Category Archives: Main Blog

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.

Play Roshambo
(log in as guest)

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.

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.]

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.

Simulating the Hadley Cell using Energy2D

Download the models
Although it is mostly used as an engineering tool, our Energy2D software can also be used to create simple Earth science simulations. This blog post shows some interesting results about the Hadley Cell.

The Hadley Cell is an atmospheric circulation that transports energy and moisture from the equator to higher latitudes in the northern and southern hemispheres. This circulation is intimately related to the trade winds, hurricanes, and the jet streams.

As a simple way to simulate zones of ocean that have different temperatures due to differences in solar heating, I added an array of constant-temperature objects at the bottom of the simulation window. The temperature gradually decreases from 30 °C in the middle to 15 °C at the edges. A rectangle, set to be at a constant temperature of -20 °C, is used to mimic the high, chilly part of the atmosphere. The viscosity of air is deliberately set to much higher than reality to suppress the wild fluctuations for a somehow averaged effect. The results show a stable flow pattern that looks like a cross section of the Hadley Cell, as is shown in the first image of this post.

When I increased the buoyant force of the air, an oscillatory pattern was produced. The system swings between two states shown in the second and third images, indicating a periodic reinforcement of hot rising air from the adjacent areas to the center (which is supposed to represent the equator).

Of course, I can't guarantee that the results produced by Energy2D are what happen in nature. Geophysical modeling is an extremely complicated business with numerous factors that are not considered in this simple model. Yet, Energy2D shows something interesting: the fluctuations of wind speeds seem to suggest that, even without considering the seasonal changes, this nonlinear model already exhibits some kind of periodicity. We know that it is all kinds of periodicity in Mother Nature that help to sustain life on the Earth.

Simulating geometric thermal bridges using Energy2D

Fig. 1: IR image of a wall junction (inside) by Stefan Mayer
One of the mysterious things that causes people to scratch their heads when they see an infrared picture of a room is that the junctions such as edges and corners formed by two exterior walls (or floors and roofs) often appear to be colder in the winter than other parts of the walls, as is shown in Figure 1. This is, I hear you saying, caused by an air gap between two walls. But not that simple! While a leaking gap can certainly do it, the effect is there even without a gap. Better insulation only makes the junctions less cold.

Fig. 2: An Energy2D simulation of thermal bridge corners.
A typical explanation of this phenomenon is that, because the exterior surface of a junction (where the heat is lost to the outside) is greater than its interior surface (where the heat is gained from the inside), the junction ends up losing thermal energy in the winter more quickly than a straight part of the walls, causing it to be colder. The temperature difference is immediately revealed by a very sensitive IR camera. Such a junction is commonly called a geometric thermal bridge, which is different from material thermal bridge that is caused by the presence of a more conductive piece in a building assembly such as a steel stud in a wall or a concrete floor of a balcony.

Fig. 3: IR image of a wall junction (outside) by Stefan Mayer
But the actual heat transfer process is much more complicated and confusing. While a wall junction does create a difference in the surface areas of the interior and exterior of the wall, it also forms a thicker area through which the heat must flow through (the area is thicker because it is in a diagonal direction). The increased thickness should impede the heat flow, right?

Fig. 4: An Energy2D simulation of a L-shaped wall.
Unclear about the outcome of these competing factors, I made some Energy2D simulations to see if they can help me. Figure 2 shows the first one that uses a block of object remaining at 20 °C to mimic a warm room and the surrounding environment of 0 °C, with a four-side wall in-between. Temperature sensors are placed at corners, as well as the middle point of a wall. The results show that the corners are indeed colder than other parts of the walls in a stable state. (Note that this simulation only involves heat diffusion, but adding radiation heat transfer should yield similar results.)

What about more complex shapes like an L-shaped wall that has both convex and concave junctions? Figure 3 shows the IR image of such a wall junction, taken from the outside of a house. In this image, interestingly enough, the convex edge appears to be colder, but the concave edge appears to be warmer!

The Energy2D simulation (Figure 4) shows a similar pattern like the IR image (Figure 3). The simulation results show that the temperature sensor placed near the concave edge outside the L-shape room does register a higher temperature than other sensors.

Now, the interesting question is, does the room lose more energy through a concave junction or a convex one? If we look at the IR image of the interior taken inside the house (Figure 1), we would probably say that the convex junction loses more energy. But if we look at the IR image of the exterior taken outside the house (Figure 3), we would probably say that the concave junction loses more energy.

Which statement is correct? I will leave that to you. You can download the Energy2D simulations from this link, play with them, and see if they help you figure out the answer. These simulations also include simulations of the reverse cases in which heat flows from the outside into the room (the summer condition).

Time series analysis tools in Visual Process Analytics: Cross correlation

Two time series and their cross-correlation functions
In a previous post, I showed you what autocorrelation function (ACF) is and how it can be used to detect temporal patterns in student data. The ACF is the correlation of a signal with itself. We are certainly interested in exploring the correlations among different signals.

The cross-correlation function (CCF) is a measure of similarity of two time series as a function of the lag of one relative to the other. The CCF can be imagined as a procedure of overlaying two series printed on transparency films and sliding them horizontally to find possible correlations. For this reason, it is also known as a "sliding dot product."

The upper graph in the figure to the right shows two time series from a student's engineering design process, representing about 45 minutes of her construction (white line) and analysis (green line) activities while trying to design an energy-efficient house with the goal to cut down the net energy consumption to zero. At first glance, you probably have no clue about what these lines represent and how they may be related.

But their CCFs reveal something that appears to be more outstanding. The lower graph shows two curves that peak at some points. I know you have a lot of questions at this point. Let me try to see if I can provide more explanations below.

Why are there two curves for depicting the correlation of two time series, say, A and B? This is because there is a difference between "A relative to B" and "B relative to A." Imagine that you print the series on two transparency films and slide one on top of the other. Which one is on the top matters. If you are looking for cause-effect relationships using the CCF, you can treat the antecedent time series as the cause and the subsequent time series as the effect.

What does a peak in the CCF mean, anyways? It guides you to where more interesting things may lie. In the figure of this post, the construction activities of this particular student were significantly followed by analysis activities about four times (two of them are within 10 minutes), but the analysis activities were significantly followed by construction activities only once (after 10 minutes).

The National Science Foundation funds SmartCAD—an intelligent learning system for engineering design

We are pleased to announce that the National Science Foundation has awarded the Concord Consortium, Purdue University, and the University of Virginia a $3 million, four-year collaborative project to conduct research and development on SmartCAD, an intelligent learning system that informs engineering design of students with automatic feedback generated using computational analysis of their work.

Engineering design is one of the most complex learning processes because it builds on top of multiple layers of inquiry, involves creating products that meet multiple criteria and constraints, and requires the orchestration of mathematical thinking, scientific reasoning, systems thinking, and sometimes, computational thinking. Teaching and learning engineering design becomes important as it is now officially part of the Next Generation Science Standards in the United States. These new standards mandate every student to learn and practice engineering design in every science subject at every level of K-12 education.
Figure 1

In typical engineering projects, students are challenged to construct an artifact that performs specified functions under constraints. What makes engineering design different from other design practices such as art design is that engineering design must be guided by scientific principles and the end products must operate predictably based on science. A common problem observed in students' engineering design activities is that their design work is insufficiently informed by science, resulting in the reduction of engineering design to drawing or crafting. To circumvent this problem, engineering design curricula often encourage students to learn or review the related science concepts and practices before they try to put the design elements together to construct a product. After students create a prototype, they then test and evaluate it using the governing scientific principles, which, in turn, gives them a chance to deepen their understanding of the scientific principles. This common approach of learning is illustrated in the upper image of Figure 1.

There is a problem in the common approach, however. Exploring the form-function relationship is a critical inquiry step to understanding the underlying science. To determine whether a change of form can result in a desired function, students have to build and test a physical prototype or rely on the opinions of an instructor. This creates a delay in getting feedback at the most critical stage of the learning process, slowing down the iterative cycle of design and cutting short the exploration in the design space. As a result of this delay, experimenting and evaluating "micro ideas"--very small stepwise ideas such as those that investigate a design parameter at a time--through building, revising, and testing physical prototypes becomes impractical in many cases. From the perspective of learning, however, it is often at this level of granularity that foundational science and engineering design ultimately meet.

Figure 2
All these problems can be addressed by supporting engineering design with a computer-aided design (CAD) platform that embeds powerful science simulations to provide formative feedback to students in a timely manner. Simulations based on solving fundamental equations in science such as Newton’s Laws model the real world accurately and connect many science concepts coherently. Such simulations can computationally generate objective feedback about a design, allowing students to rapidly test a design idea on a scientific basis. Such simulations also allow the connections between design elements and science concepts to be explicitly established through fine-grained feedback, supporting students to make informed design decisions for each design element one at a time, as illustrated by the lower image of Figure 1. These scientific simulations give the CAD software tremendous disciplinary intelligence and instructional power, transforming it into a SmartCAD system that is capable of guiding student design towards a more scientific end.

Despite these advantages, there are very few developmentally appropriate CAD software available to K-12 students—most CAD software used in industry not only are science “black boxes” to students, but also require a cumbersome tool chaining of pre-processors, solvers, and post-processors, making them extremely challenging to use in secondary education. The SmartCAD project will fill in this gap with key educational features centered on guiding student design with feedback composed from simulations. For example, science simulations can be used to analyze student design artifacts and compute their distances to specific goals to detect whether students are zeroing in towards those goals or going astray. The development of these features will also draw upon decades of research on formative assessments of complex learning.