Identifying and Validating Java Misconceptions Toward a CS1 Concept Inventory

Author(s)

Caceffo, Ricardo ; Frank-Bolton, Pablo ; Souza, Renan ; Azevedo, Rodolfo ; ACM

• Document type: Journal article
• Source: PROCEEDINGS OF THE 2019 ACM CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION (ITICSE '19); v. N/A, p. 7-pg., 2019-01-01.

Abstract

A misconception is a common misunderstanding that students may have about a specific topic. The identification, documentation, and validation of misconceptions is a long and time-consuming work, usually carried out using iterative cycles of students answering open-ended questionnaires, interviews with instructors and students, exam analysis, and discussion with experts. A comprehensive list of validated misconceptions in some subject can be used to build formal evaluation methods like the Concept Inventory (CI), a multiple-choice questionnaire that is usually performed as pre-post tests in order to assess any change in student understanding. In CS1, validated misconceptions were identified and documented in C and Python programming languages. Although there are studies related to misconceptions in the Java language, these misconceptions lack the formality, comprehensiveness, and robustness of their C and Python counterparts. On this work, we propose a methodology to adapt the validated misconceptions in C and Python to Java. Initially, through the analysis of an initial list of 33 misconceptions in C and 28 in Python, we identified and documented in an antipattern format 31 possible misconceptions in Java. We then developed a final term exam, composed of 7 open-ended questions, in which each question was designed to address some of the misconceptions covered in the course (N = 27). Through the analysis of the exams answers (N = 69 students), it was possible to validate 22 of the misconceptions (81%). Also, 6 new misconceptions were identified, leading to a total of 28 valid misconceptions in Java. (AU)

• FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
• Grantee: Munir Salomao Skaf
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 14/07502-4 - An educational methodology for promoting active learning through a dynamic content classroom
• Grantee: Ricardo Edgard Caceffo
• Support Opportunities: Scholarships in Brazil - Post-Doctoral