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WORLDCOMP'14 Tutorial: Dr. Dale E. Parson

Last modified 2014-07-15 21:29


Using Weka to Mine Temporal Work Patterns of Programming Students
Dr. Dale E. Parson
Kutztown University of Pennsylvania
Kutztown, Pennsylvania, USA

Date: July 22, 2014, 2:00pm - 3:00pm
Location: Ballroom 4


DESCRIPTION

    This one-hour tutorial is a follow up to the FECS’14 presentation of the paper, “Mining Student Time Management Patterns in Programming Projects.” The paper abstract states, “Computer science faculty members cite procrastination as one of the key causes of poor student performance in programming projects. In contrast, students cite conflicting demands for time. This study uses a tool-driven process of automated compilation and testing of student programs to collect student-project data. Data include when, for how long, how often, and with what magnitude of effort and accomplishment, students engage in work to complete programming assignments. Participation is voluntary, and data from auxiliary sources, including a questionnaire on conflicting demands on time, complement automatically collected data. Analyses reveal that procrastination and excessively brief work sessions are the main indicators of problems for students with inadequate prior success in earlier computer science courses. Some students with successful track records know when they can afford late starts and short sessions. The time of day that students work is a contributing factor to success. The goal is to build an automated warning system for at-risk students.” The tutorial provides an overview of how to use data preparation scripts written using GNU Make and Python, and how to use the Weka data mining tools, to perform the analyses of student programmer performance data used in the study.


OBJECTIVES

    The objective is to provide attendees interested in performing analyses similar to that reported in the regular FECS lecture entitled “Mining Student Time Management Patterns in Programming Projects” an opportunity to learn how to use the software tools and machine learning algorithms employed in the study.

INTENDED AUDIENCE

    Attendees of FECS track and other WorldComp participants who are interested in mining the work habits of programming students and using the Weka toolset to analyze patterns in those habits.

BIOGRAPHY OF INSTRUCTOR

    Dr. Dale E. Parson is an associate professor of computer science who has completed his sixth year of teaching at Kutztown University of PA. He earned his doctorate in computer science at Lehigh University in 1990, and spent most of his career as a computer scientist with Bell Laboratories. When not teaching or attending meetings, he likes to work on his primary area of research, computer music and its visualization. He is also interested in novel approaches to understanding students, teaching computer science, and collaborating with undergraduate researchers.

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