Many Eyes is a data visualization tool created by IBM’s CUE Visual Communication Lab. Users of the site upload data sets or use already uploaded data sets to create visualizations of the data. The tool can create 21 different types of graphs, maps, plots, and charts and can analyze texts, compare sets of values, show relationships among data points, visualize parts of a whole (i.e. pie charts), track data over time, and create maps where regions are shaded based on data values. All data uploaded to the site become public (there is no private option) and IBM retains the license to all intellectual property rights for any visualizations you create.
Type of Material:
This could be considered a development tool. It is actually a specialized and limited statistical software tool. It is also a data repository and discussion forum for visualization.
Instructors could upload data sets and have students create visualizations for an assignment. Students could use the site for project data that they collect themselves.
Internet browser and connection. Users must register for the site to upload data. Registration is free.
Identify Major Learning Goals:
Students can explore new visualization tools and understand data better from a new angle. The site encourages exploration and alternative ways of presenting data.
Target Student Population:
This site can be used by all levels of statistics students and by non-statisticians. It is powerful and easy to use.
Prerequisite Knowledge or Skills:
Basic computer skills and an introductory understanding of data visualization are all that is needed to get started. More advanced visualizations will require additional statistical understanding.
Evaluation and Observation
The resource contains a big collection of data visualizing tools including the traditional scatter plot to more advanced ones such as tag cloud. It also supports self-upload data and discussion forum. Content-wise the module is very comprehensive. The software is powerful, and the site features make it easy to use. The variety of visualizations encourage exploration and experimentation with data.
This is just a tool. Any lessons or learning material using the tool would have to be created from scratch by an instructor. This is a better tool for research/project purposes than for introductory lectures on the topic. The users need to have some background to understand and appreciate the functions of some of the tools provided.
Potential Effectiveness as a Teaching Tool
This is not a module, only a tool. It encourages exploration and student discovery. With appropriately written assignments, it could help with conceptual understanding. The model is comprehensive and collects a lot of visualization tools from classic to advanced. Some tools are very interesting and not normally used in basic statistics textbooks. For example, the tag cloud or the phrase net can be used in teaching Natural Language Processing.
The model is not designed for teaching. It does not identify learning objectives or reinforce concepts progressively. It is more like a stand-alone repository and discussion forum for the researchers who are interested in data visualization. Introductory students would need very structured assignments and explanations to use this tool effectively. More advanced students will be able to get more out of it.
Ease of Use for Both Students and Faculty
It’s easy to get up and running with the software. The interface is very intuitive and visual, and the software has very powerful data recognition tools.
There is no systematic demonstration of concepts and theory. This is not a ready-for-the-classroom resource. Instructors will need to think hard about how to use this in a statistics course. This module may be better used in research than teaching. There is reason to be concerned about the licensing agreement. There is no option for private data—everything uploaded to the site becomes publicly available, which brings up issues of copyright and privacy. This is not a ready-for-the-classroom resource. Instructors will need to think hard about how to use this in a statistics course.