Exploring The Eclectic Nature Of Instructional Design

Dr. Susan Stetson-Tiligadas has been teaching at Deree ACG since 2006. She is a faculty member in their MA in Learning Design and Technology, and is the Director of Online Education at Deree. She is also involved in building and teaching courses in the MA TESOL program and cofacilitates an online faculty training course. Susan has extensive language teaching experience, having taught at Rivermont Collegiate School, Black Hawk College, and Marycrest International University in the United States. She has a PhD in Education, specializing in Instructional Design for online learning, and has been involved with the college’s introduction of blended learning and online professional development.

Can you tell us a bit more about the LDT program that Deree offers and what the curriculum involves?

Sure! The Learning Design and Technology master’s degree program at the American University of Greece is our latest, fully asynchronous online program. The mission of the school is “to add distinctive value” for learners, and that’s just what we do in this program. The curriculum ranges from the foundational skills and knowledge that learning designers need to multimedia and User Experience design, with an eye for practical application in every course. The program effectively prepares graduates for a variety of roles and sets them up for advancement in the field by also including opportunities to work with AI, skill building for learning analytics, and leadership skill building with a course on managing distance education.

I am the SME/instructor for two courses in the program: Theories and Strategies of Learning, and Learning Design and Development. In the first course, the aim is to combine developing knowledge about the main learning paradigms and other learning theories with weighing their benefits and drawbacks. We also examine how those theories have been demonstrated in the learners’ experience and what their application would look like in a chosen context. The goal is to develop an overall awareness of principled eclecticism, of how learning designers often bring together elements of different theories and approaches to a design. And that’s because learning is so multifaceted, and among the areas of context, need, learner and learning environment, resources, etc., there are a multitude of different parameters that interact, so it’s difficult to imagine that there would be “one grand theory” of learning that could support them all.

The Learning Design and Development course aims to lead learners through a hands-on design process. Using principles of project management and a client of their choosing, learners systematically work up to building a prototype, getting feedback from me and from the client along the way. It provides real-world experience for interactions with stakeholders, as they relate to creating an intervention for someone else and justifying design choices along the way.

A unique feature of the program is the ability for learners to join at any time. This means a lot of flexibility for learners who may not want to wait one or two terms to begin. From a design perspective, this is an added challenge because it means incorporating extra scaffolding and resources for those just starting out since no prerequisite knowledge is assumed. Lastly, each course in the program also has four optional live sessions throughout the term for learners to bring questions if they also want some live interaction. Recordings of those sessions are made available to everyone in the course afterward. I am, of course, wholly biased, but I think it’s a terrific program!

What do you think is the most challenging aspect of creating effective learning outcomes?

It can indeed be really challenging to come up with good learning outcomes, and since the outcomes form the backbone of a course, module, or program, learning designers rightly spend a lot of time and energy on this. It’s also worth separating the kind of learning outcomes that SMEs and designers write for themselves during the design process—the really meaty ones that almost spell out the type of assessment(s) that could be used—from the learning outcomes that actually appear in the course or training. Who knows how many learners might be scared off if they had to read through the former?

For both types of outcomes, I think the idea of “less is more” is what can be challenging. In other words, trusting that the higher you go up the ladder of Bloom’s taxonomy verbs, the more elements are already included within the verb used.

For example, a course-level learning outcome that starts out with, “Analyze the situation and differentiate A from B to evaluate which is more appropriate for use in the context and justify a particular approach to the problem,” should really be shorter: “Justify a particular approach to the problem.” It’s challenging for SMEs and for learning designers (myself included) to trust that if we unpack the verb “justify,” we can easily see that analysis, differentiation, and evaluation are all prerequisite skills (which may be included in different units or weeks of the course as enabling outcomes) leading to the ability to justify.

When I was studying Instructional Design, a professor gave me feedback on this very thing, saying that if you see the word “and” in a learning outcome followed by a new verb, it should probably be a separate learning outcome. This has helped me rein in that tendency to be additive when writing learning outcomes. Less is more.

Keeping learners motivated is sometimes a daunting task. Can you share a few tips for how organizations can achieve it, especially when implementing the achievement-goal theory?

Finding a way to keep learners motivated is a major focus for organizations and learning designers. A strategy to foster motivation needs careful consideration regarding the learner profile, need, and application context, and the motivational strategy should be “baked into” the design from the beginning.

It can be a challenge to convince SMEs that including tasks that guide learners to articulate their own motivation—even when that weighs against time that could be used for more engagement with the content—is an investment that will pay off in the long term. It could be thought of as a quality-versus-quantity discussion. Of course, the overwhelming need is to help learners achieve the target knowledge, skills, or attitudes. However, thinking in terms of using every available moment of the time allotted to either convey content or have learners apply skills without taking time for the “why” is perhaps short-sighted.

The ostensible why is generally clear from the learning outcomes and often explicitly articulated at the beginning of the course or module. Training and learning experiences are filled with phrases such as, “As a result of this training, you will have skills to meet the needs of…” This first why is the one that responds to the learning need—but not necessarily to the learners themselves. This why might be enough to motivate some learners to complete the training or learning experience. Beyond that, though?

To create sustained motivation that carries learners not only through the course, workshop, or module but also to the sustained application of KSAs gained, we should work to accommodate the desires and aspirations of each learner. How, you may say, can we think in terms of “desires” and “aspirations” resulting from an eight-hour instructor-led or ten-hour self-paced module on new compliance procedures or on fundamental principles of Accounting? To that, the answer might be, “Think bigger.”

If we confine our ideas about motivation to getting learners over the finish line of the intervention, we are missing an opportunity to help our learners develop self-regulated learning skills. There are many theories of motivation that we can examine to form a basis for fostering sustained motivation, and often, we combine relevant elements from different theories for the intervention.

One approach that I find particularly useful is achievement-goal theory. For the past three or more decades, this theory has been used in many different contexts and continues to find relevance. To give some very brief background on its origins, Nicholls (1984) explained that in achievement motivation, people will make a logical decision regarding reaching a goal. If we think the goal is achievable, we’ll expend time and effort to pursue it. On the other hand, if a goal seems too difficult, we might step back and aim for something a bit more achievable. In this way, we can avoid the appearance of being out of our depth.

Elliot (1999) further divided goal perception into performance goals and mastery goals (sometimes called learning goals). With performance goals, it’s more extrinsic, more about the competition. How does my performance stack up against others? Conversely, mastery goals are more intrinsic, focused on self-improvement, like mastering a subject or task, and are not connected to how others are doing. In 2006, Elliot again refined the theory by introducing two mindsets that influence how we tackle our goals: approach orientation and avoidance orientation. Essentially, our perception of the goal will drive us toward it or away from it.

As learning designers, we might have a tendency to think that the intrinsic goals will always be the ones leading to better motivation and achievement, but evidence from the literature is mixed. Both types can lead to equal gains. And that’s where we should be careful not to promote one over the other. For example, gamification mechanisms, such as leaderboards, might be just the thing to spark that competitive, performance approach edge in some learners. At the same time, others might opt out of that activity entirely, not wanting to have their name appear in the top 3, 5, or 10 spots.

And this leads us back to the second why, the why that resides with the learner and not with the intervention. We can’t know which of the two forces, mastery or performance, and which of the two orientations, approach or avoidance, drive a particular learner. And logically, those forces vary within the same individual depending on the context and where the learner is at that moment of their lives. The learners themselves may not even know until they have to express it.

That’s why we need to build in opportunities where learners can articulate this. The benefit is in the articulation of the goal more than in which approach or orientation is laid out. Learners write down for themselves in their own words what brought them to the training and what they hope to gain from it in the short term—but also in the medium and longer term.

This is how we can think bigger and connect the compliance procedures to finishing the training in the near term, having fewer compliance issues in the department over the following months, thereby gaining the attention of the manager or director, raising my profile for potential new responsibilities, a higher role, bonus or better salary, more opportunities, and beyond. How we can connect those fundamentals of Accounting to finishing the course this semester, finishing the degree, setting sights on a specific firm to work for—or on starting independent practice—and beyond?

Then we also need to build in space to revisit this at the end of the intervention if it’s shorter or at preset intervals if the intervention or program is longer. After all, we should take time to see what’s been accomplished so far and celebrate that progress or to see where things have maybe deviated and set a course to get back on track. Also, our goals and our motivations change over time. Needing to articulate goals and then following up on them later is where motivation and self-regulated learning intersect.

We also simply need to be reminded from time to time what it was that we set out to do. And so do our learners. Building in space early in the intervention for learners to examine and explain their goals can foster that sustained motivation we’re all striving for, and achievement-goal theory can be one way to help us accomplish this.

One of your areas of interest is the eclectic nature of Instructional Design. How do you think emerging technologies, such as AI, will impact this field in the near future?

This is the $64,000 question, isn’t it? Even in the near future, it’s hard to tell. I certainly find AI useful. It’s helped save hours needed to turn raw video transcripts that look like stream-of-consciousness text into accessible and logical text divided into paragraphs with section headers, capital letters, and punctuation needed for learners to make sense of anything. It’s also helpful for producing learning outcomes that broadly target a learning need that can be refined to apply to a particular set of learners.

What we can do with emerging technologies, such as AI, is expanding by the day. We have maybe only scratched the surface of what the benefits can be. The sky’s the limit!

One thing makes me very optimistic, and one thing makes me pause. Tasks like creating those transcripts for accessibility, having AI analyze HTML to see why particular formatting is not working well, and creating tailored images to illustrate content are really promising uses of AI. They can save so much time and offload a lot of the more busy work tasks, such as turning completely unformatted text into readable text, freeing up time to work more on other things. I tried to use AI to create tailored images from a text description. It didn’t work so well, but it was not that far off, and more work with the prompt could certainly have helped.

On the other hand, an overreliance on AI could result in deskilling rather than upskilling for learning designers. After all, when the AI can deliver four (fourteen, forty!) focused learning outcomes using ABCD guidelines written for a target learner profile in three seconds, the designer might be inclined to use those as-is rather than pause, break down each one, and cross-reference it with the specific needs of the actual learners. The AI can’t know all the context and nuance that the SME or learning designer knows, so if we rely on AI too much, it may be doing a disservice to the actual target learners, delivering decent but generic content and ideas rather than the bespoke learning solutions designers can help create.

What’s more, if we offload more and more tasks to AI, where does the balance of AI-as-tool to AI-as-learning-designer shift? Does our job become more about collating content produced by AI? “Humanizing” it? And is it saving us time after all if/when we have to fact-check it? I have no answers, only more questions. It’s too soon to tell, but what’s certain is that big changes are definitely coming. For the better? Yes. For the worse? Maybe also yes. As MIT professor Sherry Turkle has said, “Computers are not good or bad; they are powerful,” and AI is an order of magnitude more powerful.

Wrapping Up

Thanks so much to Dr. Susan Stetson-Tiligadas for sharing her thoughts with us and offering her insider tips to boost learner motivation and make the most of emerging tech in Instructional Design.

References

Elliot, A. J. 1999. Approach and avoidance motivation and achievement goals. Educational Psychologist, 34(3), 169. doi:10.1207/s15326985ep3403_3

Elliot, A. J. 2006. The hierarchical model of approach-avoidance motivation. Motivation & Emotion, 30(2), 111-116. doi:10.1007/s11031-006-9028-7

Nicholls, J. G. 1984. Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance. Psychological Review, 91(3), 328-346. doi:10.1037/0033-295X.91.3.328

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