Flashcard Data Structure Application
 Project Goal
Most Applications on the XO use a binary data set to quiz students:
- Image Quiz uses an image map: string binary pair
- Kuku uses either an image: string pair, or a string: string pair
- Memory uses an image:image set
Unfortunately to edit the set of information used for the quiz programs you have to have a reasonable facility with Python. I would like to create a standard file type that could be passed between applications. I will also create an application to create such a file format.
The other side of this problem involves areas of information where questions could be generated by a computer, as in areas of math, and language learning. Another goal would be to make the standard file type work to creating random questions.
This not only solves the problem of making applications more easily customizable, and opens the door for non-technical individuals to create learning materials for the laptops. But also could enable programs like scratch to be used by students to create activity type programs for the XOs. In a few hours I made a replica of kuku running in scratch. Media:Number Eater.sb It has a few bugs for example, it's possible for the grid to be populated with no correct answers, but generally works correctly. It is mainly to illustrate the fact that scratch can be used by kids to create educational tools that enable them to further take control of their own educations. Also the inability to create functions, and create "sprites" in a script made it more difficult than necessary.
Create a serialized data type (Ideally to handle text, images, image maps, sound, and video, as well as rules that could generate other questions) Edit existing programs to work with data type Create a GUI to create such a data file
 Further Directions
It would also be helpful to create an output progress file that would keep track of student's correct and incorrect answers for each flashcard data file. This file could be used by teachers to more easily keep track of large numbers of students. It could also be used to identify students who are having similar problems, and students who understand concepts well and automatically create student groups based on those classifications. Such classifications could also be used to customize the test questions given to be only questions that the student was having difficulty with. These classifications could be enhanced using techniques in machine learning.