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Work in progress: A First-Year Common Course on Computational Problem Solving and Programming

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2014 ASEE Annual Conference & Exposition


Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014



Conference Session

Computers in Education Division Poster Session

Tagged Division

Computers in Education

Page Count


Page Numbers

24.1383.1 - 24.1383.17



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Paper Authors


Bruce W. Char Drexel University (Computing)

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Bruce Char is Professor of Computer Science in the Department of Computing of the College of Computing and Informatics at Drexel University.

Thomas Hewett is Professor of Psychology (emeritus) and Computer Science at Drexel University.

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Thomas T. Hewett Drexel University

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Tom Hewett is Professor Emeritus of Psychology and of Computer Science at Drexel University. His teaching included courses on Cognitive Psychology, Problem Solving and Creativity, the Psychology of Human-Computer Interaction, and the Psychology of Interaction Design. In addition, he has taught one-day professional development courses at both national and international conferences, and has participated in post-academic training for software engineers. Tom has worked on the design and development of several software projects and several pieces of commercial courseware. Some research papers have focused on the evaluation of interactive computing systems and the impact of evaluation on design. Other research papers have explored some of the pedagogical and institutional implications of universal student access to personal computers. In addition, he has given invited plenary addresses at international conferences. Tom chaired the ACM SIGCHI Curriculum Development Group which proposed the first nationally recognized curriculum for the study of Human-Computer Interaction. Tom's conference organizing work includes being Co-Chair of the CHI ‘94 Conference on Human Factors in Computing Systems and Program Chair for the 2013 Creativiey and Cognition Conference.

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Work in progress: A first year common course on computational problemsolving and programming (Abstract)Introduction We created an entry-level course on computational problem-solving andprogramming for engineering students. It has been run since 2006Course objectives The course balances a need for having computer skills useful immediately infirst-year studies with the need for building a conceptual foundation to facilitate subsequentlearning in the multiple courses and experiences needed to reach professional skill levels. Moststudents are in a co-op education program, so we are aiming for externally useful effects by theend of the year. Due to course size, efficient use of staff and space resources is also a significantconcern. The course covers computational techniques of problem-solving and programming with800+ students/term, in a mix of closed labs in classrooms, and out-of-class autograded exercises.It runs for one or two credits over several terms. From 2006-2013, it used Maple. A transition toMatlab occurred starting in 2013.Course format 30 lab sections were typically scheduled across five days using 7 instructors (2faculty, 5 grad students) and 10-12 undergraduate assistants. This allowed a 12:1 student/staffratio in lab. Lab grading by staff consists of simple verification of completion. A lab instructor'soverview is given at the start of each lab in lieu of a lecture period. Students' pre-lab preparationconsists of reading on-line course materials verified by a pre-lab on-line quiz . Post-lab on-linequizzes are supported by drop-in clinics and electronic discussion groups. Roughly 120autograded problems were created by the staff for the course in Maple TA. Many problems showa different variant for each student, and required non-trivial amounts of analysis or softwaredevelopment to answer. These were also reused for in-class proctored proficiency exams.Interim observations Work on autograded problems peaks during evenings and afternoons.Autograded problems tended to spur “retry until success”, with successful completion rates forproblems typically in excess of 80 or 90% in the second and third terms. Problem variants madeit possible to offer them them as practice before proficiency exams that used them, and allowedthem to be used in a collection of exams given over many time periods. They also led to havingmost staff time spent face-to-face instruction and tutorage. However, unproctored practice withautograded problems did not necessarily lead all to mastering the material. Some chose to relyon help to satisfy the autograder rather than doing the cognitive work leading to mastery. Thisdiscrepancy was typically uncovered by the in-class exams.On going revision and refinement. The transition to Matlab programming has also broughtallocation of additional time and staff resources for more lab time, a weekly one hour lecture,and manually grading of some assignments and projects. We look to find effective ways ofcombining the immediate but limited feedback of autograded exercises with the potentiallydeeper but slower feedback from staff grading. We look to student-selected project-based workto spur greater student responsibility and motivation for learning. We continue to investigatehow the style, coverage, and automated prescription autograded exercises affect studentlearning. We are also plan to extend the use of autograded problems for proctored certification ofspecific Matlab skills using Open Badges. We hope this will allow students, if they choose, toadvertise their achievement of such skills in a verifiable and more detailed way than a coursegrade on a transcript.

Char, B. W., & Hewett, T. T. (2014, June), Work in progress: A First-Year Common Course on Computational Problem Solving and Programming Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--23316

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