Hear From the Experts on Technology and Educational Design

DR. GLICK: Good afternoon, Dominik. Thank you so much for making time for this interview. Self-regulated learning (SRL) is an overarching term that addresses how students approach their learning, work toward goals, and evaluate their performance. The topic of self-regulated learning intertwines cognitive strategies, metacognitive strategies, and motivational beliefs. Ultimately, students who practice self-regulated learning can improve their academic performance, find value in their own learning process, and continue to be effective learners once they enter the workforce. Research has shown that students who actively engage in learning activities achieve more than students who simply listen to instruction. However, online students often struggle to self-regulate, which may contribute to low academic performance of certain student populations.

With this in mind, which design principles may prove effective in supporting students’ self-regulated learning in online courses (e.g., spacing, reflection, time management, help seeking, effort regulation, checking for comprehension, etc.)?

DR. RUS: There are several evidence-based principles and techniques instructional designers should adopt in every learning experience. I always say that a good learning experience that leads to a good outcome starts with a good learning (experience) design and it seems that we never spend enough time on the design or put enough care into it. There is so much scientific evidence from various learning sciences such as cognitive psychology or cognitive neuroscience about how humans learn (or don’t learn, for that matter), and some have been around for decades (e.g., the famous distribution effect, various cognitive biases, the spacing effect, etc.). Unfortunately, many learning experience/instructional designers still don’t know about them, or even if they do, these principles aren’t employed due to the “default design” that schools, universities, and training departments adopt (e.g., there is a structured curriculum where students move from one topic to another with no review and no refresh of the past knowledge, which quickly gets forgotten). Furthermore, some design principles are also much harder to design and deploy. While it may be easy to use techniques like elaboration, reflection, or gamification, some other techniques (e.g., interleaving, distributed learning, or spaced repetition) generally require modern AI-based technology. While there are many specific evidence-based techniques out there, I like to talk about my "5 fundamental principles of good learning design.” These are: participant-centered learning experience, collaborative learning experience (learners as prosumers rather than just consumers), gamification, distributed learning, spaced practice (via active retrieval). I believe that every single learning experience (no matter the modality, but especially in an all-virtual environment) should use these five elements to boost engagement, retention, and should consequently, improve application and transfer. One can, of course, expand each of these elements and think of very specific learning methods, tasks, or assets associated with them.

DR. GLICK: In the preface to my book “Early Warning Systems and Targeted Interventions for Student Success in Online Courses”, I note that concomitant with the boom in online learning are escalating concerns about academic accountability, specifically with respect to student outcomes as measured by persistence (i.e., retention) and success (i.e., final course grade). These concerns emerge from research indicating that attrition rates in online courses are significantly higher than in face-to-face courses. This finding is very problematic, as lower retention rates among online students have been connected to overall lack of academic success in higher education. Specifically, a study by Xu and Jaggars suggests that success in online courses is critical, particularly in view of evidence suggesting that withdrawal or failure in online learning early in a student’s college career may impede progression towards graduation.

How can instructional designers and learning scientists harness the power of AI and learning analytics to improve student retention and engagement in online courses?

DR. RUS: Think of a brick-and-mortar school, corporate training, or executive education classroom for a minute. While the learning design—and by extension, the learning experience and engagement—in these settings might not have been the best in pre-pandemic times (we’ve all seen and experienced “death by PowerPoint” and the teachers’ long-winded monologues, lacking any kind of collaboration, reflection, and elaboration), the facilitators would still have the feeling that they somehow had control over the learners and could track the students’ participation, emotional reactions, and by scaffolding the lessons, even levels of knowledge retention. I believe most of this was illusionary and many of the teachers’ insights were simply intuitions, plagued with biases, they were plain wrong, or they were simply impossible to capture. For example, as a teacher, there is no way I could remember who participated how many times during my last class, which students reflected upon the questions when I asked them to do so, which learning partners answered the questions verbally when they were asked to do so, who looked at the white board when I was writing down some important definitions, or who seemed to be more engaged during a specific activity. We can track all this and more with powerful AI-based systems now and teachers, facilitators, designers and other learning professionals should welcome such innovation for they can now focus on the aspect where humans are (still) superior to machines (e.g., motivating through powerful stories, offering feedback, mentoring and coaching, etc.) and AI can sieve through millions of data points that can inform both learning design as well as delivery.

In my organization, for example, we are currently testing a tool for synchronous instructor-led virtual training that embeds into a common videoconferencing and collaboration platform where we can gather data points such as who joined the session and when, who was late or absent, who was actually looking at the application (rather than some other website), who raised their hand (and how many times), who used the chat option (and how many times), who was more or less engaged with the
content, etc.

LMSs/LXPs are also becoming more and more powerful in terms of learning analytics and are harnessing xAPi data points rather than just traditional SCORM-based ones increasingly more, so we no longer see only who completed a specific learning session, when, and where, but can see the learners’ actual interaction within the learning assets (e.g., where learners clicked, what they liked or shared, how long the stayed on a specific screen, or—even more granularly—a specific learning object, whether they saw the entire video or not, or whether they answered the questions embedded in the interactive video or not, etc.). Such data can—and should—inform learning designers who need to start wearing the hats of engineers, user experience designers, and data scientists, performing AB testing with regard to different techniques, methods, or assets, gathering data continually, and designing learning experiences through iterations based on such data, so that learning experiences lead to improved engagement
and retention.

DR. GLICK: A growing number of education companies are taking action to advance equity, inclusion and diversity efforts in the education experiences they provide to learners and educators. An important part of this task is to structure programed for students with disabilities, difficulties, and disadvantages in a way that respects and protects these groups’ rights. Brett Christie, Director of Learning Experiences Design, notes that “…equity and inclusion are often seen as something to be considered outside of the learning experience. For truly effective learning, equity and inclusion need to be considered within the entire learning experience and how it’s designed, every step of the way.” Simply put, equity, inclusion and diversity mean that the outcomes of our teaching are fair and just regardless of the differences – socioemotional, socioeconomic, cognitive or physical – the students bring to the classroom.

What are the implications of all this for instructional design?

DR. RUS: Like you said, there are different types of disabilities, hence one should not generalize in terms of learning design for the learners with disabilities. Generally, though, online instruction tends to be more visual and auditory, and more interactive, and as such, better suited for learners with disabilities than traditional face-to-face instruction. In terms of physical disabilities, good practice to follow is to offer both audio and video in every online learning experience (ideally one or the other could be turned off by the learner). In terms of cognitive disabilities, I’d mention the misuse of learning content that causes cognitive load (which is a common malpractice even in the case of learners without cognitive disabilities!), either too much text or too much video. Video, for example, has become the ubiquitous learning asset that is found in almost every face-to-face and online learning interaction, but is often used wrongly. It is great for episodic memory (e.g., for teaching a single concept or perhaps a process with a few steps through storytelling), for example, but fails miserably when used for semantic memory (e.g., teaching many facts about a certain topic or teaching processes with several steps where memorization is required)—no matter how professionally it is done and how attractive it looks. With respect to cognitive limitations, I’d also mention the importance to adapt timing-sensitive learning events such as timed assessments or other timed activities. There are other equity-related issues to consider here that are much more common than physical and cognitive limitations, and that is sheer exposure to technology, which generally goes hand in hand with the socioeconomic status and age. For example, during the pandemic it became apparent that many learners, especially economically disadvantaged kids in schools, including in the developed world, had a hard time following online instruction, be it synchronous or self-paced. Some were not tech savvy and didn’t know how to use the computers well, some were not used to self-paced learning using LMS, some struggled with videoconferencing tools, etc. As such, it is critical that every learning design and delivery be as explicit and unambiguous as possible. For example, an e-learning course must state explicitly how to use the navigation bar, a virtual space in an LMS must state clearly where on the page a certain learning asset is attached, learners need to be trained on how to use a particular videoconferencing and collaboration tool (such as Teams, Zoom, or Slack) through explicit instruction, etc.

DR. GLICK: Career paths and workforce needs are changing. Due to workforce and economic shifts, programs are required to help learners develop employability skills and arm them with the skills they need to be competitive in the quickly evolving workplace. As noted by Chris Dede and John Richards in The 60-Year Curriculum: New Models for Lifelong Learning in the Digital Economy, disruptive shifts in higher education and in working lives require a revolution in educational objectives. Students and workers expect that their current job or careers will, at some point, disappear or evolve, forcing them to prepare for novel jobs in several new careers at unpredictable points throughout their lives.

What are the implications of all this for the future of higher education and workplace training?

DR. RUS: We know that the rise of digital technologies, complexity, and ambiguity bring a set of challenges that we must address in our educational and workplace training programs and everyone seems to be talking about upskilling and reskilling now. However, I am seeing some trends that have flooded schools and corporate training that might not be the best strategies. First, everyone seems to be focusing on digital literacy only. True, as technologies continue to evolve, the need for those with the digital skills also increases. But not everyone is cut to study AI, machine learning, or data science and not every education intervention merits a STEM-based curriculum. While we can (and probably should) incorporate more subjects like AI and data science into our elementary and high school curricula, we can’t just massively reskill adults working as store cashiers, hotel receptionists, waiters, or pilots who have been made redundant (due to the pandemic) and make them suddenly code or become data scientists. Reskilling is long, messy, and generally expensive because development is long, messy, and often expensive. However, when it is strategic and well executed, it pays off. I believe these initiatives should mirror those found in career paths and succession planning in an organization where you know who needs to upskill, right-skill, and/or reskill in the next 2-3 years (also how, and why, and how much it would cost).

Second, many schools and corporate training organizations are forgetting that together with digital literacy, the critical skills that are needed in the workplace and will be needed increasingly more in the future are the ability to adapt to change, cognitive intelligence (e.g., problem solving, critical thinking, cognitive flexibility), and social intelligence (empathy, influence, and collaboration) and the schools and organizations are neither training for nor encouraging or evaluating the application of these. For example, many L&D organizations are still stuck in the 90s with respect to the skills found in their performance management systems and the L&D offering. Most of the skills in employee evaluations and in training programs are generic, often cookie-cutter courses like time management, prioritization, communication, leadership, and the like instead of the above-mentioned fine-grained skills. What is more, many organizations still believe that training is the only answer to upskilling and reskilling, while we know from research and practice, that it is the special assignments, projects, inter- and intra-company mobility, fast tracks, mentoring, coaching and other development programs that bring about skills improvements and I believe both higher education and workplace training should take advantage of these initiatives much more than they do.

DR. GLICK: Dominik, thank you so much for sharing your valuable insights!

Danny Glick is a Research Affiliate at the University of California, Irvine’s Online Learning Research Center where he explores ways to improve student persistence and performance in online courses using early warning systems and light-touch interventions. He is a former visiting scholar at the University of California, Irvine’s School of Education where he investigated the effects of blended learning on the achievement of low-income students. Dr. Glick is also the Director of Pedagogical Implementation at Edusuft, a subsidiary of ETS, where he leads a team of EdTech implementation specialists. For the past 20 years, he has helped ministries of education and higher education institutions in 35 countries to shift from traditional instruction to online learning. Dr. Glick holds a PhD in Learning Technologies and a Master’s degree in Curriculum & Instruction, and has presented and published on topics including early warning systems, targeted interventions, student persistence, and
learning design.

Dominik is the Global Head of L&D Innovation and Technology at TTEC where he oversees the design and deployment of innovative learning solutions for both virtual and classroom environments for 58,000+ employees around the globe who offer exceptional customer service on behalf of 150+ clients across multiple industries.

Besides an L&D executive with almost 15 years of experience, Dominik has also been a facilitator, learning project manager, professor, and an L&D consultant for numerous learning programs at various education centers and universities in Europe, the US, and Central America.

Prior to joining TTEC, he was the global corporate L&D Director for Copa Airlines in Panama, where he oversaw all corporate learning initiatives for over 12,000 employees in more than 30 countries, and prior to that, he was the Panama L&D Director for National Assets Recovery Services (now Radius Global Solutions), a US business process outsourcing company, where he oversaw corporate L&D in the banking, IT, automotive, and service industries.

He is an external professor of neuroleadership at INCAE Business School in Costa Rica, an external professor of Leadership and L&D at University of Louisville/Quality Leadership University in Panama, and a co-founder and current President of Latin-American Association for the Innovation in Learning and Talent Development (ALIAT).

Dominik considers himself a learning scientist—his academic background lies in neurobiological and neurocognitive approaches to learning and memory. He holds a B.A. in English Linguistics from the University of Ljubljana, Slovenia, and a Ph.D. in linguistics from Georgetown University in Washington, DC.

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