Visual analytics for uncovering complex learning: Part II

Figure 1. Most students performed well throughout.

In an early article, I have introduced the idea of using graph theory to visualize learning. In this follow-up article, I will give an example based on our recent classroom study with our mixed-reality technology that engaged 65 high school students in three chemistry classes to learn the kinetic molecular theory through exploring gas laws. The primary task of the study is to investigate how a novel kind of mixed-reality technology that combines actions in and across real and virtual worlds can help students develop molecular reasoning skills to explain macroscopic phenomena with microscopic theories.

Figure 2. Less successful than Figure 1.

The key is to study students' interactions with the technology in great details in order to shed light on how the mixed-reality experience may help students make micro-macro connections. For most students, there is a huge gap between their macro perception and their micro conception. We want to fill the gap using this powerful real-time technology to provide just-in-time, integrated instruction.

Figure 3. Performed well initially and then went astray.

The challenge is to find a way to describe both the interaction process and the reasoning process. Our software logs everything students are doing behind the scenes (i.e., we can track students' hands). Meanwhile, through our embedded assessment using causality maps (a type of concept map), we can track students' minds. Our objective is to find any conceptual changes from the "mind-tracking" data and attribute them to the "hand-tracking" data. Only after we establish this association can we evaluate the worth of the mixed-reality technology in fostering science learning.

Both the "hand-tracking" and "mind-tracking" data are being studied using graph theory. In this article, we will show the analysis of the "mind-tracking" data first. The data were generated by students connecting 6-7 provided molecular concepts between a cause and an effect to compose an explanation. Some of the concepts are irrelevant and used as distractors. For example, increasing the number of molecules has nothing to do with the molecular mass.

Given the fact that there are 65 students and four causality maps, we need a way to quickly visualize student learning. Our solution is to construct graphs based on these data. It becomes immediately obvious that this visual analytics can provide extremely informative graphics that shows student learning on a statistical basis. The three images in this article show that: 1) Most students in Class E performed well throughout Graph 3; 2) Students are less successful in Class B in Graph 1; and 3) Students in Class D performed well in Graph 4 at the beginning and then went astray -- largely because this is a more difficult challenge that involves multiple reasoning paths. (The gray bands in the three images represent the correct reasoning chains in each case. The thickness of an edge represents the number of students who drew the link in the concept map.) These graphs can potentially show the weaknesses of the students at the scale of the whole class. I could not help thinking how useful this would be to the teacher if such informative feedback is provided to her.

This graph-based analytics is NOT just visual -- It is also interactive. You can examine all the student data we have collected at this page, programmed in Dart -- the latest Web programming language from Google. The page provides filters for you to examine and compare any number of students' work. You can also drag the nodes around for clarity. This kind of feedback tool is what teachers would want to have and what we should create for them. The world has spent billions of dollars building visual business analytics to assist business executives to make investment decisions day to day. How about spending a bit money to build an infographics system to assist teachers to make instructional decisions day to day?

Dart projects of Energy2D and Quantum Workbench announced

Update: I have decided to suspend these Dart projects due to the uncertainty of the future of the language following Google's decision to not bundle the Dart VM into Chrome. At this point, I have to fall back to JavaScript.

Last month, Google announced Dart 1.0, a new programming language for the Web that aims to greatly accelerate Web development. Dart uses HTML5 as the UI. It can either run on the Dart Virtual Machine being built in Chrome or be compiled into JavaScript to run in other browsers. Dart can also be used to create standalone apps (I guess it is meant to be the main programming language for Google's own Chrome OS) or server-side software. An ECMA Technical Committee (TC 52) has been formed to make Dart into an international standard.

This is the moment I have been waiting for. As a developer with C/Java background, I am not convinced that JavaScript is made for large, complex projects (as Web programming seems to be moving towards) -- even after reading many articles and books about JavaScript. The facts that after ten years Google Docs still has only a tiny fraction of functionality of Word and basic functions such as positioning an image have not improved much suggest that its JavaScript front end has probably reached its limit.

Don't get me wrong. JavaScript is an excellent choice for creating interactive Web experiences. I use JavaScript extensively to create Web interfaces for interacting with the Energy2D applet. But I think it is in general healthy for the developer community if we are given more options. Recognizing the weaknesses of JavaScript, the community has already created CoffeeScript and TypeScript (supersets of JavaScript that strips off unproductive features of JavaScript) that also require compilation into native JavaScript. Dart is Google's solution to these problems that should be welcomed. To a Java developer like me, Dart provides a much better option because it returns the power of class-based object-oriented programming to developers who must create Web-based front ends. What is even sweeter is that its SDK provides a familiar Eclipse-based programming platform that makes many developers feel at home.

Excited about the potential of this new language (plus it is from Google and will be highly performant on Chrome), I am announcing the development of the Dart versions of our Energy2D and Quantum Workbench software. These software are based on complex mathematical solutions of extremely complex partial differential equations and will hopefully provide some showcases to anyone interested in Dart. This is not to say the development of the Java versions will cease. We are committed to develop and maintain both Dart and Java versions.

Hopefully 2014 will be an exciting year for us!