The 2017 E-learning User Study was conducted by Elearning! Media Group via an online survey of learning professionals to reveal the current trends and practices in e-learning. These findings were tabulated from 363 responses across corporate, government, education, and non-profit organizations. The study was conducted industry wide, including Elearning! Magazine subscribers. E-learning encompasses enterprise-wide learning and workplace technologies.
LEARNING DEPLOYMENT BY LOCATION TYPE
Drivers for Learning Investments
Employee engagement and improved collaboration are the top business objectives for learning investments. Personalized learning moves up to #3 in priority.
Training PrioritiesCompliance regains the lead in training priority for 2017.
LEARNING SOLUTIONS USED & PURCHASES
Learning teams use a variety of solutions and are actively sourcing new solutions. The fastest growing solutions based upon purchase intention are:
TalentQuest, a provider of Cloud-based talent management software, releases a new learning management system (LMS) that features technology obtained from Purple- frame's acquisition. Purpleframe Technologies advances in virtual reality software, interactive educational games and new type of assessments sets TQ LMS apart.
"The acquisition of Purpleframe allows us to really create the next-generation type of learning content that is very interactive, very immersive, using 3-D, using virtual reality and augmented reality."
"The acquisition of Purpleframe allows us to really create the next-generation type of learning content and content that is very interactive, very immersive, using 3-D, using virtual reality and augmented reality, says Kevin Sessions, president of TalentQuest, "We wanted to make sure that with that we had the best way to distribute that content and provide it for our clients, so hence the LMS."
-Learn more: www.talentquest.com
Jay Fulcher was named new CEO and chairman at Zenefits. Fulcher was most recently president and CEO at online video technology products and services startup Ooyala. Fulcher brings a combination of vision, operating discipline and leadership experience to Zenefits. His previous experience includes Agile Software Corp., where he served as president and CEO; PeopleSoft (executive vice president); and SAP (vice president).
Last year was a turbulent one for the tech-focused brokerage, which started with founder Parker Conrad resigning as CEO.
Amazon is using Alexa to compete against all of the other retailers on the planet and Google Home. Tesla’s A.I. downloads updated geo-intelligence to compete against all the other car brands that don’t update via the Cloud. IBM’s Watson is automating decision analysis that competes with clinics and hospitals not enabled by its cognitive computer. “This is just the beginning of the A.I. Wars,” says James Canton, futurist.
Companies that are using A.I. to compete will shape the future of A.I. There are companies using A.I. for diagnosing disease, deciphering law, designing fashion, writing films, drafting music, reading taxes or figuring out if you’re a terrorist, fraudster or threat. A.I. is everywhere.
You are exposed to A.I. in real-time if you are within sight of video camera, cell phone, driving a car, traveling by transit, in the city, online or offline claims Canton.
“Here’s a forecast—every job a human can do will be augmented by (increased intelligence assets) and possibly replaced by A.I. Companies will use A.I. to outcompete other companies. A.I. augmented humans will outcompete the Naturals—humans not augmented by A.I.,” predicts Canton.
—Source: Institute of Global Futures www.globalfuturist.com
Australia’s first HR-tech start-up accelerator program is being delivered by corporate start-up Slingshot in collaboration with talent solutions provider Hudson, online employment marketplace SEEK and The University of Technology, Sydney (UTS). Start-ups accepted into “Human Capital,” a 12-week program that kicks off in March, will receive up to $50,000 from the Slingshot Investment Fund for 10% of the equity in the business as well as training and resources, a support team of mentors and access to a co-working space.
According to Karen Lawson, CEO of Slingshot and former CEO of CareerOne, the program will help corporate leaders reinvent the human capital elements of their businesses by connecting them with disruptive start-ups, scale-ups and entrepreneurs in the “future-of-work” space.
Artificial intelligence (A.I.) is playing a bigger role in our every- day lives, but how are enterprises adopting this technology? En- ter cognitive computing and predictive analytics, and so much more. According to the National Business Research Institute, A.I.’s most important benefit is the ability to predict future tasks (38%) and automation of tasks (27%).
—Test your AI knowledge at: https://www.emarketer.com/quiz/artificial-intelligence?ecid=1014#/q/1
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
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.
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.
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.
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
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.
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:
>> 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.
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.