Archive for the ‘Main Blog’ Category

On the instructional sensitivity of computer-aided design logs

July 20th, 2014 by Charles Xie
Figure 1: Hypothetical student responses to an intervention.
In the fourth issue this year, the International Journal of Engineering Education published our 19-page-long paper on the instructional sensitivity of computer-aided design (CAD) logs. This study was based on our Energy3D software, which supports students to learn science and engineering concepts and skills through creating sustainable buildings using a variety of built-in design and analysis tools related to Earth science, heat transfer, and solar energy. This paper proposed an innovative approach of using response functions -- a concept borrowed from electrical engineering -- to measure instructional sensitivity from data logs (Figure 1).

Many researchers are interested in studying what students learn through complex engineering design projects. CAD logs provide fine-grained empirical data of student activities for assessing learning in engineering design projects. However, the instructional sensitivity of CAD logs, which describes how students respond to interventions with CAD actions, has never been examined, to the best of our knowledge.
Figure 2. An indicator of statistical reliability.

For the logs to be used as reliable data sources for assessments, they must be instructionally sensitive. Our paper reports the results of our systematic research on this important topic. To guide the research, we first propose a theoretical framework for computer-based assessments based on signal processing. This framework views assessments as detecting signals from the noisy background often present in large temporal learner datasets due to many uncontrollable factors and events in learning processes. To measure instructional sensitivity, we analyzed nearly 900 megabytes of process data logged by Energy3D as collections of time series. These time-varying data were gathered from 65 high school students who solved a solar urban design challenge using Energy3D in seven class periods, with an intervention occurred in the middle of their design projects.

Our analyses of these data show that the occurrence of the design actions unrelated to the intervention were not affected by it, whereas the occurrence of the design actions that the intervention targeted reveals a continuum of reactions ranging from no response to strong response (Figure 2). From the temporal patterns of these student responses, persistent effect and temporary effect (with different decay rates) were identified. Students’ electronic notes taken during the design processes were used to validate their learning trajectories. These results show that an intervention occurring outside a CAD tool can leave a detectable trace in the CAD logs, suggesting that the logs can be used to quantitatively determine how effective an intervention has been for each individual student during an engineering design project.

Simulating PTC and NTC heating elements with Energy2D

June 23rd, 2014 by Charles Xie
Figure 1: A demo simulation.
A heating element converts electricity into heat through Joule heating: Electric current passing through the element encounters resistance, causing the temperature of the element to rise. A thermistor is a type of resistor whose resistance changes significantly with temperature. In a heating element that uses a thermistor with a positive temperature coefficient (PTC), called a PTC heating element, the temperature increases rapidly. In a heating element that uses a thermistor with a negative temperature coefficient (NTC), called a NTC heating element, the heating will gradually weaken when the temperature increases.

Figure 2: Setting the temperature coefficient.
Several Energy2D users have requested adding PTC/NTC controls to the software. So this was added last night. You can now set the temperature coefficient while defining a power source, as shown in Figure 2.

Figure 1 shows the comparison of the temperature increasing in a PTC heater, a constant-power heater, and a NTC heater, with the temperature coefficients being 0.1, 0, and -0.1, respectively. Note that in the case of constant power, the temperature increases linearly in time (as per the definition of constant power), whereas PTC and NTC exhibit nonlinear behaviors.

You can click the link under the image to run the simulation yourself.

Design replay: Reconstruction of students’ engineering design processes from Energy3D logs

June 18th, 2014 by Charles Xie
One of the useful features of our Energy3D software is the ability to record the entire design process of a student behind the scenes. We call the reconstruction of a design process from fine-grained process data design replay.


Design replay is not a screencast technology. The main difference is that it records a sequence of CAD models, not in any video format such as MP4. This sequence is played back in the original CAD tool that generated it, not in a video player. As such, every snapshot model is fully functional and editable. For instance, a viewer can pause the replay and click on the user interface of the CAD tool to obtain or visualize more information, if necessary. In this sense, design replay can provide far richer information than screencast (which records as much information as the pixels in the recording screen permit).


Design replay provides a convenient method for researchers and teachers to quickly look into students' design work. It compresses hours of student work into minutes of replay without losing any important information for analyses. Furthermore, the reconstructed sequence of design can be post-processed in many ways to extract additional information that may shed light on student learning, as we can use any model in the recorded sequence to calculate any of its properties.



The three videos embedded in this post show the design replays of three students' work from a classroom study that we just completed yesterday in a Massachusetts high school. Sixty-seven students spent approximately two weeks designing zero-energy houses -- a zero-energy house is a highly energy-efficient house that consumes net zero (or even negative) energy over a year due to its use of passive and active solar technologies to conserve and generate energy. These videos may give you a clue how these three students solved the design challenge.

Psychology and Climate Change

May 28th, 2014 by Andy Zucker

The Nobel Prize winner Doris Lessing, author of more than 50 books, had a rare talent for writing in different styles about a variety of people, from adolescent boys, to lonely old women, to kings and queens living on another planet. She had a brilliant novelist’s intuitive understanding of other people’s minds.

In her slender 1987 non-fiction book, Prisons We Choose to Live Inside, Lessing wrote about growth in scientists’ understanding of psychology, and the need to apply that knowledge to public affairs. Ever since, more and more high-quality popular books on psychology have been published, including Thinking Fast and Slow, The Righteous Mind, The Black Swan, and many others. Yet in the case of climate change, Lessing’s plea that we pay closer attention to psychology has grown more imperative. Humanity cannot address species-threatening climate problems without better understanding the ways we think.

As one significant example, earlier this month the New York Times reported that the West Antarctic ice sheet is not only melting quickly, but, according to two scientific papers, the melting appears to be irreversible. Over time – fortunately, the time scale is likely to be centuries – sea level will probably rise ten feet or more due to the melting of this single ice sheet. Simultaneously, other climate-related changes will also contribute to rising sea levels. Sea level is about to increase three or four feet in this century alone.

This once-in-a-geological-epoch news item about Antarctic melting ought to have caught the attention of people everywhere. Earth’s population will be in for a rougher ride than expected – and climate scientists have been predicting a rough ride for years. Yet the news of irreversible Antarctic ice melting probably passed unnoticed by a majority of Americans.

At almost the same time, a likely contender for the Presidential nomination of a major political party said that he does not believe human activity is causing climate change, a statement that may seem less shocking when one realizes that fewer than half of Americans think human beings are the primary cause of climate change. What strategies – other than waiting for more climate disasters to strike and hoping that politicians will accept facts – will work best to persuade the public and its representatives that action is needed now? Are there perhaps key groups, such as religious leaders, who are not the typical audience for scientists’ press releases, who might accelerate public understanding and acceptance of climate change?

It is easy to call climate change skeptics ignorant, or worse. But name-calling is seldom the best strategy for changing people’s minds and, what is more, modern psychology has demonstrated that virtually everyone’s thinking is badly flawed in certain situations. To take one example, airplane pilots must be taught to trust their instruments instead of their senses when they cannot see the horizon, and yet every year some crash because they did not learn this lesson. To take another example, some scientists’ first reactions in 1980 to scientific papers about the extinction of the dinosaurs by a giant meteor striking earth – calling the authors “arrogant,” “ignorant,” and “all wrong” – demonstrated prejudices rather than open minds.

Developing a better understanding of the science behind climate change is essential. There is also some work under way to develop better communications strategies, and those efforts are laudable. But mankind is not likely to change its behavior rapidly enough to prevent disaster if we do not learn more about how people think about global warming and apply that knowledge to quicken public action.

Temperature change may not represent heat transfer; heat flux does.

May 4th, 2014 by Charles Xie
Figure 1 (go to simulation)
There has been some confusion lately about the heat transfer representations in Energy2D simulations. By default, Energy2D shows the temperature distribution and uses the change of the distribution to visualize heat flow. This is all good if we have only one type of medium or material. But in reality, different materials have different thermal conductivities and different volumetric heat capacities (i.e., the ability of a given volume of a substance to store thermal energy when the temperature increases by one degree; the volumetric heat capacity is in fact the specific heat multiplied by the density).

A
Figure 2 (go to simulation)
According to the Heat Equation, the change of temperature is affected by the thermal diffusivity, which is the thermal conductivity divided by the volumetric heat capacity (now that I have written the terminology down, I can see why these terms are so confusing). In general, a higher thermal conductivity and a lower volumetric heat capacity will both result in faster temperature change.

To illustrate my points, Figure 1 shows a comparison of temperature changes in two materials. The pieces that have the same texture are made of the same material. The upper ones have a lower thermal conductivity but a higher thermal diffusivity. The lower ones have a higher thermal conductivity but a lower thermal diffusivity. In both upper and lower setups, the piece on the left side maintains a higher temperature to provide the heat source. Everything else starts with a low temperature initially. The entire container is completely insulated -- no heat in, no heat out. Two thermometers are placed just at the right ends of the middle rods. Their results show that the temperature rises more quickly in the upper setup (Figure 1) -- because it has a higher diffusivity.

The fact that something diffuses faster doesn't mean it diffuses more. In order to see that, we can place two heat flux sensors somewhere in the rods to capture the heat flows. Figure 2 shows the results from the heat flux sensors. Obviously, there is a lot more heat flow in the lower setup in the same time period.

Figure 3 (go to simulation)
The conclusion is that it is the heat flux, not the temperature change, that ultimately measures heat transfer. If you want to know how fast heat transfer occurs, the thermal conductivity is a good measure. However, if you want to know how fast temperature changes, the thermal diffusivity is a good measure. This may be also important to remember for those who use infrared cameras: Infrared cameras only measure temperature distribution, so what we really see from infrared images is actually thermal diffusion and thermal diffusion alone could be deceiving.

Figure 4 (go to simulation)
To make this even more fun (or confusing), let's replace the pieces on the right of the container with two pieces that are made of the same material that has a volumetric heat capacity between those of the other upper and lower ones. You wouldn't think this change would affect the results, at least not qualitatively. But the truth is that, the temperature in the lower setup in this case rises more quickly than the temperature in the upper setup -- exactly opposite to the case shown in Figure 1! The surprising result indicates how unreliable temperature change may be as an indicator of heat transfer. In this case, the temperature field of the middle rod is affected by what it is connected with. If we look at the results from the heat flux sensors (Figure 4), the heat flux that goes through the rod is much higher in the lower setup. This once again shows that heat flux is a more reliable measure of heat transfer.

In Energy2D, we have implemented an Energy Field view to supplement the Temperature Field view to remedy this problem.

Building performance analyses in Energy3D

April 6th, 2014 by Charles Xie
Energy3D (Tree image credit: SketchUp Warehouse and Ethan McElroy)
A zero-energy building is a building with zero net energy consumption over a year. In other words, the total amount of energy used by the building on an annual basis is equal to or even less than the amount of renewable energy it produces through solar panels or wind turbines. A building that produces more renewable energy than it consumes over the course of a year is sometimes also called an energy-plus building. Highly energy-efficient buildings hold a crucial key to a sustainable future.


One of the goals of our Energy3D software is to provide a powerful software environment that students can use to learn about how to build a sustainable world (or understand what it takes to build such a world). Energy3D is unique because it is based on computational building physics, done in real time to produce interesting heat map visualization resembling infrared thermography. The connections to basic science concepts such as heat and temperature make the tool widely applicable in schools. Furthermore, at a time when teachers are required by the new science standards to teach basic engineering concepts and skills in classrooms, this tool may be even more relevant and useful. The easy-to-use user interface enables students to rapidly sketch up buildings of various shapes, creating a deep design space that provides many opportunities of exploration, inquiry, and learning.


In the latest version of Energy3D (Version 3.0), students can compute the energy gains, losses, and usages of a building over the course of a year. These data can be used to analyze the energy performance of the building under design. These results can help students decide their next steps in a complex design project. Without these simulation data to rationalize design choices, students' design processes would be speculative or random.

A complex engineering design project usually has many elements and variables. Supporting students to investigate each individual element or variable is key to helping them develop an understanding of the related concept. Situating this investigation in a design project enables students to explore the role of each concept on system performance. With the analytic tools in Energy3D, students can pick an individual building component such as a window or a solar panel and then analyze its energy performance. This kind of analysis can help students determine, for example, where a solar panel should be installed and which direction it should face. The video in this post shows how these analytic tools in Energy3D work.

Spring is here, let there be trees!

March 28th, 2014 by Charles Xie
Trees in Energy3D.
Trees around a house not only add natural beauty but also increase energy efficiency. Deciduous trees to the south of a house let sunlight shine into the house through south-facing windows in the winter while blocking sunlight in the summer, thus providing a simple but effective solution that attains both passive heating and passive cooling using the trees' shedding cycles. Trees to the west and east of a house can also create significant shading to help keep the house cool in the summer. All together, a well-planed landscape can reduce the temperature of a house in a hot day by up to 20°C.

The tree to the south side shades the house in the summer.
With the latest version of Energy3D, students can add trees in designs. As shown in the second image in this blog post, the Solar Irradiation Simulator in Energy3D can visualize how trees shade the house and provide passive cooling in the summer.

The Solar Irradiation Simulator also provides numeric results to help students make design decisions. The calculated data show that the tree to the south of the house is able to reduce the sunlight shined through the window on the first floor that is closest to it by almost 90%. Students can do this easily by adding and removing the tree, re-run the simulation, and then compare the numbers. They will be able to add trees of different heights and types (deciduous or evergreen). There will be a lot of design variables that students can choose and test.

A design challenge is to combine windows, solar panels, and trees to reduce the yearly cost of a building to nearly zero or even negative (meaning that the owner of the house actually makes money by giving unused energy produced by the solar panels to the utility company). This is no longer just a possibility -- it has been a reality, even in a northern state like Massachusetts!

Learning analytics is the "crystallography" for educational research

March 24th, 2014 by Charles Xie
To celebrate 100 years of dazzling history of crystallography, the year of 2014 has been declared by UNESCO as the International Year of Crystallography. To this date, 29 Nobel Prizes have been awarded to scientific achievements related to crystallography. On March 7th, the Science Magazine honored crystallographers with a special issue.

Why is crystallography such a big deal? Because it enables scientists to "see" atoms and molecules and discover the molecular structures of substances. One of the most famous examples is the discovery of the DNA helix by Rosalind Franklin in 1952, followed by Crick, Watson, and Wilkins' double helix model. Enough ink has been spilled on the importance of this discovery.

Science fundamentally relies on techniques such as crystallography for detecting and visualizing invisible things. Educational research needs this kind of techniques, too, to decode students' minds that are opaque to researchers. Up to this point, educational researchers depend on methods such as pre/post-tests, observations, and interviews. But these traditional methods are either insufficient or inefficient for measuring learning in complex processes such as scientific inquiry and engineering design. To achieve a level of truly "no child left behind," we will need to develop a research technique that can monitor every student for every minute in the classroom.

Such a technique has to be based on an integrated informatics system that can engage students with meaningful learning tasks, tease out what are in their minds, and capture every bit of information that may be indicative of learning. This involves development in all areas of learning sciences, including technology, curriculum, pedagogy, and assessment. Eventually, what we have is a comprehensive set of data through which we will sift to find patterns of learning or evaluate the effectiveness of an intervention.

The whole process is not unlike crystallography. At the end, it is the learning analytics that concludes the research. Today we are seeing a lot of learner data, but we probably have no idea what they actually mean. We can either say there is no significance in those data and shrug off, or we can try to figure out the right kind of data analytics to decipher them. Which attitude to choose probably depends on which universe we live in. But the history of crystallography can give us a clue. It was Max von Laue who created the first X-ray diffraction pattern in 1912. He couldn't interpret it, however. It wasn't until William Henry Bragg and William Lawrence Bragg's groundbreaking work later in the same year that scientists became able to infer molecular structures from those patterns. In educational research, the equivalent of this is the learning analytics -- a critical piece that will give data meaning.

For more information, read my new article "Visualizing Student Learning."

Energy3D in France and Energy3D User’s Guide

February 24th, 2014 by Charles Xie
Solar irradiation simulations of urban clusters in Energy3D.
More than four years ago, I blogged about our ideas to develop a computer-aided design (CAD) program for education that is different from SketchUp. We wanted a CAD program that allows students to easily and quickly perform physical analyses to test the functions of their 3D models while constructing them -- in contrast to typical industry practices that involve pre-processing, numerical simulation, and then post-processing. We thought closing the gap between construction and analysis is fundamentally important because students need instantaneous feedback from some authentic scientific computation to guide their next design steps. Without such a feedback loop, students will not be able to know whether their computer designs will function or not -- in the way permitted by science, even if they can design the forms well.

Four years after Saeid Nourian and I started to develop our Energy3D CAD program, we received the following comment from Sébastien Canet, a teacher from Académie de Nantes:
"I am a French STEM teacher and a trainer of technical education teachers in west France. Our teachers loved your software! We were working on an 'eco-quartier' with the goal to use as much passive solar energy as possible. Each student worked with SketchUp to model his/her house and then pasted the model on a map. Then we tested different solar orientations. Your software is a really good complementary tool to SketchUp, though the purposes are not the same. It is fast, easy to use, and perfect for constructing!!! I will use it instead of SketchUp in our activities."

Sébastien wrote that, if we can provide a French version, there would be hundreds of French STEM teachers who will adopt our software through his Académie. We are really happy to know that people have started to compare Energy3D with SketchUp and are even considering using Energy3D instead of SketchUp. This might be a small change to those users who make the switch but it is a big thing to us.

On  a separate note, we just finished the initial version of the User's Guide for Energy3D. We intend this to eventually grow into a book that will be useful to teachers who must, upon the requirement of the Next Generation Science Standards, teach some engineering design in K-12 schools. Our recent experiences working with high school teachers in Massachusetts show the lack of practical engineering materials tailor-made for high school students. As a result, one of the teachers with whom we are collaborating has to use a college textbook on architectural engineering. Perhaps we can provide a book that will fill this gap -- with a student-friendly CAD program to support it.

A high school student’s design work with Energy3D

February 22nd, 2014 by Charles Xie
Cormac Paterson is a student at Arlington High School. We ran into him last year while conducting research in the school. He quickly mastered our Energy3D CAD software. In as short as just five class periods, he came up with three different architectural designs that appear to be very sophisticated and impressive (see the second row in the image). After that, Mr. Paterson continued his creative work with Energy3D. In his latest projects, he designed a Mars colony and a solar tree. Many of his design elements surprised us: As the developers of the CAD software, we didn't even know that it could do those things until we saw his designs!

Thanks to the National Science Foundation, we obtained a bit more funding to deepen our research on engineering design. We are extremely interested in studying Mr. Paterson's gift in architectural design: What makes him such an extraordinary designer as a high school student? Since our Energy3D software can monitor every move of the designer, we may be able to find some clues from the data generated in his design processes.

Note: We are very serious in protecting the privacy of minors. In this case, we have obtained a permission from Mr. Paterson's parent to feature him and his work.