After listening to Mr. Daniel Dimian speak about the use of Web Grid, I find it quit easy to get my project started. I have to admit that coming up with a topic for this Web Grid is not easy for me. After a long wrestling, I decided on exploring the topic of music.
I selected Classical music, Rock, Heavy Metal, Popular music, Jazz and Rap as my elements to compare over the scale of 1 to 5. I find creating constructs for this topic is not easy. I love listening to classical music and know very little about other music. The first pair of constructs I used was Loud/Pleasant. As I progressed, I discovered that Web Grid actually helped me to come up with constructs to differentiate the type of music I have chosen. From the FOCUS, I found that Rap and Heavy Metal are closely related and are very different from Jazz and Classical Music. I also think that heavy music tends to appeal to certain types of audience. These results also reflected from the PrinCom analysis, where the Heavy/Light and Particular Group Of Audience/Wider Audience lines were correlated together whereas the rest were are scattered around the other quadrant of the graph.
After exploring the use of Web Grid, I tried to find constructs that are more general and will fit all the topics, although it was not easy. I used Less User Interaction/Heavy User Interaction and Platform Dependent/Not Platform Dependent as pairs of construct as I found them suitable for most of the topics that we covered in our course. I also used constructs such as Make Use Of Video/Do Not Make Use Of Video to differentiate the topics which I could not come up with a better one at the time.
I rated all the topics against 11 constructs that I had created and compared my results with the original grid. There were quite a few terms that I used which were similar to the original grid. I was expecting to see similar rating of each topic on my Artificial Intelligence/No Artificial Intelligence to Intelligent System/Conventional system but I found we strongly disagreed in the topics Virtual Reality and Visual Programming. However, we reached a consensus. I also found that I termed certain properties differently from that of the original grid. This was reflected in the Graphical User Interface related/Not Graphical User Interface related and Targeted on interface/Targeted on overall system. Generally, my terminology usage in describing the topics were agreeable to the original grid.
I had chosen AppWare, VisSim, KidSim, Visual Basic, and C as my target programming languages. The first three are visual programming languages. AppWare are more commenly used in the business world while KidSim and VisSim have only very limited user group. Visual Basic sounds like one of the visual programming language but it is not, and C is clearly conventional textual programming language.
The FOCUS analysis showed that I have repeated myself in definding Visual Programming Language/Not Visual Programming Language and Visual Programming/Textual Programming. Textual Programming languages seemed to be rich in functionality, fast in execution but hard to learn. VisSim and KidSim were similar languages and AppWare was of the same kind. These three languages had nothing similar to Visual Basic or C. The analysis also showed that Visual Basic and C were not totally the same.
The PrinCom disgram was somewhat scattered. However, it was concentrated at the constructs descripbing functionality, kind of programming language, user group and popularity. Specific User Group, Visual Programming, and Limited Functionality were best description for the three visual programming languages from the diagram. Difficulty in programming and GUI enhancement were two that were quit different in describing the languages. Unlike the three visual programming languages, C and Visual Basic here were quite far at the border of the graph.