Tag Archives: Visual Analytics

Listen to the data with the Visual Process Analytics


Visual analytics provides a powerful way for people to see patterns and trends in data by visualizing them. In real life, we use both our eyes and ears. So can we hear patterns and trends if we listen to the data?

I spent a few days studying the JavaScript Sound API and adding simple data sonification to our Visual Process Analytics (VPA) to explore this question. I don't know where including the auditory sense to the analytics toolkit may lead us, but you never know. It is always good to experiment with various ideas.


Note that the data sonification capabilities of VPA is very experimental at this point. To make the matter worse, I am not a musician by any stretch of the imagination. So the generated sounds in the latest version of VPA may sound horrible to you. But this represents a step forward to better interactions with complex learner data. As my knowledge about music improves, the data should sound less terrifying.

The first test feature added to VPA is very simple: It just converts a time series into a sequence of notes and rests. To adjust the sound, you can change a number of parameters such as pitch, duration, attack, decay, and oscillator types (sine, square, triangle, sawtooth, etc.). All these options are available through the context menu of a time series graph.

At the same time as the sound plays, you can also see a synchronized animation of VPA (as demonstrated by the embedded videos). This means that from now on VPA is a multimodal analytic tool. But I have no plan to rename it as data visualization is still and will remain dominant for the data mining platform.

The next step is to figure out how to synthesize better sounds from multiple types of actions as multiple sources or instruments (much like the Song from Pi). I will start with sonifying the scatter plot in VPA. Stay tuned.

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.

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).

Time series analysis tools in Visual Process Analytics: Autocorrelation

Autocorrelation reveals a three-minute periodicity
Digital learning tools such as computer games and CAD software emit a lot of temporal data about what students do when they are deeply engaged in the learning tools. Analyzing these data may shed light on whether students learned, what they learned, and how they learned. In many cases, however, these data look so messy that many people are skeptical about their meaning. As optimists, we believe that there are likely learning signals buried in these noisy data. We just need to use or invent some mathematical tricks to figure them out.

In Version 0.2 of our Visual Process Analytics (VPA), I added a few techniques that can be used to do time series analysis so that researchers can find ways to characterize a learning process from different perspectives. Before I show you these visual analysis tools, be aware that the purpose of these tools is to reveal the temporal trends of a given process so that we can better describe the behavior of the student at that time. Whether these traits are "good" or "bad" for learning likely depends on the context, which often necessitates the analysis of other co-variables.

Correlograms reveal similarity of two time series.
The first tool for time series analysis added to VPA is the autocorrelation function (ACF), a mathematical tool for finding repeating patterns obscured by noise in the data. The shape of the ACF graph, called the correlogram, is often more revealing than just looking at the shape of the raw time series graph. In the extreme case when the process is completely random (i.e., white noise), the ACF will be a Dirac delta function that peaks at zero time lag. In the extreme case when the process is completely sinusoidal, the ACF will be similar to a damped oscillatory cosine wave with a vanishing tail.

An interesting question relevant to learning science is whether the process is autoregressive (or under what conditions the process can be autoregressive). The quality of being autoregressive means that the current value of a variable is influenced by its previous values. This could be used to evaluate whether the student learned from the past experience -- in the case of engineering design, whether the student's design action was informed by previous actions. Learning becomes more predictable if the process is autoregressive (just to be careful, note that I am not saying that more predictable learning is necessarily better learning). Different autoregression models, denoted as AR(n) with n indicating the memory length, may be characterized by their ACFs. For example, the ACF of AR(2) decays more slowly than that of AR(1), as AR(2) depends on more previous points. (In practice, partial autocorrelation function, or PACF, is often used to detect the order of an AR model.)

The two figures in this post show that the ACF in action within VPA, revealing temporal periodicity and similarity in students' action data that are otherwise obscure. The upper graphs of the figures plot the original time series for comparison.

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.