Tag Archives: Machine Learning

What’s new in Visual Process Analytics Version 0.3


Visual Process Analytics (VPA) is a data mining platform that supports research on student learning through using complex tools to solve complex problems. The complexity of this kind of learning activities of students entails complex process data (e.g., event log) that cannot be easily analyzed. This difficulty calls for data visualization that can at least give researchers a glimpse of the data before they can actually conduct in-depth analyses. To this end, the VPA platform provides many different types of visualization that represent many different aspects of complex processes. These graphic representations should help researchers develop some sort of intuition. We believe VPA is an essential tool for data-intensive research, which will only grow more important in the future as data mining, machine learning, and artificial intelligence play critical roles in effective, personalized education.

Several new features were added to Version 0.3, described as follows:

1) Interactions are provided through context menus. Context menus can be invoked by right-clicking on a visualization. Depending on where the user clicks, a context menu provides the available actions applicable to the selected objects. This allows a complex tool such as VPA to still have a simple, pleasant user interface.

2) Result collectors allow users to gather analysis results and export them in the CSV format. VPA is a data browser that allows users to navigate in the ocean of data from the repositories it connects to. Each step of navigation invokes some calculations behind the scenes. To collect the results of these calculations in a mining session, VPA now has a simple result collector that automatically keeps track of the user's work. A more sophisticated result manager is also being conceptualized and developed to make it possible for users to manage their data mining results in a more flexible way. These results can be exported if needed to be analyzed further using other software tools.

3) Cumulative data graphs are available to render a more dramatic view of time series. It is sometimes easier to spot patterns and trends in cumulative graphs. This cumulative analysis applies to all levels of granularity of data supported by VPA (currently, the three granular levels are Top, Medium, and Fine, corresponding to three different ways to categorize action data). VPA also provides a way for users to select variables from a list to be highlighted in cumulative graphs.

Many other new features were also added in this version. For example, additional information about classes and students are provided to contextualize each data set. In the coming weeks, the repository will incorporate data from more than 1,200 students in Indiana who have undertaken engineering design projects using our Energy3D software. This unprecedented large-scale database will potentially provide a goldmine of research data in the area of engineering design study.

For more information about VPA, see my AERA 2016 presentation.

Visual Process Analytics (VPA) launched


Visual Process Analytics (VPA) is an online analytical processing (OLAP) program that we are developing for visualizing and analyzing student learning from complex, fine-grained process data collected by interactive learning software such as computer-aided design tools. We envision a future in which every classroom would be powered by informatics and infographics such as VPA to support day-to-day learning and teaching at a highly responsive level. In a future when every business person relies on visual analytics every day to stay in business, it would be a shame that teachers still have to read through tons of paper-based work from students to make instructional decisions. The research we are conducting with the support of the National Science Foundation is paving the road to a future that would provide the fair support for our educational systems that is somehow equivalent to business analytics and intelligence.

This is the mission of VPA. Today we are announcing the launch of this cyberinfrastructure. We decided that its first version number should be 0.1. This is just a way to indicate that the research and development on this software system will continue as a very long-term effort and what we have done is a very small step towards a very ambitious goal.


VPA is written in plain JavaScript/HTML/CSS. It should run within most browsers -- best on Chrome and Firefox -- but it looks and works like a typical desktop app. This means that while you are in the middle of mining the data, you can save what we call "the perspective" as a file onto your disk (or in the cloud) so that you can keep track of what you have done. Later, you can load the perspective back into VPA. Each perspective opens the datasets that you have worked on, with your latest settings and results. So if you are half way through your data mining, your work can be saved for further analyses.

So far Version 0.1 has seven analysis and visualization tools, each of which shows a unique aspect of the learning process with a unique type of interactive visualization. We admit that, compared with the daunting high dimension of complex learning, this is a tiny collection. But we will be adding more and more tools as we go. At this point, only one repository -- our own Energy3D process data -- is connected to VPA. But we expect to add more repositories in the future. Meanwhile, more computational tools will be added to support in-depth analyses of the data. This will require a tremendous effort in designing a smart user interface to support various computational tasks that researchers may be interested in defining.

Eventually, we hope that VPA will grow into a versatile platform of data analytics for cutting-edge educational research. As such, VPA represents a critically important step towards marrying learning science with data science and computational science.

Learning analytics is the "crystallography" for educational research

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."