Analysing graduation project rubrics using machine learning techniques

Goksu Tuysuzoglu, Nazanin Moarref, Zehra Cataltepe, Ayse Tosun Misirli, Yusuf Yaslan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

When grading a student's performance, determining the assessment factors is a substantial step in course evaluation. The aim of this paper is to improve the quality of the assessment criteria for our Computer Engineering Department's graduation reports. We employ machine learning methods to identify the most important evaluation rubrics that affect the overall grade given to graduation projects. First, we eliminate the redundant factors by computing the correlations between them. Second, we apply K-Means & Hierarchical Clustering methods and third, we analyze the proportion of variance values to find the sufficient amount of eigen values to explain the data. Our results show that Overall Performance is the most important, whereas References is the least important evaluation rubric affecting the graduation project grades. The techniques we use can be used to analyze the graduation rubric grading practices and also to come up with an equivalent rubric with smaller set of questions.

Original languageEnglish
Title of host publication10th International Conference on Computer Science and Education, ICCSE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-24
Number of pages6
ISBN (Electronic)9781479965984
DOIs
Publication statusPublished - 9 Sept 2015
Event10th International Conference on Computer Science and Education, ICCSE 2015 - Cambridge, United Kingdom
Duration: 22 Jul 201524 Jul 2015

Publication series

Name10th International Conference on Computer Science and Education, ICCSE 2015

Conference

Conference10th International Conference on Computer Science and Education, ICCSE 2015
Country/TerritoryUnited Kingdom
CityCambridge
Period22/07/1524/07/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • ABET
  • Clustering
  • Correlation
  • Graduation Project Rubrics
  • Hierarchical Clustering
  • K-Means Clustering
  • Proportion of Variance

Fingerprint

Dive into the research topics of 'Analysing graduation project rubrics using machine learning techniques'. Together they form a unique fingerprint.

Cite this