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The Affordances of Content Design –

The Affordances of Content Design –

Content material is infrastructure.

David Wiley

I opened my first publish on this collection with a press release about courseware and content material design:

An unbelievable variety of words have been written concerning the know-how affordances of courseware—progress indicators, nudges, analytics, adaptive algorithms, and so on. But what seems to have gone utterly unnoticed in all this analysis is that the quiet revolution in the design of instructional content that makes all of those affordances potential. It is invisible to professional course designers because it is like the air they breathe. They take it without any consideration, and no one outdoors of their domain asks them what they’re doing or why. It’s invisible to everyone else as a result of no one talks about it. We are distracted by the know-how bells and whistle. But make no mistake: There can be no fancy courseware know-how with out this variation in content design. It is the key to the whole lot. When you understand it, instantly the know-how prospects and limitations turn into a lot clearer.

That’s all true. But this collection is not really about courseware. It is concerning the capabilities and limitations of digital curricular materials, whether they are merchandise bought by vendors, OER, or faculty-developed. The content design sample I’m exploring is neither unique to vended courseware products nor invented by business courseware providers. Actually, educational designers and LMS providers have been desperately making an attempt to convince school of the worth of this course design pattern for a many years. However designing content material this manner takes plenty of work and missing good examples of the return on that funding, most instructors have not opted to construct their content this manner.

What the proliferation of economic courseware offers that is new is a wealth of professionally developed examples that we will look at to raised perceive how this content design pattern works to help certain educating and studying affordances in digital curricular supplies. On this submit and the subsequent, I’ll draw on some of these examples, which happen to return from Empirical Educator Venture sponsors, to point out the design sample in action.

Crucial message of this collection, for each educators and technologists, is that real advances in instructional know-how will virtually all the time come up out of and be greatest understood by means of our information of educating and studying. In this case, technological affordances resembling learning analytics and adaptive studying are only potential because of the educational design of the content upon which they operate. And we typically overlook that “educational design” means design of instruction. The baseline we are working from is educational content material, usually (but not solely) designed for self-study. How much value can college students get from it? How far can we push that envelope? Whatever the fancy algorithms may be doing, they are doing it with, to, and across the content. The content is the infrastructure. So should you can develop a wealthy understanding of the value, uses, and limitations of the content material, then you possibly can understand the value, uses, and limitations of the each applied sciences applied to the content and the pedagogical strategies that the mixture of content and applied sciences afford.

The position of digital curricular materials

Let’s begin by wanting on the holistic position that digital curricular supplies play when carried out in a approach that the design pattern helps. From there, we’ll back into a few of the particulars.

I’ll ask you to observe a short promotional video from Pearson of a psychology professor who participated in considered one of their efficacy research shares her experiences and observations about educating with their courseware merchandise. (It is best to know that Pearson has engaged me as a marketing consultant to evaluation their efficacy reviews, together with this one, to offer them with suggestions on the best way to make these studies as useful as potential.) The fact that this professor’s story is a component of a bigger efficacy research signifies that it’s richly documented in methods which are helpful to our current function.

As you watch, pay attention to Dr. Williamson says concerning the affordances of the content material and how these affordances help her pedagogical methods and aims:

Dr. Manda Williamson of University of Nebraska-Lincoln on her courseware experiment

The first thing she talks about is layered formative assessments. Students are given small chunks of content material followed by frequent studying activities. They then are prompted to take formative assessments which, relying on the outcomes and the students’ confidence ranges, might end in recommending further exercise. (The one mentioned within the video was “rereading.”) If your anchor level for the value of the product is the readings that you simply assign for homework, then you possibly can see how interactive content material that’s properly designed on this approach is perhaps an enchancment over flat, non-interactive readings (and even videos).

When the students come into class—and that is key—Dr. Williamson engages with them on the results of their formative assessments. She teaches to the place the scholars are, and she or he is aware of where they’re as a result of she has the info from the formative assessments.

How does that work?

Those evaluation gadgets are tied to studying goals. Expertise and information which were clearly articulated. In nicely designed content, the training aims have been articulated first and the evaluation questions have been written particularly to align with these learning objectives. With this content design work in place, creating a “dashboard” shouldn’t be technologically difficult or fancy in any respect. No clever algorithms are vital.

Suppose you give students five questions for every learning objective. A method you might create a dashboard is to point out a line item for each studying objective and present what proportion of the class received all 5 questions right, what proportion received 4 out of five, and so on. I am going to present some instance dashboards from other products later in this submit. For now, the take-away is that the scholars are principally taking low-stakes quizzes together with their readings, and the trainer is getting the quiz outcomes earlier than the category starts in order that she will train the students to the place they’re.

Hopefully the formative assessments don’t feel like “quizzes;” Dr. Williamson has positioned them as instruments to assist the students study, which is how precisely how formative assessments must be positioned. But the primary point is that the content material consists of some assessed exercise which allows the instructor to have a clearer understanding of what the students know and what sorts of help they could want.

Because of adopting the digital content design and educating methods that the content material and know-how affordances supported, Dr. Williamson’s DFW price dropped from 44% to 12%. Since her course is a gateway course, that number is especially essential for general scholar success. So it is a dramatic success story. Nevertheless it’s not magic. Should you understand educating, and for those who take a look at the enhancements made in the self-study content material and the in-class educating strategies, you shortly come to see that it isn’t know-how magic but considerate curriculum design, strong product usability and utility, and exhausting work in the classroom that produced these positive factors. Know-how played a crucial but highly circumscribed supporting position.

You’ll be able to read more about Pearson’s efficacy research, starting from a tutorial account of the research to a more layperson-oriented educator information, here.

Design particulars

It’d help to make this somewhat extra concrete. I’ll present a number of example screens in this submit which are pretty intently tied to the essential affordances that I’ve discussed above, after which I’ll discover some more complicated variations in the subsequent publish in this collection.

I discussed earlier that the formative assessments ought to perform like quizzes however that college students shouldn’t really feel like they’re being examined. This concept—that the assessments are to help the students quite than to examine or surveil them—is constructed into the design of excellent curricular supplies on this fashion. For example, Lumen Studying’s Waymaker programs of a module that explicitly addresses this idea with the scholars:

Lumen Studying “Succeeding With Waymaker” module emphasizes the worth of formative assessment.

The Waymaker product then makes use of the formative assessments the scholars take, tied to their learning goals, to point out students the related content areas where they’ve proven mastery and others the place they still need some work. This scholar dashboard is known as the “research plan”:

Lumen Learning’s research plan updates based mostly on formative assessment scores.

There are totally different philosophies about learn how to provide this type of suggestions. One product designer informed me one philosophy he was occupied with is that the perfect dashboard is not any dashboard, which means that giving scholar little progress indicators and nudges are higher. For educators evaluating alternative ways to deliver the content material, the commonalities provide the instruments for evaluating the differences. “Knowledge” are (primarily) the formative scholar assessment solutions. “Analytics” are methods of summing up or extracting insights from the collection of solutions, either for a person scholar or for a category. “Dashboards,” “nudges,” and “progress indicators” are strategies of communicating helpful insights in ways in which encourage productive motion, both on the a part of the scholar or the educator.

Speaking of the latter, let us take a look at some educator dashboards. Let us take a look at a dashboard from Soomo Studying’s Webtext platform. Even before you get into how students are performing on their formative assessments, you may need to understand how far college students have gotten on their assigned work. This could be notably necessary in an asynchronous on-line course or different setting where you could have specific purpose to anticipate that college students might be shifting alongside at totally different paces. So this dashboard types scholar by their progress in a chapter:

Soomo Studying Webtext dashboard exhibits proportion of questions answered in a chapter.

Discover that progress right here is measured by proportion of questions answered. That tells us something about where the product designers assume the worth is. A formative assessment is not solely a measure of learning progress. Additionally it is a learning exercise in and of itself. We study by doing. We study extra successfully by doing and getting prompt feedback. So fairly than measure pages seen or time-on-page (though we do see a toggle choice for “time” within the higher right-hand corner), the first measure within the dashboard is proportion of questions answered.

Drilling down, Soomo also exhibits proportion right by web page:

Soomo’s Webtext dashboard exhibits scholar score by web page

There’s a little bit of a rabbit gap that I’ll point to but keep away from taking place relating to how cleanly one can separate studying goals. Does it all the time make the most sense to present one and only one studying objective per page? And in that case, then what’s the easiest way to present analytics? Fairly than explore Soomo’s specific philosophy on that fantastic level, let’s give attention to highlights of the low scores. That is one detail that instructors will need to know at some pretty effective degree of granularity. (If two learning goals are on the identical well-designed web page, it is often because they’re intently associated.) This dashboard allows instructors to see which students, both individually and as a gaggle, scored poorly on specific assessments on a page.

Once more, there is no algorithmic magic right here. Let’s assume for the sake of argument that the content and assessments are properly designed. Soomo is considering what educators would wish to find out about how students are progressing by way of the self-study content material so as to make good educational selections. They are then designing their screens to make that info obtainable at a glance.

Now imagine for a moment that you’ve this type of increased visibility on how college students are doing with their self-study. You see that students are doing properly on a learning objective general, however they’re scuffling with one specific query. In the previous world of analog homework, you won’t catch this kind of factor till a high-stakes check. But with digital curricular supplies, the place you may give extra formative evaluation and have it scored for you (inside the bounds of what machines are capable of scoring), you may shortly find one specific drawback in an evaluation that students are scuffling with. Is the question poorly written? Is it catching a hidden talent, or a twist that you simply did not understand made the problem troublesome? You’d need to drill down. This is a drill-down display from Macmillan’s Achieve formative assessment product:

Macmillan Achieve query drill-down exhibits question-by-question performance.

(You must know that I serve on Macmillan’s Influence Analysis Advisory Council.)

This is exactly the kind of clue that an educator may need to take a look at while getting ready for a class. What are the weird patterns of scholar performance? What may that inform us about hidden studying challenges and opportunities? And what may it tell us about our course design?

Hints of what’s coming

I am going to share two more display photographs as a method of teasing a number of the concepts coming within the next submit. This first one is from Carnegie Mellon University’s OLI platform:

Carnegie Mellon College OLI’s Predicted Mastery learning dashboard

At first look, this seems to be like one other learning dashboard. What proportion of the category are green, yellow, or purple (or haven’t began) for every learning goal? But discover one little phrase: “predicted mastery levels.” Predicted. Once you begin accumulating sufficient knowledge, by which we imply enough scholar scores to start to see significant patterns, we will apply statistical analysis to make predictions. There’s a specific amount of justifiable nervousness about utilizing predictive algorithms in schooling, but the problem springs from making use of the maths without understanding it. That’s what predictive algorithms are, at their most elementary. They’re statistical math formulation. And truthfully, most of the predictive algorithms utilized in ed tech are, in truth, primary sufficient that educators can get the gist of them. We’ve been taught to consider that the magic is within the algorithm. But really, most of the time, the magic is within the content material design.

And this is a display from D2L Brightspace:

Brightspace conditional launch pill view.

There’s rather a lot to unpack here, and I will not have the ability to get to it all in this publish. This can be a tablet view of features that Brightspace has been build up for ever and day. Since long before trendy courseware existed as a product category. For starters, you possibly can see within the prime box that Brightspace can assign mastery for a learning objective. (On this case, the target occurs to be “CBE Terminology: Prior Information.”) However what follows is a set of straightforward programming instructions of the form, “If a scholar meets situation X [e.g., receives less than 65% on a particular assessment] then perform action Y [e.g., show video Z].” Within the olden days of private computers, we might name this a “macro.” Within the olden days of LMSs, we might call it “conditional release.” At present’s scorching lingo for it is “adaptive studying” or “personalised learning.” Discover in this instance that we are still starting with performance towards a learning objective. We’re still beginning with content material design.

(Observe additionally that most of the know-how affordances built into vended courseware are additionally obtainable in content-agnostic merchandise like LMSs and have been for fairly some time. Instructors can build content in this design sample with the tools they have at hand and achieve advantages from it.)

Within the next submit, I’ll speak about how advanced statistical methods, together with machine learning methods, and automation, together with what we commonly check with as adaptive studying, are methods that digital course content designers use to reinforce the value of their course content designs. But all of these enhancements nonetheless construct off of and rely upon that bedrock content design pattern that I described within the first publish of this collection.

The atomic unit of digital curricular supplies design

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