Title

A Data-Driven Analysis Of Informatively Hard Concepts In Introductory Programming

Keywords

Dimension extraction; Introductory programming education; Multiobjective optimization; Performance analysis; Problets

Abstract

What are the concepts in introductory programming that are easy/hard for students? We propose to use Dimension Extraction algorithm (DECA) inspired by coevolution and co-optimization theory to answer this question. We propose and use the metrics of informatively easy/hard concepts to identify programming concepts that are solved correctly by the most "dominated student" versus solved incorrectly by the most "dominant student". As a proof of concept, we applied DECA to analyze the data collected by software tutors called problets used by introductory programming students in Spring 2014. We present the results, i.e., informatively easy/hard concepts on a dozen different topics covered in a typical introductory programming course. It is hoped that these results will inform programming instructors on the concepts they should (de)/emphasize in class. They will also contribute towards creating a concept inventory for introductory programming.

Publication Date

2-17-2016

Publication Title

SIGCSE 2016 - Proceedings of the 47th ACM Technical Symposium on Computing Science Education

Number of Pages

370-375

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2839509.2844629

Socpus ID

84968662596 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84968662596

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