Predicting performance in an introductory computer science course
Communications of the ACM
What best predicts computer proficiency?
Communications of the ACM
Predicting the success of freshmen in a computer science major
Communications of the ACM
Contributing to success in an introductory computer science course: a study of twelve factors
Proceedings of the thirty-second SIGCSE technical symposium on Computer Science Education
Interacting factors that predict success and failure in a CS1 course
Working group reports from ITiCSE on Innovation and technology in computer science education
Predicting student exam's scores by analyzing social network data
AMT'12 Proceedings of the 8th international conference on Active Media Technology
CoBAn: A context based model for data leakage prevention
Information Sciences: an International Journal
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In the past, predicting students grades has been done primarily by looking at students past test scores, at their history [1, 2], by looking at their current notes and/or their note taking ability [3, 5], from surveys about various other aspects of their background, or by a combination of such factors [4, 6, 7]. In this paper a different approach is taken -- here, the complexity of the teacher's lecture notes is examined as a predictor of the students' grades. The results of this research indicate that simple measures such as total number of words in the lecture or total number of lines of code that appear in the lecture are not good predictors of students' grades; however, "buzzword density," or the total number of words with Computer Science meaning divided by the total number of words in the lecture does predict students' grades.