Building data science fluency using games

The National Science Foundation has awarded the Concord Consortium a three-year Cyberlearning grant to develop and test new data science games for high school biology, chemistry, and physics, and research how learners conceive of and learn with data. The Data Science Games project builds on prior work, which led to the invention of a new genre of learning technology—a “data science game.”

The use of games for education is a growing field with significant promise for STEM learning. Games provide a powerful means of motivation and engagement, and align with many STEM learning goals. Data Science Games is making use of the data generated as students play digital games in a novel and creative way. When students play a data science game, their gameplay actions generate data—data that is essential to the game itself. To succeed at a data science game, students must visualize, understand, and properly apply the data their game playing has generated in order to “level up” and progress within the game. As they visualize and analyze the data, planning and plotting new, evolving strategies, students learn the fundamentals of data science.

The new data science games will be embedded in our open source Common Online Data Analysis Platform (CODAP). Data from the games will flow seamlessly into CODAP thanks to innovations that leverage advances in the interoperability of components embedded in browsers, new capabilities for data visualization using HTML5, and recent innovations in design of interfaces compatible with both PC-based browsers and touch devices.

Project research will investigate ways this new genre of educational technology can be integrated into classroom learning. We will identify and characterize learner perceptions of data, including how learners see flat, hierarchical, and network structures as emerging from realistic problems; questions learners ask with data; and learning trajectories for restructuring and visualization of data.

The project will also produce guidelines for making use of data science games across a range of grade levels and subject matter. Data Science Games will thus provide both models and templates of how to integrate learning of data science into existing content areas, helping to grow the next generation of data scientists.

Data Science Games Play Roshambo against the evil Dr. Markov (log in as guest). If you win, you can save Madeline the dog. Improve your odds by analyzing Markov’s moves in a graph.

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Solarizing a house in Energy3D

Fig. 1 3D model of a real house near Boston (2,150 sq ft).
On August 3, 2015, President Obama announced the Clean Power Plan – a landmark step in reducing carbon pollution from power plants that takes real action on climate change. Producing clean energy from rooftop solar panels can greatly mitigate the problems in current power generation. In the US, there are more than 130 million homes. These homes, along with commercial buildings, consume more than 40% of the total energy of the country. With improving generation and storage technologies, a large portion of that usage could be generated by home buildings themselves.

A practical question is: How do we estimate the energy that a house can potentially generate if we put solar panels on top of it? This estimate is key to convincing homeowners to install solar panels or the bank to finance it. You wouldn't buy something without knowing its exact benefits, would you? This is why solar analysis and evaluation are so important to the solar energy industry.

The problem is: Every building is different! The location, the orientation, the landscape, the shape, the roof pitch, and so on, vary from one building to another. And there are over 100 MILLION of them around the country! To make the matter even more complicated, we are talking about annual gains, which require the solar analyst to consider solar radiation and landscape changes in four seasons. With all these complexities, no one can really design the layout of solar panels and calculate their outputs without using a 3D simulation tool.

There may be solar design and prediction software from companies like Autodesk. But for three reasons, we believe that our Energy3D CAD software will be a relevant tool in this marketplace. First, our goal is to enable everyone to use Energy3D without having to go through the level of training that most engineers must go through with other CAD tools in order to master them. Second, Energy3D is completely free of charge to everyone. Third, the accuracy of Energy3D's solar analysis is comparable with that of others (and is improving as we speak!).

With these advantages, it is now possible for homeowners to evaluate the solar potential of their houses INDEPENDENTLY, using an incredibly powerful scientific simulation tool that has been designed for the layperson.

In this post, I will walk you through the solar design process in Energy3D step by step.

1) Sketch up a 3D model of your house

Energy3D has an easy-to-use interface for quickly constructing your house in a 3D environment. With this interface, you can create an approximate 3D model of your house without having to worry about details such as interiors that are not important to solar analysis. Improvements of this user interface are on the way. For example, we just added a handy feature that allows users to copy and paste in 3D space. This new feature can be used to quickly create an array of solar panels by simply copying a panel and hitting Ctrl/Command+V a few times. As trees are important to the performance of your solar panels, you should also model the surrounding trees by adding various tree objects in Energy3D. Figure 1 shows a 3D model of a real house in Massachusetts, surrounded by trees. Notice that this house has a T shape and its longest side faces southeast, which means that other sides of its roof may worth checking.
Fig. 2 Daily solar radiation in four seasons

2) Examine the solar radiation on the roof in four seasons

Once you have a 3D model of your house and the surrounding trees, you should take a look at the solar radiation on the roof throughout the year. To do this, you have to change the date and run a solar simulation for each date. For example, Figure 2 shows the solar radiation heat maps of the Massachusetts house on 1/1, 4/1, 7/1, and 10/1, respectively. Note that the trees do not have leaves from the beginning of December to the end of April (approximately), meaning that their impacts to the performance of the solar panels are minimal in the winter.

The conventional wisdom is that the south-facing side of the roof is a good place to put solar panels. But very few houses face exact south. This is why we need a simulation tool to analyze real situations. By looking at the color maps in Figure 2, we can quickly figure out that the southeast-facing side of the roof of this house is the optimal side for solar panels and we also know that the lower part of this side is shadowed significantly by the surrounding trees.

Fig. 3 Solarizing the house
3) Add, copy, and paste solar panels to create arrays

Having decided which side to lay the solar panels, the next step is to add them to it. You can drop them one by one. Or drop the first one near an edge and then copy and paste it to easily create an array. Repeat this for three rows as illustrated in Figure 3. Note that I chose the solar panels that have a light-electricity conversion efficiency of 15%, which is about average in the current market. New panels may come with higher efficiency.

The three rows have a total number of 45 solar panels (3 x 5 feet each). From Figure 2, it also seems the T-wing roof leaning towards west may be a sub-optimal place to go solar. Let's also put a 2x5 array of panels on that side. If the simulation shows that they do not worth the money, we can just delete them from the model. This is the power of the simulation -- you do not have to pay a penny for anything you do with a virtual house (and you do not have to wait for a year to evaluate the effect of anything you do on its yearly energy usage).

4) Run annual energy analysis for the building

Fig. 4 Energy graphs with added solar panels
Now that we have put up the solar panels, we want to know how much energy they can produce. In Energy3D, this is as simple as selecting "Run Annual Energy Analysis for Building..." under the Analysis Menu. A graph will display the progress while Energy3D automatically performs a 12-month simulation and updates the results (Figure 4).

I recommend that you run this analysis every time you add a row of solar panels to keep track of the gains from each additional row. For example, Figure 4 shows the changes of solar outputs each time we add a row (the last one is the 10 panels added to the west-facing side of the T-wing roof). The following lists the annual results:
  • Row 1, 15 panels, output: 5,414 kWh --- 361 kWh/panel
  • Row 2, 15 panels, output: 5,018 kWh (total: 10,494 kWh) --- 335 kWh/panel
  • Row 3, 15 panels, output: 4,437 kWh (total: 14,931 kWh) --- 296 kWh/panel
  • T-wing 2x5 array, 10 panels, output: 2,805 kWh (total: 17,736 kWh) --- 281 kWh/panel
These results suggest that 30 panels in Rows 1 and 2 are probably a good solution for this house -- they generate a total of 10,494 kWh in a year. But if we have better (i.e., high efficiency) and cheaper solar panels in the future, adding panels to Row 3 and the T-wing may not be such a bad idea.

Fig. 5 Comparing solar panels at different positions
5) Compare the solar gains of panels at different positions

In addition to analyze the energy performance of the entire house, Energy3D also allows you to select individual elements and compare their performances. Figure 5 shows the comparison of four solar panels at different positions. The graph shows that the middle positions in Row 3 are not good spots for solar panels. Based on this information, we can go back to remove those solar panels and redo the analysis to see if we will have a better average output of Row 3.

After removing the five solar panels in the middle of Row 3, the total output drops to 16,335 kWh, meaning that the five panels on average output 280 kWh each.

6) Decide which positions are acceptable for installing solar panels

The analysis results thus far should provide you enough information with regard to whether it worth your money to solarize this house and, if yes, how to solarize it. The real decision depends on the cost of electricity in your area, your budget, and your expectation of the return of investment. With the price of solar panel continuing to drop, the quality continues to improve, and the pressure to reduce fossil energy usage continues to increase, building solarization is becoming more and more viable.

Solar analysis using computational tools is typically considered as the job of a professional engineer as it involves complicated computer-based design and analysis. The high cost of a professional engineer makes analyzing and evaluating millions of buildings economically unfavorable. But Energy3D reduces this task to something that even children can do. This could lead to a paradigm shift in the solar industry that will fundamentally change the way residential and commercial solar evaluation is conducted. We are very excited about this prospect and are eager to with the energy industry to ignite this revolution.

Open invitation to software developers

CODAP Screenshot Our Common Online Data Analysis Platform (CODAP) offers easy-to-use web-based software that makes it possible for students in grades 6 through college to visualize, analyze, and ultimately learn from data. Whether the source of data is a game, a map, an experiment, or a simulation, CODAP provides an immersive, exploratory experience with dynamically linked data representations, including graphs, maps, and tables. CODAP is not dependent on specific content, so data analysis can be integrated into math, science, history, or economics classrooms.

CODAP is HTML5, making use of JavaScript, HTML, and CSS3. Various open source libraries are part of CODAP, including SproutCore, JQuery, Raphaël, Leaflet, and several other smaller libraries. CODAP uses SproutCore as an application framework. You can deploy CODAP as a static website with no server interaction. CODAP can be configured to store documents on your local device, or integrated with an online server for cloud-based document management. It can also log user actions to a server specified in a configuration file.

Our goal is to create a community of curriculum and software developers committed to ensuring that students from middle school through college have the knowledge and skills to learn with data across disciplines. We need your help!

Get involved

Digital gaming will connect afterschool students with biotech mentors

Our nation’s future competitiveness and our citizens’ overall STEM literacy rely on our efforts to forge connections between the future workforce and the world of emerging STEM careers. Biotechnology, and genetics in particular, are rapidly advancing areas that will offer new jobs across the spectrum from technicians to scientists. A new $1.2 million National Science Foundation-funded project at the Concord Consortium will use Geniverse, an immersive digital game where students put genetics knowledge into action as they breed dragons, to help connect underserved students with local biotechnology professionals to strengthen student awareness of STEM careers.

East End House Students

Students from East End House enjoy collaborating on computer-based science activities.

Geniverse is our free, web-based software designed for high school biology that engages students in exploring heredity and genetics by breeding and studying virtual dragons. This game-like software allows students to undertake genetics experimentation with results that closely mimic real-world genetics. The new GeniConnect project will extend the gaming aspects of Geniverse and revise the content to more fully target middle school biology, introducing Geniverse to the afterschool environment.

The three-year GeniConnect project will develop and research a coherent series of student experiences in biotechnology and genetics involving game-based learning, industry mentoring, and hands-on laboratory work. Industry professionals from Biogen, Monsanto, and other firms will mentor afterschool students at East End House, a community center in East Cambridge, Massachusetts.

With researchers from Purdue University, we’ll explore how an immersive game and a connection to a real scientist can increase STEM knowledge, motivation, and career awareness of underserved youth. We will also develop and research a scalable model for STEM industry/afterschool partnerships, and produce a STEM Partnership Toolkit for the development of robust, educationally sound partnerships among industry professionals and afterschool programs. The Toolkit will be distributed to approximately 500 community-based organizations and afterschool programs nationally that are member organizations of the Alliance for Strong Families and Communities.

Geniverse Narrative

Beautiful graphics designed by FableVision Studios engage students in a compelling narrative. Students follow the arduous journey of their heroic character and suffering dragon to the Drake Breeder’s Guild.

Geniverse Lab

Students are welcomed into the Drake Breeder’s Guild where they will learn the tricks of the genetic trade. (Drakes are a model species that can help solve genetic mysteries in dragons, in much the same way as the mouse is a model species for human genetic disease.) Students are engaging in an authentic, experiment-driven approach to biology—in a fantastical world.

Launching a new interdisciplinary field of study in spoken language technology for education

A grant from the National Science Foundation will help launch a new interdisciplinary field of study in spoken language technology for education. The one-year “Building Partnerships for Education and Speech Research” project will unite the extensive education research and educational technology backgrounds at the Concord Consortium and SRI International’s Center for Technology in Learning (CTL) and bring them together with two of the strongest groups in spoken language technology research, the Speech Technology and Research (STAR) Laboratory at SRI and the Center for Robust Speech Systems (CRSS) at the University of Texas at Dallas.

The sophistication of technologies for processing and understanding spoken language—such as speech recognition, detection of individual speakers, and natural language processing—have radically improved in recent years, though most people’s image of modern spoken language technology is colored by often-finicky interactions with Siri or Google products. In fact, many lesser-known technologies can now automatically detect many features of speech, including question asking, dialog interchanges, word counts, indication of emotion or stress, and specific spoken keywords with high accuracy.

However, educational research has barely begun exploring their potential to provide insight into, and eventually revolutionize, research areas as diverse as collaboration, argumentation, discourse analysis, emotion, and engagement. And capturing the most critical and substantive interactions during the teaching and learning process—the discourse and conversation among students, teachers, and mentors—remains elusive.

The central goal of this new project is to generate interest in and momentum toward the use of spoken language technologies in education research. The potential for such applied technologies is vast, and the broader impacts could be significant. As these technologies become established for use in improved education research and development, researchers will be able to better understand and target interventions, educators will be able to monitor and adjust their interactions with learners, and learners will be better informed of their learning progress.

The National Science Foundation funds grant to pair intelligent tutoring system and Geniverse

Games, modeling, and simulation technologies hold great potential for helping students learn science concepts and engage with the practices of science, and these environments often capture meaningful data about student interactions. At the same time, intelligent tutoring systems (ITS) have undergone important advancements in providing support for individual student learning. Their complex statistical user models can identify student difficulties effectively and apply real-time probabilistic approaches to select options for assistance.

The Concord Consortium is proud to announce a four-year $1.5 million grant from the National Science Foundation that will pair Geniverse with robust intelligent tutoring systems to provide real-time classroom support. The new GeniGUIDE—Guiding Understanding via Information from Digital Environments—project will combine a deeply digital environment with an ITS core.

Geniverse is our free, web-based software for high school biology that engages students in exploring heredity and genetics by breeding and studying virtual dragons. Interactive models, powered by real genes, enable students to do simulated experiments that generate realistic and meaningful genetic data, all within an engaging, game-like context.

Geniverse Breeding

Students are introduced to drake traits and inheritance patterns, do experiments, look at data, draw tentative conclusions, and then test these conclusions with more experimentation. (Drakes are a model species that can help solve genetic mysteries in dragons, in much the same way as the mouse is a model species for human genetic disease.)

The GeniGUIDE project will improve student learning of genetics content by using student data from Geniverse. The software will continually monitor individual student actions, taking advantage of ITS capabilities to sense and guide students automatically through problems that have common, easily rectified issues. At the classroom level, it will make use of this same capability to help learners by connecting them to each other. When it identifies a student in need of assistance that transcends basic feedback, the system will connect the student with other peers in the classroom who have recently completed similar challenges, thus cultivating a supportive environment.

At the highest level, the software will leverage the rich data being collected about student actions and the system’s evolving models of student learning to form a valuable real-time resource for teachers. GeniGUIDE will identify students most in need of help at any given time and provide alerts to the teacher. The alerts will include contextual guidance about students’ past difficulties and most recent attempts as well as suggestions for pedagogical strategies most likely to aid individual students as they move forward.

The Concord Consortium and North Carolina State University will research this layered learner guidance system that aids students and informs interactions between student peers and between students and teachers. The project’s theoretical and practical advances promise to offer a deeper understanding of how diagnostic formative data can be used in technology-rich K-12 classrooms. As adaptive student learning environments find broad application in education, GeniGUIDE technologies will serve as an important foundation for the next generation of teacher support systems.

Daily energy analysis in Energy3D

Fig. 1: The analyzed house.
Energy3D already provides a set of powerful analysis tools that users can use to analyze the annual energy performance of their designs. For experts, the annual analysis tools are convenient as they can quickly evaluate their designs based on the results. For novices who are trying to understand how the energy graphs are calculated (or skeptics who are not sure whether they should trust the results), the annual analysis is sometimes a bit like a black box. This is because if there are too many variables (which, in this case, are seasonal changes of solar radiation and weather) to deal with at once, we will be overwhelmed. The total energy data are the results of two astronomic cycles: the daily cycle (caused by the spin of the Earth itself) and the annual cycle (caused by the rotation of the Earth around the Sun). This is why novices have a hard time reasoning with the results.

Fig. 2: Daily light sensor data in four seasons.
To help users reduce one layer of complexity and make sense of the energy data calculated in Energy3D simulations, a new class of daily analysis tools has been added to Energy3D. These tools allow users to pick a day to do the energy analyses, limiting the graphs to the daily cycle.

For example, we can place three sensors on the east, south, and west sides of the house shown in Figure 1. Then we can pick four days -- January 1st, April 1st, July 1st, and October 1st -- to represent the four seasons. Then we run a simulation for each day to collect the corresponding sensor data. The results are shown in Figure 2. These show that in the winter, the south-facing side receives the highest intensity of solar radiation, compared with the east and west-facing sides. In the summer, however, it is the east and west-facing sides that receive the highest intensity of solar radiation. In the spring and fall, the peak intensities of the three sides are comparable but they peak at different times.

Fig. 3: Daily energy use and production in four seasons.
If you take a more careful look at Figure 2, you will notice that, while the radiation intensity on the south-facing side always peaks at noon, those on the east and west-facing sides generally go through a seasonal shift. In the summer, the peak of radiation intensity occurs around 8 am on the east-facing side and around 4 pm on the west-facing side, respectively. In the winter, these peaks occur around 9 am and 2 pm, respectively. This difference is due to the shorter day in the winter and the lower position of the Sun in the sky.

Energy3D also provides a heliodon to visualize the solar path on any given day, which you can use to examine the angle of the sun and the length of the day. If you want to visually evaluate solar radiation on a site, it is best to combine the sensor and the heliodon.

You can also analyze the daily energy use and production. Figure 3 shows the results. Since this house has a lot of south-facing windows that have a Solar Heat Gain Coefficient of 80%, the solar energy is actually enough to keep the house warm (you may notice that your heater runs less frequently in the middle of a sunny winter day if you have a large south-facing window). But the downside is that it also requires a lot of energy to cool the house in the summer. Also note the interesting energy pattern for July 1st -- there are two smaller peaks of solar radiation in the morning and afternoon. Why? I will leave that answer to you.

Energy3D in Colombia

Camilo Vieira Mejia, a PhD student of Purdue University, recently brought our Energy3D software to a workshop, which is a part of Clubes de Ciencia -- an initiative where graduate students go to Colombia and share science and engineering concepts with high school students from small towns around Antioquia (a state of Colombia).

Students designed houses with Energy3D, printed them out, assemble them, and put them under the Sun to test their solar gains. They probably have also run the solar and thermal analyses for their virtual houses.

We are glad that our free software is reaching out to students in these rural areas and helping them to become interested in science and engineering. This is one of the many examples that a project funded by the National Science Foundation also turns out to benefit people in other countries and impact the world in many positive ways. In this sense, the National Science Foundation is not just a federal agency -- it is a global agency.

If you are also using Energy3D in your country, please consider contacting us and sharing your stories or thoughts.

Energy3D is intended to be global -- It currently includes weather data from 220 locations in all the continents. Please let us know you would like to include locations in your country in the software so that you can design energy solutions for your own area. As a matter of fact, this was exactly what Camilo asked me to do before he headed for Colombia. I would have had no clue which towns in Colombia should be added and where I could retrieve their weather data (which is often in a foreign language).

[With the kind permission of these participating students, we are able to release the photos in this blog post.]

Geothermal simulation in Energy3D

Fig.1: Annual air and ground temperatures (daily averages)
A building exchanges heat not only with the outside air but also with the ground. The ground temperature depends on the location and the depth. At six meters under and deeper, the temperature remains almost constant throughout the year. That constant temperature roughly equals to the mean annual air temperature, which depends on the latitude.
Fig.2: Daily air and ground temperatures on 7/1

The ground temperature has a variation pattern different from that of the air temperature. You may experience this difference when you walk into the basement of a house from the outside in the summer or in the winter at different times of the day.

For our Energy3D CAD software to account for the heat transfer between a building and the ground at any time of a year at the 220 worldwide locations that it currently supports, we must develop a physical model for geothermal energy. While there is an abundance of weather data, we found very little ground data (ground data are, understandably, more difficult and expensive to collect). In the absence of real-world data, we have to rely on mathematical modeling.

Fig.3: Daily air and ground temperatures on 1/1
This mission was accomplished in Version 4.9.3 of Energy3D, which can now simulate the heat transfer with the ground. This geothermal model also opens up the possibility to simulate ground source heat pumps -- a promising clean energy solution, in Energy3D (which aims to ultimately include various renewable energy sources in its design capacity to support energy engineering).

Exactly how the math works can be found in the User Guide. In this blog post, I will show you some results. Figure 1 shows the daily averages of the air and ground temperatures throughout the year in Boston, MA. There are two notable features of this graph: 1) Going more deeply, the temperature fluctuation decreases and eventually diminishes at six meters; and 2) the peaks of the ground temperatures lag behind that of the air temperature, due to the heat capacity of the ground (the ground absorbs a lot of thermal energy in the summer and slowly releases them as the air cools in the fall).

Fig. 4: Four snapshots of heat transfer with the ground on a cold day.
In addition to the annual trend, users can also examine the daily fluctuations of the ground temperatures at different depths. Figure 2 shows the results on July, 1. There are three notable features of this graph: 1) Overall the ground temperature decreases when we go deeper; 2) the daily fluctuation of the ground temperature decreases when we go deeper; and 3) the peaks of the ground temperatures lag behind the peak of air temperature. Figure 3 shows the results on January 1 with a similar trend, except that the ground temperatures are higher than the air temperature.

Figure 4 shows four snapshots of the heat transfer between a house and the ground at four different times (12 am, 6 am, 12 pm, and 6 pm) on January 1. The figure shows arrays of heat flux vectors that represent the direction and magnitude of heat flow. To exaggerate the visualization, the R-values of the floor insulation and the windows were deliberately set to be low. If you observe carefully, you will find that the change in the magnitude of the heat flux vectors into the ground lags behind that of those into the air.

The geothermal model also includes parameters that allow users to choose the physical properties of the ground, such as thermal diffusivity. For example, Dry land tends to have smaller thermal diffusivity than wet land. With these properties, geology also becomes a design factor, making the already interdisciplinary Energy3D software even more so.

The National Science Foundation awards grant to study virtual worlds that afford knowledge integration

The Concord Consortium is proud to announce a new project funded by the National Science Foundation, “Towards virtual worlds that afford knowledge integration across project challenges and disciplines.” Principal Investigator Janet Kolodner and Co-PI Amy Pallant will explore how the design of project challenges and the contexts in which they are carried out can support knowledge integration, sustained engagement, and excitement. The goal is to learn how to foster knowledge integration across disciplines when learners encounter and revisit phenomena and processes across several challenges.

Aerial Geography and Air QualityIn this model, students explore the effect of wind direction and geography on air quality as they place up to four smokestacks in the model.

We envision an educational system where learners regularly engage in project-based education within and across disciplines, and in and out of school. We believe that, with such an educational approach, making connections across learning experiences should be possible in new and unexplored ways. If challenges are framed appropriately and their associated figured worlds (real and virtual) and scaffolding are designed to afford it, such education can help learners integrate the content and practices they are learning across projects and across disciplines. “Towards virtual worlds” will help move us towards this vision.

This one-year exploratory project focuses on the possibilities for knowledge integration when middle schoolers who have achieved water ecosystems challenges later attempt an air quality challenge. Some students will engage with EcoMUVE, where learners try to understand why the fish in a pond are dying, and others will engage with Living Together from Project-Based Inquiry Science (PBIS), where learners advise about regulations that should be put in place before a new industry is allowed to move into a town. A subset of these students will then encounter specially crafted air quality challenges based on High-Adventure Science activities and models. These, we hope, will evoke reminders of experiences during their water ecosystem work. We will examine what learners are reminded of, the richness of their memories, and the appeal for learners of applying what they are learning about air quality to better address the earlier water ecology challenge. Research will be carried out in Boston area schools.

Sideview Pollution Control Devices In this model, students explore the effects of installing pollution control devices, such as scrubbers and catalytic converters, on power plants and cars. Students monitor the level of primary pollutants (brown line) and secondary pollutants (orange line) in the model over time, via the graph.

The project will investigate:

  1. What conditions give rise to intense and sustained emotional engagement?
  2. What is remembered by learners when they have (enthusiastically) engaged with a challenge in a virtual figured world and reflected on it in ways appropriate to learning, and what seems to affect what is remembered?
  3. How does a challenge and/or virtual world need to be configured so that learners notice—while not being overwhelmed by—phenomena not central to the challenge but still important to making connections with content outside the challenge content?

Our exploration will help us understand more about the actual elements in the experiences of learners that lead to different emotional responses and the impacts of such responses on their memory making and desires.

Lessons we learn about conditions under which learners form rich memories and want to go back and improve their earlier solutions to challenges will form some of the foundations informing how to design virtual worlds and project challenges with affordances for supporting knowledge integration across projects and disciplines. Exemplar virtual worlds and associated project challenges will inform design principles for the design and use of a new virtual world genre — one with characteristics that anticipate cross-project and cross-discipline knowledge integration and ready learners for future connection making and knowledge deepening.