Enhancing Personalization Within ASSISTments

Donnelly, Christopher

Etd

Enhancing Personalization Within ASSISTments

Public

ASSISTments is an online adaptive tutoring system with the ability to provide assistance to students in the form of hints and scaffolding. ASSISTments has many features to help students improve their knowledge. Researchers run studies in order to discover ways for students to learn better but ASSISTments is missing one major aspect for researchers: student level personalization. It is easy to create an assignment for a particular class or school but it would take much longer to create an assignment for each student and it would be difficult for the teacher to look through many assignment reports. One of the strongest code blocks in coding is the if-then; allowing the program to branch off to another set of code under certain circumstances. ASSISTments needed an if-then system in order for students to branch off to other parts of the assignment under certain circumstances. With this, researchers would be able to personalize assignments to give more help to lower knowledge students or allow students to get a choice of what kind of tutoring they would like to receive. With this idea in mind, the basic if-then structure was implemented into ASSISTments using problem or problem set correctness as the condition statement. Once the if-then system was created opportunities opened to create additional experiments and run studies in ASSISTments. The basic if-then was limited in using correctness only for its condition statement. This meant that a new if-then system would need to be implemented to include custom condition statements that allowed the researcher to have the assignment branch on any condition using all the information recorded in the assignment. While work was being done on the if-then system, research was being done and two papers were written on partial credit in ASSISTments. Partial credit was found out to be as accurate as knowledge tracing in determining student performance on the next problem. Once a partial credit algorithm was found, a study using if-then was analyzed. It was found that there was no statistically significant difference between students who were given a choice on their feedback and students who received no choice.

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