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Personalized Attention @ Scale: Talk Isn't Cheap, But It's Effective

Published:24 February 2015Publication History

ABSTRACT

Fostering an effective learning environment in large classes is a challenge: instructors and teaching assistants are stretched thin across many students, students often lack opportunities for personal interaction with course staff, and the size of the classes makes them seem impersonal. Furthermore, students in large classes can often find solutions to their labs and assignments online or copy them from other students, diminishing their impetus to learn and raising plagiarism concerns.

This paper describes our experience and evaluation of an assessment method that resolves many of these problems and appears to scale to large classes of 600+ students. Using this method, students are evaluated via a combination of automatic grading mechanisms (or clear objective rubrics) and a 1-on-1 "grading interview". The grading interview serves to ensure the provenance of the student's work product and their depth of understanding. This change allows us to make more effective use of peer-instruction and pair-programming in our courses. It also provides the ability to re-use assignments, the insurance of timely feedback to students, and the opportunity for individualized staff attention.

This paper describes variations on this method across numerous classes over the past seven years, some of the goals of this method, modifications and adaptations of the method over time, and the student experience of using this method based on survey feedback.

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  1. Personalized Attention @ Scale: Talk Isn't Cheap, But It's Effective

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        cover image ACM Conferences
        SIGCSE '15: Proceedings of the 46th ACM Technical Symposium on Computer Science Education
        February 2015
        766 pages
        ISBN:9781450329668
        DOI:10.1145/2676723

        Copyright © 2015 ACM

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        New York, NY, United States

        Publication History

        • Published: 24 February 2015

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        SIGCSE '15 Paper Acceptance Rate105of289submissions,36%Overall Acceptance Rate1,595of4,542submissions,35%

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