Computer Science 590ED - EducDataMining&LearnerAnalytic

The primary goal of this course is for students to be comfortable with exploring educational datasets and analyzing them, finding patterns in educational data, creating models that summarize and describe data, and creating predictive models, using a variety of techniques that span machine learning, data mining, and statistics. We will work with datasets that come from educational learning technologies, tutoring and assessment software, as well as other datasets that students might be interested in (e.g. datasets from students grades classes, many of which are publicly available through national sources). Students are encouraged to bring their own datasets of interest, if they have them or want to. Another goal is to understand how current researchers in the field of educational data mining, AI in Education, and educational technology are using a variety of techniques to understand and model their data. We will cover many different methods, from machine learning methods applied to educational data and classic models inspired by cognitive theory. We will also cover various applications including computerized adaptive testing, affect detection, reading and writing analysis, activity log analysis, and instructional policy design. This course counts as a CS Elective for the BS or BA.

Open to Graduate Computer Science students only. CROSS-LISTED WITH EDUC 590ED. COMPSCI LECT 01 FOR UNDERGRADS; LECT 02 FOR GRADS; EDUC SECTION FOR GRADS. PREREQUISITE EXPLAINED: MATH 545 AND COMPSCI 240 AND STATS 515 C OR BETTER. MATH 545 CAN BE SKIPPED BY STUDENTS WHO HAVE TAKEN MATH 235 AND MATH 233 BOTH WITH A B+ OR BETTER. STATS 515 CAN BE SKIPPED BY STUDENTS WHO HAVE TAKEN COMPSCI 240 WITH A B+ OR BETTER. STUDENTS NEEDING SPECIAL PERMISSION MUST REQUEST OVERRIDES VIA THE ON-LINE FORM: https://www.cics.umass.edu/overrides.