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Predicting Grades

Author(s):

Y. Meier
Conference/Journal:

Semester Thesis, FS15 (10417)
Abstract:

To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. Existing grade prediction systems focus on maximizing the accuracy of the prediction while overseeing the importance of issuing timely and personalized predictions. In this semester thesis we consider a course which is taught for several years with only slight modifications. Students attending the course have to complete performance assessments such as graded homework assignments and in-class exams throughout the entire course. Our goal is to predict for each student individually with a certain confidence his overall performance before all performance assessments have been taken. The performance of the algorithm will be shown through simulations on actual data from an undergraduate digital signal processing course that has been taught at UCLA for about ten years. We will implement several well-known benchmark algorithms to compare their performance to the performance of our algorithm.

Supervisors: Jie Xu, UCLA, Onur Atan, UCLA, Mihaela van der Schaar, UCLA, Florian Dörfler

Year:

2015
Type of Publication:

(13)Semester/Bachelor Thesis
Supervisor:

F. Dörfler

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2015:IFA_5341
}
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