improvements to our curriculum design. The result of the relationship between learning analytics and instructional practices is a continuous refinement of questions and analysis techniques, and a resultant evolution of instructional practices. 

The adoption and expansion of e-learning methods in higher education continues to this day. According to the “2015 Online Report Card: Tracking Online Learning in the United States," conducted by the Bab- son Survey Research Group, more than 25 percent of the more than 20 million college students in the United States enrolled in at least one course online. Overall growth rates for online course enrollments grew at a rate of more than 7 percent from 2012 to 2014.

LESSON 2:  The price of light is less than the cost of darkness.

Higher education institutions (HEIs), places of both progress and tradition, present a special case study for educational leaders who aim to overcome resistance to incorporating new methods and technologies. According to the Babson Study, in 2014, 29.1 percent of Chief Academic Officers believe that members of their faculties accept the value and legitimacy of online education. Many leaders of HEIs perceive value in technology-enhanced instruction but struggle to get faculty members to adopt learning technologies, develop the talent to use it, or to develop the administrative processes to capture the value from learning analytics.

Perhaps the greatest challenge for leaders of HEIs is getting faculty buy-in. The deep traditions of higher education and significant skepticism of e-learning methods require an honest assessment of the effectiveness of current practices, leaving open the possibility of alternatives to traditional approaches to teaching and learning. Educational leaders who can use information to shed light on a culture that reinforces mediocrity are well positioned to develop a strategy that focuses on learner success, continuous improvement, and the use of learning analytics to make data-informed decisions. Existing data elements are essential in establishing a culture where individuals leverage new technologies to in- form teaching practice and develop a level of comfort with learning analytics. For example, existing industry and research reports on the effectiveness and increasing popularity of e-learning methods set the tone for our institutional conversations.

LESSON 3:  Strategy comes before measurement.

A clear educational strategy should drive the system of measurement and not the other way around. Measurement tells educators if our strategy is successful or not and where there may be opportunities for improvement. Data is enormously valuable in analyzing the teaching and learning processes. However, when one emphasizes metrics without the proper strategy in place, the result can drive behaviors that lead to data manipulation and other misuses of informa- tion. Lastly, in making the point that data informs rather than drives practice, it is important to clarify the limitations of data and to express a desire to honor the experience and intuitions of faculty and staff members.

LESSON 4:  “Quality is not an act; it is the result of intelligent effort.” —John Ruskin

One point often taken for granted in HEIs is that faculty members all have the same definition of academic quality. In fact, members of our faculty had very different ideas about quality, and if and how it could be measured. In defining quality, definitions ranged from the presence of a qualified faculty member, covering subjects, meeting the tradition of the discipline, fidelity to the standards of excellence set by experts, student satisfaction, meeting students goals, meeting faculty members’ goals, the learning process added value, and continuous improvement of the teaching and learning process based on assessment data. In some professional fields, the definition of academic quality should include a larger group of stakeholders that may include employers, associations and professional organizations.

Based on our quality conversations, we shifted from a focus on subject and content coverage to a focus on determining how students can apply knowledge learned in real-world settings. Early conversations also considered questions such as, “What do we dream our students will learn from us in our courses?” and “What would you want graduates to say about their learning experience?” and “What kinds of learning experiences would you want for them in order to succeed after college?”

We identified our goals to: assure high quality instruction across all academic programs; clarify valuable and rigorous learning outcomes for students; assure activities and assignments align to learning outcomes; allow students to experience meaningful and relevant learning activities and assignments; and make instructional materials support the needs of the instructors and learners.

LESSON 5: “Start with the ending; it’s the best way to begin.” —David Wilcox

In academic settings, inputs have long been treasured more than outcomes. Academic ranking services such as the annual U.S. News and World Report college rankings, have a long history of measuring academic quality — not based on student learning success, but based on a myriad of inputs to the learning process. These input measures include but are not limited to admissions selectivity, standardized admissions test scores and admissions rates, alumni donations, student-to-faculty ratio, class size and faculty credentials.

Today, learning outcomes are the currency of higher education, affording transferability of learning and courses between institutions, enabling educators to communicate what is to be learned, and supporting learners’ ability to communicate what they have learned. Learning outcomes are informed by a variety of inputs including but not limited to the educational goals of institutions and learning and performance tasks of employers. Faculty members must therefore agree on basic learning outcomes for each course, and how those course out- comes fit within the overall curriculum.

The adoption of a learning outcomes approach with the aim of identifying the right outcomes expressed at the appropriate level of rigor, revealed a great deal about the teaching and learning process at Franklin University. For example, many faculty members were well versed in subject categories and the topics they wanted to cover but not in writing measurable learning outcomes for learners. An evaluation of our syllabi across all programs and courses revealed inconsistencies in introducing, reinforcing and evaluating course outcomes through learning activities and assignments. Other concerns included unintended redundancies of course materials in some programs, hidden prerequisites, and a skill deficit among our faculty to address evolving manifestations of some rapidly evolving disciplines.

LESSON 6: The bait needs to be attractive to  the fish — not to the fisherman.

Most e-learning experiences offered at colleges are organized by faculty members in the same way as the face-to-face version of the course. They are often presented and delivered within the same parameters and schedule, and are evaluated using the same student satisfaction methods. Technology- enhanced curriculum provides opportunities to truly rethink how education is delivered. A clear understanding of learner needs, learning requirements, and of the potential ways learners and educators interact with learning technologies factor heavily in the success of technology-enhanced learning experiences. Some scholars suggest that success in digital learning is more likely if students serve as learning designers and engage in formative evaluation activities; i.e. an evaluation that takes place by the students before learning projects occur, with the aim of improving the project’s design and performance. This approach is quite different from a traditional lecture method where faculty members maintain total control of instruction.

To make education meaningful to the learner, the process of selecting instructional materials is also important. This process is often unmanaged at HEIs, with faculty members teaching each section of a course — often offering different resources to students at different price points. The instructional materials, technologies and virtual learning materials should: be accessible to and used by students; support learning outcomes; and contribute to student success. The selection of appropriate educational technologies is essential to successful teaching and learning. They should match the requirements of learning tasks, and be accessible and easy to use by students. Student surveys can be a place where data is collected on students’ reactions to instructional materials. Increasingly, instructional materials generate their own data, informing faculty about the use and effectiveness of the material in contributing to student learning. Another important data collection consideration is the cost of instructional material relative to other options and relative to perceived instructional benefit.

LESSON 7: “Every line is the perfect length if you do not measure it.” —Marty Rubin

There has always been a way to examine the effectiveness of courses. Prior to the introduction of e-learning methods, faculty members at Franklin University measured student attendance and retention, course completion, and student grades. However, today the capabilities of our learning management system (LMS) allow faculty members to examine student behaviors in relation to their academic achievement. Faculty members can now examine data related to the time students accessed course information, whether they watched all or part of an assigned video, answered questions correctly, responded to and posted to discussion boards, clicked on a lecture, or opened up an email with assignment instructions. The interrelationship between student actions and student success measures (such as course grades or nationally normed exams) allows us to uncover patterns and formulate predictions. Where data is informing student support practices, interventions for supporting students outside of the classroom and within virtual environments are evolving rapidly.

LESSON 8: “Before anything else, preparation is the key to success.” —Alexander Graham Bell

Faculty members are not born with an innate knowledge of how to teach or how to assess student learning. To ensure widespread understanding, we offered faculty members training and development opportunities designed to build a level of comfort and familiarity with e-learning and the use of learning analytics. Training and development allows faculty members to practice the range of teaching and learning methods. The workshops lead to conversations about assessment, encourage faculty to use the language of assessment, and help them gain competence and confidence as teachers using a variety of instructional approaches. A number of historic measures remain important. These include student surveys of faculty and faculty observations based on teaching effectiveness rubrics. In addition, modern LMS technology allows an analysis of faculty behaviors and engagement with the course and with students. The results of measuring teaching effectiveness allows professional development planning and other HR decisions. 

LESSON 9: “What gets measured, gets managed.”  —Peter Drucker

In many organizations, after the effort to gather and make sense of data, it can be summarized and placed on bookshelves to collect dust. Data collected should be used to make improvements to the course outcomes, instructional methods and ma- terials, or the assessment methods used. Figure 2 illustrates this relationship. In the end, data should inform improvements to student learning. Based on review of the data, our faculty members have achieved a number of the following goals:

ELM march The Evolution 2

>>   Redesign of the entire program’s curriculum to better fit market requirements and to avoid irrelevancy;

>>   Inform hiring plans for additional faculty;

>>   Target improvement of certain student learning outcomes for transferability;

>>   Change assignment requirements, supporting materials, and grading criteria;

>>   Change student feedback and faculty development practices;

>>   Change outcome assessment criteria; and

>>   Add learner support services.

Based on student and faculty feedback on courses, university-wide decisions have led to an increase in the perceived value and attractiveness of courses and programs.

LESSON 10: Our future is more data driven.

A number of trends suggest that the future of education will be more data driven. These trends include: (1) advances in technology; (2) looking at the softer side of learning; (3) greater interoperability of data systems; and (4) adaptive learning technologies.

First, analytical software is becoming more advanced and more broadly available. LMS software is becoming more advanced as designers of the software respond to the increasingly sophisticated user by adding new features. Hardware and learning software are also becoming less expensive and more powerful in terms of their computing capabilities.

Second, while economic measuressuch as enrollment and retention, course completion and grades were early indicators as dependent variables, increasingly, faculty are looking at the softer side of learning. These measures include student well-being, their active engagement, and the perceived relevance of the curriculum as it relates to their personal and career aspirations that are believed to be related to their success later in life.

Third, there is a movement toward greater interoperability of data systems. Currently, data silos exist both within and across organizations. As we begin to see the relationship between data sets as predictors of student success, this will drive efforts to have these systems talk to one another. For example, as many community college students enter four-year colleges prior to graduation from their two-year associate programs, data on learning outcomes met or courses taken may be sent from the four-year college back to the community college. This “reverse transfer” may signal the awarding of the two-year degree from the community college, which would positively affect their graduation rates and financial allocations from the community. Another example includes tying faculty development data to student success data. These data sources often reside in different places. Yet, it is believed that good teaching contributes to student learning. Systems that connect these two data sets would more effectively answer questions about the relationship between teaching and learning.

Lastly, adaptive learning is an educational method that uses computers as interactive teaching devices to direct learning tasks and paths based on the users’ competence and their unique needs. Adaptive learning is a form of machine learning that tailors educational experiences based on their responses. These methods produce both activity data and outcome data. As prerequisite knowledge and learning pathways continue to become clearer, adaptive methods will become more effective at individualizing learning.

CONCLUSIONS

The data generated by learning technologies such as content repositories, digital learning materials and interactive media objects are magnifying the view into how teachers teach and how students learn. The data also illuminate the environmental conditions under which learning occurs. Decisions informed by learning analytics can influence a culture of assessment and continuous improvement. We have, by no means, perfected our analysis and understanding of quality teaching and student success in higher education. Fortunately, the process of treating teaching and learning as a subject that can be analytically understood is moving forward, nudged by technology and human curiosity. With all this said, individuals and organizations need to constantly consider and develop new measures, new algorithms, and new social processes that enhance our ability to make data informed improvements.

—Dr. Washington is Senior Vice President for Academics at Franklin University. He opened the International Institute of Innovative Instruction, a collective body of learning scientists from across the globe the work to create and teach dynamic and innovative courses. He received the 2016 Learning! Champion Award for exceptional contributions to the learning industry.

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Artificial Intelligence, Machine Learning, Intelligence Systems. These applications are transforming business, and the enterprise technology an platforms to support them. By Catherine Upton

The digital evolution is changing how business is done. This is the era of impassioned CEOs and technology leaders with creative ideas who can inspire their organizations and lead them in transforming into digital businesses.

"The learning ecosystem is going through a technical disruption to automation and autonomous learning programs in the corporate space. Reminiscent of the shift from contact management software to sales force automation software or email marketing to marketing automation, the learning stack is the laggard to be re-invented and adopted, says Rory Cameron, General Manager, Litmos by Callidus Cloud.

In a Gartner report titled, “Top 10 Strategic Technology Trends” authored by David W. Cearley, Brian Burke and Mike J. Walker, there are three macro trends leaders must embrace to enable a shift to the digital enterprise.

MACRO TREND 1:ALGORITHMIC BUSINESS  DRIVES TRANSFORMATION

Algorithmic business is an accelerator and extension of digital business, according to Gartner. It focuses on how increasingly intelligent algorithms enable smart machines and systems to become autonomous actors in the digital business as agents for human beings. Algorithms drive the connectedness among people, things, businesses and information that drive business value. Algorithms provide the “intelligence” to get the most out of the connections and interplay between people, things, processes and information. Algorithms also are critical to delivering a differentiated customer experience. Although big data remains a major concern for CEOs, big data generated as part of the digital business process is of no value in itself. It is only when the organization shifts from a focus on big data to “big answers” that value begins to emerge.

"Forward-thinking learning profes- sionals and learning technology providers have long recognized that we are amassing a significant amount of data on learners, reports Chip Ramsey, CEO, Intellum. “From the corporate perspective, the enterprise should already be drilling down to the individual employee to determine which learning asset positively altered which specific outcome. On the learning technology side, we should be leveraging the tremendous amount of anonymous user data within our reach to identify learning trends that impact performance. But these are still ‘fixed’ approaches by which learning technology providers, and our clients, are making decisions."

Analyzing big data to identify patterns and insights that drive business actions is the start of this shift, according to Gartner. Algorithmic business transformation occurs when organizations encapsulate these insights into algorithms tied tightly to real-time business processes and decision-makers, and when they use machine learning to allow increasingly autonomous algorithmic action. Algorithms are more essential to the business than data alone. Algorithms define action.

Algorithmic business extends beyond data and analytics to influence the evolution of applications, business models and future digital business solutions. This is ushering in a post-app era in which system and application vendors such as Microsoft, Google and Apple are likely to deliver platforms and applications with ever-more powerful agent- based interfaces.

Intellum’s Ramsey continues: “As business sectors across the board, including learning, continue to apply machine learning techniques, these traditionally fixed algorithmic approaches are themselves learning. At Intellum, we are already testing a solution that presents the exact information the user needs to consume at the moment in which that presentation has the highest likelihood of improving that employee’s performance. The algorithms that control this approach are not static equations but processes that learn from large numbers of prior successful outcomes to better determine who needs what, when.”

Algorithmic business builds on digital business, shifting the emphasis to the intelligence encoded in software, according to Gartner. Enterprise architects must add algorithmic business and related enabling technologies to their planning and future enterprise, data, security and application architectures.

IBM’s acquisition of The Weather Company is an example of algorithmic business. The Weather Company has a massive Internet of Things (IoT) implementation, with hundreds of thousands of weather sensors sending 28 billion transactions to its Cloud every day. Before the acquisition, IBM had an agreement to feed data to IBM Watson for weather prediction. With the acquisition, IBM brings together The Weather Company’s digital environment and associated data with IBM’s analytical and cognitive computing capabilities. This has created an algorithmic business that provides analytical services and results to a business ecosystem with more than 5,000 customers. These customers — in, for example, airlines, insurance companies and retailers — can use the algorithmic input to drive their own business operations.

Organizations must examine the potential impact of these macro trends, factor them into their strategic planning for 2017 and 2018, and adjust business models and operations appropriately. If they fail to do so, they will risk losing competitive advantage to organizations that do. {See Figure 1}

ELM March Disruptions 1

Ramsey concludes: “The algorithm that learns how to present the right information to the right person at the right time is beyond valuable. It will fundamentally transform the company that learns to harness it. Imagine the competitive advantage gained when the learning solution recognizes in real time an opportunity to intercede and present the user with information (a new sales technique) that turns an otherwise negative outcome (lost sale) into a positive one (closed sale). This is not an imagined future state. Companies like Intellum will be providing this competitive advantage to clients within the year.”

MACRO TREND 2:THE EMERGENCE OF THE DIGITAL MESH

Gartner defines the “economics of connections” as the creation of value through increased density of interactions among business, people and things. As an organization increases the density of its connections (among people, business and things), it increases the potential value it can realize from those connections.

Connections are at the core of digital and algorithmic business models. The digital mesh builds on the economics of connections, focusing on devices, services, applications and information. The digital mesh is a people-centered theme that refers to the collection of devices (including things), information, apps, services, businesses and other people that exist around the individual. As the mesh evolves, all devices, computers, information resources, businesses and individuals will be interconnected. The interconnections are dynamic and flexible, changing over time. Building business solutions and user experiences (UXs) for the digital mesh — while addressing the challenges they create — must be a priority for enterprise architects.

“This concept of a digital mesh that is made up of all the devices and digital applications that are tracking every aspect of our lives is very applicable to enterprise learning," claims Ramsey. “In a corporate environment, we use applications to manage projects and relationships, receive customer feedback, and control versions of critical documents and code. We interact with these applications across a number of devices from a number of locations. The things we rely on to get our jobs done are actually gathering data about how well we do our jobs.”

The digital mesh has emerged as a re- sult of the collision of the physical and virtual worlds, as computing capability becomes embedded in virtually everything around us. Additional advances allow the virtual world to enter the real world through advanced UI and virtual reality models, as well as physical items created with 3-D printers. This blending of both worlds delivers new insights into the physical world, allowing us to understand it in greater detail, and interact with it in new and intelligent ways. This will change how people experience the world in their daily lives. Opportunities for new business and operating models will abound.

Ramsey adds: “At Intellum, we can already mine this data from a range of devices (think Fitbit) and applications (think Salesforce) to determine employee performance levels. We can now experiment with how well specific inputs, like a mid-day walk or a two-minute video on how to become more persuasive, can alter an outcome or improve an employee’s performance. Once these feedback loops are in place, particularly at scale, we can apply the algorithms that will determine the exact learning asset an employee should encounter in a specific scenario. This will, of course, require even more data from even more sources, and the digital mesh will continue to grow.”

MACRO TREND 3:SMART MACHINES SET THE STAGE FOR ALGORITHMIC BUSINESS AND THE ALGORITHMIC ECONOMY

The smart machines theme describes how information of everything is developing to extract greater meaning from a rapidly expanding set of sources, reports Gartner. Advanced data analysis technologies and approaches are evolving to create physical and software-based machines that are programmed to learn and adapt, rather than programmed only for a finite set of prescribed actions.

The amount of big data collected by the many devices currently in place is staggering. However, the accelerating merger of the physical and virtual worlds will make the present volumes seem paltry. New kinds of data will continuously stream from new types of devices at record rates. This oversupply will overwhelm those who are ill-prepared. But for those who are prepared, the potential to gain new kinds of critical intelligence will be unprecedented. Leading senior executives will build a strong competency in turning this data into critical intelligence that will drive their organizations’ future direction. Additionally, leading organizations will significantly advance operational agility with near-real-time information, feeding business processes that can absorb it and react accordingly. Data coming from almost all directions provides the possibility for intelligence everywhere when combined with advanced artificial intelligence algorithms and other machine-learning techniques.

Three distinct trends are intimately linked in the smart machines theme. They represent an evolution in how systems deal with data, and the machines and people that create and consume this data, culminating in intelligence everywhere. {See Figure 2}

ELM March Disruptions 2

“These three macro trends are substantiated by what we have seen in the financial trading arena," says Apratim Purakayastha, CTO, Skillsoft. “For some years, sophisticated algorithms have taken over trading decisions. Those algorithms are connected in a mesh, taking decisions and automatically trading across firms — and those ‘smart machines’ — have set the stage for a mostly automated algorithmic business. There are other areas, such as supply chain management, where this trend is currently growing. In the area of digi-tal advertisement, we can also see this trend dominating. Overall, it is already a broad, cross-industry phenomenon.

Even everyday objects such as a stethoscope and enterprise software such as CRM systems or security tools increasingly have a smart and autonomous aspect. In “Top 10 Strategic Technology Trends: Autonomous Agents and Things,” Gartner looked at how information of everything and advanced machine-learning algorithms, supported by advanced system architectures, are leading to more intelligent software and hardware-based solutions. These are creating new market segments and enhancing existing ones.

“The pervasive nature of these trends demands that everyone understand what comprises a 100 percent digital workforce — a workforce that is fully trained and conversant with fundamental digital skills, along with its benefits and risks,” adds Purakayastha.

The key digital skills sets required include but are not limited to:

>> Broad digital skills such as productivity and collaborative tools.

>> Modern technological trends such as Big Data, Blockchain, etc.

>> A thorough understanding of fundamental cybersecurity issues such as phishing, ransomware and other risks

>> Best practices and laws relative to digital compliance and data privacy

>> Digital “presence, leadership and image in a virtually interconnected workforce.

—This article contains excerpts from the Gartner Research Report titled “Top 10 Strategic Technology Trends” by David W. Cearley, Brian Burke, Mike J. Walker. To access the complimentary Gartner report, download it at: http://gartnerevents.com/ Top_10_Strategic_EMEA?ls=ppcggle&gclid =CJiMlrSN184CFVAo0wodWdQNkQ

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Empowering Employees to Take Charge of their Development - By Ritu Hudson

At Navy Federal Credit Union, we frequently receive these questions in learning and development. You probably do too. People look to us, the training department, to support their development. But most team members aren't aware of all the training department offers, or even where they should start. Enter Pathfinder at the Navy Federal Credit Union.

Pathfinder is a tool that provides employees awareness of the variety of resources that Learning & Development offers. It makes development planning easier by providing resources based on a career path or competency. It facilitates developmental conversations between leaders and staff by providing a common language. Overall, the tool provides the resources for our employees to own their development and their future.

To assure success, we created a process to effectively develop and launch the solution. We relied on a process that is familiar to learning and development professionals: Analysis, Design, Development, Implementation, Evaluation (ADDIE). Our approach included:

>> Obtaining upper leadership buyin;

>> Spending time up front to complete a needs analysis, organizing the content, and planning the project;

>> Determining whether to develop inhouse or find a vendor;

>> Utilizing a phased design-and-development approach to minimize the need for rushing to completion;

>> Launching the Pathfinder tool and creating awareness around it through branding and marketing; and

>> Continuously gathering feedback, revising, and reinventing the tool.

CHALLENGES AND NEEDS

Before creating the solution, we went  through a thorough discovery process that included talking to employees and identifying needs. We discovered three main challenges:

  1. Employees had difficulty identifying what skills they needed for specific positions. They wanted to know, "What do I need to do to become a ____?' They also wanted a "path" created for them to achieve the necessary skills and experiences to prepare for that role.
  2. Despite developing a process, a work- sheet template, and even a workshop to help employees create their competency-based individual development plans (IDPs), they were not being used as widely across the organization. Our IDP pro-cess stressed that development is driven by the employee and that the employee should take the initiative to meet with his or her leader on a regular basis to discuss progress. While employees and leaders were open to having these conversations, there was confusion regarding what developmental activities could go in the IDP, especially around the organization-wide competency framework.
  3. Many employees were not taking charge of their own development and waited until their leaders initiated a developmental conversation.

 

To overcome these challenges, we needed to:

>> Support employees by guiding their learning along career paths. We were consistently hearing, "How do I become a business analyst?" or "How do I become a project manager?" We needed to guide, not prescribe, learning resources based on career paths.

>> Encourage the use of IDPs across the organization. Leaders and employees had the resources needed to create their plans, and the suggested developmental activities associated to competencies.

>> Encourage employees to self-initiate their development by giving them the resources to do so.

Based on the identified challenges and associated needs, we determined that the overall goal was to improve employee performance and engagement by empowering our employees to take charge of their development. This goal directly aligned with the organization's strategic plan, which included an initiative to "…have highly skilled, engaged team members empowered to execute our strategy." With this alignment, we were able to gain visibility for this project, obtain an executive level champion, and also make it a priority for our team.

 DESIGN & DEVELOPMENT

Armed with the organization's needs and strategic plan, we were ready to begin development. We decided to develop the tool in- house instead of using a vendor. This allowed us to keep the tool current as we developed new learning resources. As with any design project, we went through multiple iterations to get it to where it is today.

Before beginning development, we reorganized our learning resources to help our employees understand the developmental categories involved. We created eight developmental tracks:

>> Career Development

>> Communication

>> Financial Management

>> Functional/Technical

>> Leadership

>> Management

>> Member Experience

>> Self Enrichment

Our employees would be able to more easily identify developmental resources, such as workshops and e-learning courses. In an effort to identify guided paths for employees developing for a specific role, we organized our learning resources into career paths. Despite having hundreds of positions across the organization, we utilized 10 areas of subject-matter expertise:

>> Administrative Assistants

>> Business Analysts

>> Executives

>> HR Professionals

>> IT Specialists

>> Loan Officers

>> Managers

>> Project Managers

>> Supervisors

>> Training Specialists

Last, we created an "All Employees" path for general employee development. Now, we were ready to build the tool.

Iteration 1

The first iteration of the tool was an interactive Adobe Acrobat PDF document. It allowed users to click on a Career Path at the top of the document, which highlighted the courses applicable to development for that path. This version of the tool was easy to send over email, but it was limited by scope and physical space. It only included selected learning resources, and no information beyond the resource's title was available.

ELM March Empowering Employees 1

Iterations 2 & 3

After deploying the first version of the tool, we saw what worked and didn't work for our audience. The second iteration produced a standalone, wizard-style tool. This tool was hosted on the organization's intranet, making it easily accessible to employees. The focus of this version was to enable our learners to pick the type of development that they needed.

The second version allowed us to take a more holistic approach. We added additional career paths and learning resources- e-learning courses, workshops (physical and virtual classroom), career development advice, and competencies. Furthermore, the tool allowed the resources to be organized in a manner that effectively provided learners with the ability to obtain learning to develop specific competency and to develop in a current or future position.

With Iteration 2's focus on functionality, we were able to fine-tune the tool in Iteration 3. We added additional paths and fully integrated the tool into our intranet. Instead of a link, it was now embedded within the site, allowing users to leverage the intranet's search functionality.

ELM March Empowering Employees 2

ELM March Empowering Employees 3

IMPLEMENTATION

Throughout the development periods, we worked diligently to market the tool across the organization. We created a logo and tagline for the tool, and used it everywhere. We aligned the tool with our annual Catalog of Services (outlining our offerings, categorized into the same development tracks) and integrated the tool into our workshops, including our New Employee Orientation. We went on road shows and demonstrated the tools at various business unit meetings. We sent targeted emails and advertised it on the intranet. We even created 3-D posters advertising Pathfinder and posted them everywhere. We communicated to employees that we listened, developed a tool to support them, and simplified the "how to" of development.

EVALUATION & IMPACT

Between our marketing and word-of- mouth, the tool became an integral part of employee development within our organization. We received positive feedback that the tool was user-friendly, accessible and interactive. Employees and leaders began using the tool in the development of IDPs. Pathfinder reinforced the competency language/framework that we utilize throughout our organization in behavioral interviews and annual performance reviews, and it further provided a common language for our employees and leaders to have developmental and performance conversations.

We continue to review and modify Pathfinder on an annual basis. Based on learner input, we have continued to add career paths. We also review the tool for functionality and to improve the user experience. We have linked Pathfinder to the learning management system (LMS), providing employees with the ability to review course descriptions in Pathfinder and quickly link directly to our LMS to open the e-learning course or register for the workshop.

Not only did Pathfinder support a more developmentally-focused culture and provide awareness of our department's offerings, it was a steppingstone to new and different employee-initiated development programs. We recently linked Pathfinder's Career Development section to an extensive job shadowing program in which employees make requests to shadow positions in other business units. We have also implemented self-paced certificate programs that put the learning in the hands of our employees. They register for and work through a curriculum of workshops and e-learning courses to obtain the certificate, some of which are based on development tracks. Further, when we get a development inquiry, we introduce them to a tool and other self- initiated programs that puts their devel- opment in their hands.

The Navy Federal Credit Union is a five-time Learning! 100 Award winner, recognized for innovation and high performance.

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By 2025, 46 percent of the workforce will be Millennials.

According to a report from the National Chamber Foundation, Millennials expect close relationships and frequent feedback from management, viewing their managers as coaches or mentors. Their managers — rather than the corporations themselves — can earn the loyalty of Millennial employees by keeping their word. Management can reduce the risk of Millennial employees leaving a company by maintaining a positive relationship with them. Findings indicate that the main reason that this age group leaves a company is directly related to a superior.

At Express, the future is about those Millennials. “We structure our learning and development for them,” says Adam Zaller, Vice President of Organizational Development, Express. “The average age at Express is 27, and at the retail stores it is middle to low 20s.”

Realizing this, Express identified an opportunity to evolve its talent management strategy for its primarily Millennial-aged employees while becoming a fashion authority for both men and women.

According to Zaller, “[Millennials] are always connected, multi-taskers who are very socially aware. They have more friends ... two-and-a-half times more than Boomers. Because of this, they are influenced by their peers; they seek status among the peer group; they tend to ‘crave experiences.’ In our development programs, we focus more on the experiences and activity and less on the classroom or the course.”

To support this culture, Express’s organizational development team created an intuitive, irresistible, social and mobile learning experience for its more than 22,000 mostly-Millennial employees. The program has pushed limits and established an engaged employee population that’s driven customer experience scores and internal engagement scores to their highest levels while decreasing turnover to its lowest rate ever during the three years that it’s been implemented.

“It’s Uber personalization and individualization,” continues Zaller. “It’s not one size fits all. Simplicity is king, and experience and activities are paramount to actual courses. And most importantly, it’s all about smartphones.”

How does this translate into learning and development? Millennials wants more communication. “Everyone has that one thing they are phenomenal at … provide them a talent management framework so they can socialize that,” suggests Zaller.

THE EXPRESS TALENT DEVELOPMENT PLAN

At Express, all training programs are designed to organizational competencies. “Over time, people can use the competencies to measure against and grow their career at Express,” shares Zaller. “It’s by [job] layer and area of focus. You can see at the contributor, manager or director level, what’s appropriate at that role, the manager above you, so you can formulate a career development program just from our competencies.”

PERSONALIZING LEARNING

Express’s talent program starts with an individual’s personal aspirational vision of what he or she wants to do with his or her career. They look at courses and classes, articles and books to gain some knowledge from; then the experiences follow. “It really starts at how we create a meaningful experience for you, so you can grow your career,” says Zaller. “It’s really important to provide Millennials the space to share what they are really great at in these collaborative spaces. They can connect and see what everyone else is doing, or share ideas that they have.”

Communication is key to the Millennials and Express took “a riff ” off of what millennials use to communicate today. Millennials use a range of social mediums and the learning experience needs to reflect this; Instagram, Twitter, Snapchat, Pinterest and Periscope. “

What we love most is that our environment looks like Facebook meets Twitter meets learning site,” adds Zaller. “You can’t tell where there are classes or courses, or where there’s an activity stream where someone is saying this is a great article, or have you considered this idea. It all molds together to create a curated experience for somebody.”

The learning platform, supplied by Saba, enables team members to find their own online development in bite-sized chunks that appeal to them. By switching to a user-driven learning platform, Express supports blended learning at a personalized level: providing each employee with personal, relevant recommendations of classes, content and expert connections that help each succeed at his or her job.

The new learning ecosystem enables individuals to opt-in and access learning in areas of interest, resisting a one-sizefits-all approach. The system provides real-time recommendations, builds personal networks, promotes social collaboration, and provides direction for each of the more than 22,000 associates at Express. Prescriptive analytics provide each employee with personal, relevant recommendations of classes, content and expert connections that help them succeed at their job.

“Whether you are walking down the hall, at your desk or in a store, you’ll have the same experience with learning,” reports Zaller. “You have bits and bytes of learning and communications based on your courses, articles, or activities of interest … over 20,000 people adding to the site on a daily basis.”

LEADERSHIP DEVELOPMENT AT EXPRESS

The Express Essentials for organizational competencies describe the leadership skill set needed at a specific level in the company. They are cataloged to focus on key behaviors. Outlined as a map, the competencies are shown at each level and how they build upon each other in each area of focus. The maps help employees create individualized development plans and evaluate the competencies needed to further grow in each level of the company. The competencies keep employees on track with their goals every day, and management integrates them into the mid-year and annual review process.

In order to develop the best leaders in the retail industry who create an engaging environment consistent with the brand’s values, Express focuses on a few core programs at each level that drive leadership behaviors. As part of its talent management strategy, Express wants to drive employee self-development through the creation of a personalized and meaningful experience. Using data and analytics is an essential asset to shape the talent management experiences and to provide the best results for evaluation.

There are five key talent priorities that support Express’ leadership initiatives:

>> Increase the importance of engagement through communication.

>> Encourage employees to socialize their native genius to grow the company’s overall knowledge.

>> Encourage personalization and individualization.

>> Leverage knowledge nuggets instead of large traditional courses.

>> Implement a modern, easy-to-use talent management platform which leverages experiences and activities to drive knowledge.

BUSINESS IMPACT

The program is doing well, based on the results the organizational development team tracks. Since the program’s implementation in 2013, Express has been able to spend less on development while experiencing the following positive results:

>> Reducing employee turnover by 14 percent year-over-year.

>> A 100 percent improvement in associate engagement scores.

>> An increased Net Promoter Score by more than 80 percent.

>> The ability to spot potential employees with high potential. (Half of all field district managers are alumni of Express’s high-potential program.)

WHAT’S NEXT

With its loyalty program being titled ExpressNext, the company is always looking toward the future. Zaller shares they are planning to invite people to post their own videos, create quick knowledge nuggets and expand their leadership programs.

—Sources: “The Millennial Generation: Research Review,” National Chamber Foundation, https://www.uschamberfoundation.org/sites/default/files/article/foundation/MillennialGeneration.pdf

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