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Utilization of Rubrics for Self-assessment of Computer Science Students Enrolled in a Research Writing Course

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Published:05 May 2017Publication History

ABSTRACT

This study 1 determined the utilization of rubrics in self-assessment of Computer Science students who underwent research writing course. Two sections with a total of 69 students served as participants of the study. The grades of the teacher in the course, oral defense grades, panel members' individual ratings on the students, and self-assessment rating using rubrics were collected by the researchers. Paired sample t-test was employed to determine if self-assessment ratings were significantly different from the teacher's grade, oral defense grade, and panel members' individual ratings on the students. Moreover, self-rating assessment was investigated between low and high performing students. Informal interviews with five students were conducted to explain their self-assessment ratings. All rejected are the three null hypotheses stating that self-rating assessments are not significantly different from the teacher's grade, oral defense grade, and panel members' individual ratings on the students. Meanwhile, the fourth null hypothesis stating that there is no significant difference between the self-assessment rating of the low-performing and high-performing students in the research writing course was accepted. Thus, the statistical analyses revealed that students tend to inflate the self-assessment rating in order to achieve higher grades regardless of their classroom performance. The study described the utilization of self-assessment rubrics of computer science students enrolled in a research writing class. Recommendations and limitations were also discussed.

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  1. Utilization of Rubrics for Self-assessment of Computer Science Students Enrolled in a Research Writing Course

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      cover image ACM Other conferences
      WCCCE '17: Proceedings of the 22nd Western Canadian Conference on Computing Education
      May 2017
      42 pages
      ISBN:9781450350662
      DOI:10.1145/3085585

      Copyright © 2017 ACM

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      Publication History

      • Published: 5 May 2017

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      Overall Acceptance Rate78of117submissions,67%

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