Style Points: Augmented Reality and the Tailored Learning Experience

In case you missed the memo, the next wave of the Digital Revolution – in the form of immersive computing – is rapidly approaching the shores of higher ed, and with it, one of the greatest opportunities to transform learning in a generation.

Surfing along the crest of this radical wave of new technologies is augmented reality (AR). Sometimes referred to as “blended reality,” it allows users to experience the real world, printed text, or even a classroom lesson with an overlay of additional 3D data content, amplifying access to instant information and bringing it to life; in turn, bringing thrilling new opportunities for experiential education.

Perhaps more importantly, AR has the potential to democratize learning and tailor visual or data displays to fit a wide range of individual cognitive strengths. Augmented-reality apps and wearables enable access to rich, immersive educational experiences, and have the potential to differentiate instruction by catering to the specific learning needs and styles of an increasingly diverse student population. Because, let’s face it – many educators on the ground have already realized that a one-size-fits-all approach to curricular material does not always lead to strong learning outcomes.

Learning in Style

A better understanding of what differentiated learning means, in and of itself, may be helpful for developing lesson plans and instructional materials that meet the needs of individual students. Delving into the concept of “learning styles,” for one, can drive home the point that different students perceive and interact differently to information within their learning environment and, therefore, have varying preferences and necessities in terms of how they’re taught. (However, I should note that research on learning styles is an area of study that continues to evolve, so there is no definitive consensus on how to address this increasingly relevant issue in education as of this writing.)

To illustrate how AR can provide various entry points to learning, let’s discuss a few examples of learning preferences that researchers have identified, along with potential AR experiences that could speak to those learning styles.

Visual Learners

Many students learn best when they’re able to access visual rather than verbal information. Whereas classroom materials that integrate visuals might include presentation slides, textbooks, handouts and the like, AR takes visuals to the next level. Augmented Chemistry, a tangible user interface (TUI), is an example of the visual affordances of AR. Using TUI, chemistry students can pick up virtual atoms, position them to compose molecules, and rotate the 3D molecule to view it from all angles.  Compare this learning experience to the use of traditional textbooks consisting of 2D images that can’t be manipulated – the latter now seems pretty, well, flat in comparison, no?

Kinesthetic Learners

Kinesthetic learners respond well to physically engaging exercises, which place-based or location-based AR can offer in spades. Geological positioning systems (GPS) within place- or location-based AR systems give users access to relevant information as they arrive at a location, requiring them to physically move within an environment to complete tasks. AR provides kinesthetic learning opportunities, too, by allowing users to use bodily motions to manipulate virtual objects.

Social, Field-Dependent, and Application-Directed Learners

Researchers have also identified a learning-styles dimension that emphasizes the social aspect of learning. To wit, some learners desire interaction with others as a means of co-constructing knowledge. In addition to a preference for interacting with others, field-dependent learners rely on an external frame of reference (which may be provided by other learners); and then there are application-directed learners, who mainly prefer concrete applications of subject matter. Through leveraging connected learning and providing a virtual platform for social activity, AR has the potential to meet the needs of such learners.   

For example, in Environmental Detectives – an augmented-reality simulation game – users role-play environmental scientists. Players move about in a real space while being provided with location-specific information. They interview non-players to gather info, and they’re able to beam data to one another. Such a game incorporates social aspects of learning while also accommodating users who learn by interacting with an external frame of reference, as well as those learners who benefit from concretely applying their knowledge in a scenario.

Wave of the Future

With so many possibilities and applications, AR could truly be a game-changer in education. It allows for dynamic instruction that can’t be accomplished through traditional classroom experiences (without, of course, replacing the classroom altogether). Think of it as a powerful supplemental learning tool with the awesome ability to reach every style of student.

So join the Learning Lab team as we continue this journey and further explore the exciting realm of unprecedented opportunities AR presents us with here in higher ed. Together, we’ll face this new wave of immersive technology with open arms, encouraging educators to push the boundaries of teaching and, ultimately, the very boundaries of learning itself.

This blog post, written by Learning Lab Project Delivery Manager Lan Ngo, is the first in a series of posts that will explore AR technology and its applications in education. If you would like to add to this conversation, please leave a comment!

Learning Lab = World-Class Games for a Global University

Learning Lab Technical Dir. Sarah Toms (center) stands with students she guided through the Executive Development Program (EDP) sim in Thailand last year.

What do the Hong Kong University of Science and Technology, the IE Business School in Madrid, HEC Paris, and Dubai’s S P Jain School of Global Management all have in common with Wharton? Well, besides being among the Financial Timestop-ranked MBA programs in 2017, they all use simulation games developed right here in the Learning Lab.

And they’re not alone. Around the globe, from the Grandes Écoles (“Grand Schools”) of France to top universities in Copenhagan, Australia, India and dozens of other countries, there are thousands of students applying their burgeoning business acumen to The Startup Game and OPEQ — two of our best-selling sims available through Harvard Business Publishing (HBP), which recently issued a report detailing worldwide distribution of both games in 2016.

Their popularity in Wharton entrepreneurship and negotiation classes notwithstanding, the HBP report is a noteworthy success for our team in that it illustrates the symbiotic synergy between the goals of the Learning Lab and those of the University at large.

The former reflects the expressed intentions of our namesake, Alfred West Jr., who gifted the School with $10 million in 2001 to establish a veritable laboratory for creating “innovative learning tools that challenge students to think strategically across business functions and organizations” and enable Wharton to “take a lead role in rethinking the learning paradigm.” Nearly two decades later, the Learning Lab’s historic mission is increasingly central to President Amy Gutmann’s own vision for the future of the University of Pennsylvania.

According to Gutmann, “Our commitment to global engagement is essential to what I call ‘educational diplomacy. Now more than ever, we are bringing Penn to the world and the world to Penn. And in doing so, we are building stronger cross-cultural connections, deeper relationships, and mutual understanding within the global community.”

Wharton Dean Geoffrey Garrett in Seoul during his “Global Conversations Tour,” where he shared his vision for the School.

Sharing an ethos that embraces collaboration and the exchange of knowledge is Wharton Dean Geoffrey Garrett. “Globalization and technological change are poised to transform business education. I have no doubt Wharton will be in the vanguard of this transformation here and in other countries,” he stated upon taking over the position in 2014.

The School’s Executive Education division has helped draw an international audience as well, partnering with the Learning Lab to build custom learning experiences for foreign audiences both on-campus and abroad.

In 2016 alone, more than 1,000 participants experienced one of our simulations in their Wharton Exec Ed program. One of Africa’s foremost financial institutions, for example, has sent over managerial staffers for a two-week business-leadership bootcamp built around the EDP Simulation four times in the last two years! (And it always ends the same: with a celebratory, fist-pumping “warrior chant.” See it in the video below.) Among dozens of other games to cheer for, I should note, we developed a similar EDP program for a multi-national manufacturer in Thailand, which Technical Director extraordinaire Sarah Toms flew out to personally facilitate in 2016.

From the start, Dean Garrett has made it known that, though seated in the U.S., he sees Wharton as an asset to the entire world — and, in turn, bringing the world into the classroom in order to prepare students to be truly global leaders.

Whether that classroom is in Singapore, Saudi Arabia, Switzerland or Estonia, Learning Lab sims like OPEQThe Startup Game, and EDP are doing just that, augmenting traditional learning in undergraduate, MBA, and executive education programs with dynamic, virtual “real-life” business experiences. And while they may be created with faculty members here in Philadelphia, they are now driving home their educational underpinnings on campuses around the globe.

SIMPL: One Data Model to Rule Them All

Code starts at the model-level. So before we wrote one line of SIMPL (the Learning Lab’s new simulation framework), we needed to figure out what, exactly, our data model would look like. Considering the ambitious goal of the project — a simulation framework that could support all of our current games as well as games yet unknown — we had to be very careful to create one that would be flexible enough to adjust to our growing needs, but not so complex as to make development overly challenging. Luckily, we have decades worth of simulation development expertise on our team, and were able to draw from that wellspring of knowledge when we worked on SIMPL’s foundational data model.

A data model, I should say, is basically the definition of how data is stored in the system, and how the pieces of data relate to one another. When we began the process of creating SIMPL, we needed to define the logical pieces that create a simulation, and build relationships among those pieces that, well, made sense.  

Speaking the Same Language

Our first challenge was agreeing upon a nomenclature for the pieces that comprise a simulation in general. This may seem like a fairly trivial process; after all, everyone pretty much knows what we mean when we say a “game run,” or  a “decision,” or a “scenario.” However, the implications of this language when developing a data model meant different things to different people — especially when we tried to communicate these requirements to the outside vendor working with us on the platform. With that in mind, we ended up creating a glossary of terms, defined right in the context of the simulation platform. This glossary helped us bridge the gap between our team and the vendor, allowing us to talk about terms in ways we all agreed upon and understood.  

Start with What You Know

Once we agreed on the definitions of various parts that make up a sim, we began to map out what our data model would look like. To assist us in this process, we leaned on our collective years of simulation experience here in the Learning Lab — namely, the games we’ve already supported and developed. Then came the whiteboarding (sooo much white boarding), wherein we drew relationships between objects and assessed if the connections we were making made sense.

We then broke down existing games and made sure the new data model would be able to accommodate the unique implementation of each of those sims. This served as a valuable “smoke test” for us — i.e., a way to ensure we were on the right track. To that end, we picked games with diverse implementations in order to be 100-percent certain the model we were creating was flexible enough to meet our needs.

The results of one of our white-boarding sessions. 

 

The current SIMPL data model.

Where to Go from Here?

After a long period of iteration, we finally settled on a data model that made sense to both us and our vendor. We made further changes along the way as development progressed, but the main structure we came up with remained the same from whiteboard etchings to the implementation of our first sim. Going forward, of course, every new simulation we develop will be an opportunity to test the limits of this model, which we can improve or simplify where and when the need arises.

Moreover, the lessons we learned building our data model for SIMPL could be applied to any data-driven application. In that regard, here are the main things we came away with:

  • Take time to think deeply about your data model, and do so in collaboration with project managers and developers who will ultimately be responsible for the application. The decisions you make here will dramatically impact the future of your application. It’s easy to make changes when you’re working on a whiteboard; it’s a lot harder to do so once you’ve written applications dependent on the model.
  • Don’t assume everyone knows what you mean when describing the model. And, perhaps equally important, empower your team members to speak up when something does not make sense. Data models can be complex animals, and the more everyone understands, the better end result you will have.
  • Test your assumptions. Before a single line of code is written, walk through hypothetical applications with your data model. Can you get the data you need in a duly sensible way? Do the relationships you’ve built reflect the logic required within the application? The more tests you run, the more confidence you have in making sure you have a solid model.

In the wise words of George Harrison, “If you don’t know where you are going, any road will take you there.”

That same logic can be applied to creating a data model that hits all the right notes. Given that this critical construct would be the cornerstone of our new simulation framework, if we hadn’t spent the time to exhaustively map out all of our needs (as well as how SIMPL would meet them), then there was a good chance we would have lost direction and the whole project could have veered off course. So take it from us —  while it can be tempting to take shortcuts when embarking on a project of this scale, carefully inching your way through a proper planning phase goes a long way toward ensuring that you’re ultimately able to reach your destination and meet your end-goals.

SIMPL Magic: Automatic Browser Page Updates

In multiplayer web-based games, all users should be able to see up-to-date game data without having to manually refresh their browsers. For example, players need to be notified when the game has moved from a phase in which they can submit decisions to a phase in which they cannot submit decisions. Monitoring the game state for such changes is often handled by the simulation’s front-end code.

One of the real pleasures of developing simulations using the Learning Lab-authored SIMPL framework is never needing to request fresh data in front-end code. That’s because SIMPL’s architecture ensures a game user’s browser page is always up to date. Curious how we managed to pull that off? Then keep reading!

First, it’s important to understand that each SIMPL game comprises three components:

  • SIMPL-Games-API (a service shared with other games that maintains the SIMPL database)
  • Model Service (defines and runs the game’s simulation model)
  • Front-end Server (provides the game’s user interface assets to the browser)

SIMPL-game-architecture

Architecture of a SIMPL game

 

Our SIMPL-Games-API service manages the SIMPL database. It provides a REST API used by the game’s model service.

The game’s model service defines the game’s simulation model and handles running the simulation and database updates. It is implemented in Python using classes provided by our SIMPL-Modelservice package.

The game’s front-end user interface code is implemented in Javascript using SIMPL game front-end functions provided by our SIMPL-React JavaScript library (built using React and Redux).

These SIMPL game components work together in concert to ensure that game users consistently see the current state-of-the-game data stored in the SIMPL database.

Here’s how it works: Each time the model service updates the database using the SIMPL-Games-API’s REST API, a webhook is triggered that notifies SIMPL-Modelservice functions of the update. SIMPL-Modelservice code then pushes an update notification to each game user’s browser via WAMP. There, SIMPL-React code handles updating the browser’s Redux store state, automatically updating the React components.

And there you have it — the user is automagically guaranteed to see fresh data, without game authors having to write a line of code! It works like magic, but it’s actually quite SIMPL

 

For more details, please see our SIMPL Framework docs.

SIMPL: Wharton Launches Its Own Simulation Framework

Imagine a world where simulations are simply … SIMPL!

Simulations are expensive to create, require highly specialized expertise, and if you create something that doesn’t deliver against the intended learning objectives, the process to make tweaks and updates can be more complicated than is necessary. There are other persistent challenges that keep us managers of teams working on simulations up at night. Such as retention of technical talent, which is difficult because if you’re authoring simulations on a commercial platform, your team will need to learn a large amount of specialized know-how, and these are skills that most times don’t translate to other careers in technology. Authoring platforms also present other problems – including a lack of integration with our learning management systems (LMS), the best source for user management, and no single sign on authentication integration.

When we hit the mark, simulations are an incredibly powerful and effective form of educational technology, that can far outperform traditional lectures and cases.

For example, students who completed our Looking Glass for Entrepreneurship simulation performed one standard deviation better on the final exam than students who didn’t go through this experience. And we have lots and lots of examples just like this one!

With a burning desire to overcome the challenges we face in the simulation space, in 2016 the Learning Lab authored our own simulation framework called SIMPL, which is written on the already open source Python/Django. We’re incredibly excited about this new direction for the team, and are already seeing a myriad of returns on our efforts.

At Wharton we have completed our first multi-player simulation on SIMPL – Rules of Engagement – a marketing strategy simulation. Intermap – a mind mapping tool used in idea generation – is utilizing certain aspects of SIMPL, namely the LTI integration libraries for authentication and the tool is published within Canvas as a module, also using aspects of SIMPL (User management? What user management!). There are a number of other simulation projects in the pipeline for the coming year, and all will be written on SIMPL.

Possibly the most exciting part about controlling our own destinies is that in mid-2017 we will release SIMPL to the world, free of charge, and under an open source license. Our goal is to develop a rich community of practitioners and other experts around this framework, because we believe a rising tide lifts all boats. If you’re interested in getting a sneak peak into SIMPL, here’s the docs. In the coming weeks, there will be a variety of blog posts from other SIMPL authors about more specific areas of the SIMPL framework. And if you’re interested in being included in the beta, please email learninglab@wharton.upenn.edu.

SIMPL-Architecture

SIMPL Architecture

 

SIMPL-Team

Here’s the team behind SIMPL, left to right – Donna St. Louis, Flavio Curella (Revsys), Joseph Lee, Jane Eisenstein, Sarah Toms.

Not pictured: Frank Wiles and Jeff Triplett, Revsys

 

 

 

Does this process make me look fat?

bang-head-here…Hey. Where did everybody go?!

Relax. When it comes to workflow efficiency or best practices, this is not a trick question. But it’s also not a question you’re ever likely to hear. That’s because people rarely ask for – or voice – an honest opinion on bad, bloated, or outdated processes. They’re just something we grudgingly don in order to get our work completed.

Truth be told, it’s oftentimes easier to bear the burden of “that’s just how it’s always been done” than to actually address the flaws of ill-fitting processes directly.

However, as part of my new role with the Learning Lab I’ve been given a unique opportunity to do just that. This need was born out of the Lab taking on an ever-increasing amount of multi-day simulations (such as the Executive Development Program) that require gritting through the bad stuff in the heat of the moment, while thinking of ways we could definitely do it better in the future. And just as you’d expect, I’ve found that tackling baked-in redundancies or antiquated inefficiencies can be a delicate dance between helping and offending. I’ve also learned that altering a process requires a process all of its own…

Read more ›

Monopoly’s Anti-Capitalist, Socialist Roots as a Teaching Game at Wharton

Monopoly board game

“A virtue of gaming that is sometimes overlooked by those seeking grander goals is its unparalleled advantages in training and educational programs. A game can easily be made fascinating enough to put over the dullest facts. To sit down and play through a game is to be convinced as by no argument, however persuasively presented.”

— A.M. Mood, RAND Corporation (1954)

Look no further than the Learning Lab for proof that games play an increasingly valuable role in the classroom and beyond, having long been recognized as a uniquely effective means of experiential education. But while, today, we harness technology and data to craft immersive, competitive simulation platforms, sometimes all you need to teach complex concepts is a board, some moveable pieces, and a pedagogical goal.

Take chess, for instance, which has been used for centuries to impart lessons of military strategy – its rules and competitive purpose create the conditions for tactical thinking and planning needed to checkmate one’s opponent.

Then there’s Monopoly, wherein the primary objective is to bankrupt everyone else through clever investment strategies. Hard to square that with lofty, Ivy-league business objectives, right? Yet, what is arguably the world’s best-selling board-based simulation of capitalism (and frequent ruiner of family game night) was once used as a teaching aid in Wharton economics classes. But before you cynically smirk at the very idea, there’s something you should know about the game’s hidden history: A century ago, Monopoly was not a platform to illustrate the merits of a laissez-faire system; rather, it was a way to demonstrate an alternative to the corporate rent-seeking that drives inequality.

Read more ›

Women! Where the heck are you?! The tech field NEEDS you!

women techI read a Fast Company article last night citing a recent SmartAsset study on women in tech. It has me thinking a lot about my own 20-year career in technology, and wondering: “Women! Where the heck are you?!

From my own experience working in technology, this is an amazing field – one where I have been given countless opportunities to progress, and one in which I am challenged intellectually every single day. I’m paid well (inline with my male colleagues), and when I’ve needed or wanted to seek out new challenges, it’s never taken me long to find something that fits my needs and desires perfectly. Most importantly, my career in technology has allowed me to build a life for myself and my family that is far exceeding expectations.

For all these reasons, the fact that we – female tech experts – are still a minority has me dumbfounded, and I’m trying to understand why there simply aren’t more of us. Read more ›

Recipe for Quick Coding: How to Cook Up a Good Glossary, Fast

glossaryTwo weeks before the Learning Lab’s new Customer Centricity simulation was set to go live for the first time in a Wharton MBA class, I was asked to add a CRM glossary to it – one that could grow as more data reports became available to a player throughout the course of the game.

Suffice it to say this was a quite a task given the timeframe. Nevertheless, I approached the challenge with an open mind and a lot of quick thinking. Viewing it as a somewhat exploratory endeavor, I managed to meet the deadline and our sim made its scheduled debut with a fresh-baked working glossary. Now, having devised an efficient process for whipping one up on the fly, I’d like to share with you my recipe:

Read more ›

Fighting the Good-Code Fight (Or, ‘We Need To Talk About Tech Debt’)

Maximus

“What we do in life echoes in eternity…” 

I’ll hazard a guess that “Gen. Maximus Decimus Meridius” (hint: the gladiator in Gladiator) was not thinking about the importance of code quality and documentation when he addressed the above wisdom to his cavalry on the battlefield.

But really, what IT organization wouldn’t benefit from a fictional Roman general showing up before the start of a new project to gravely remind everyone about lasting consequences? After all, the decisions you make in code design today will affect your organization for months – or years – into the future.

Read more ›