Improving pedagogical feedback and objective grading

ABSTRACT

It is important for learning that students receive enough of educational feedback of their work. To get the students to be seriously disposed to the feedback it has to be personal, objective and consistent. In large classes ensuring such feedback can be difficult. Grading rubrics are a solution to the objectivity and consistency.

ALOHA is an online grading tool based on rubrics which all the graders have to use. Particularly, ALOHA provides features that make the grading process more convenient for the graders and the teacher. By facilitating the graders work ALOHA allows them to focus more on feedback writing.

To test the effectiveness of ALOHA in objectivity and consistency we did a comparative statistical analysis on the distribution of grades. The results supported the assumptions showing improvement resulting in similar distribution of grades amongst different graders who used the tool.

REFERENCES

• 1: Tuukka Ahoniemi , Tommi Reinikainen, ALOHA - a grading tool for semi-automatic assessment of mass programming courses, Proceedings of the 6th Baltic Sea conference on Computing education research: Koli Calling 2006, February 01-01, 2006, Uppsala, Sweden [doi>10.1145/1315803.1315830]
• 2: Kirsti Ala-Mutka , Hannu-Matti Jarvinen, Assessment Process for Programming Assignments, Proceedings of the IEEE International Conference on Advanced Learning Technologies, p.181-185, August 30-September 01, 2004
• 3: K. M. Ala-Mutka. A survey of automated assessment approaches for programming assignments. Computer Science Education, 15(2):83--102, June 2005.
• 4: Katrin Becker, Grading programming assignments using rubrics, Proceedings of the 8th annual conference on Innovation and technology in computer science education, June 30-July 02, 2003, Thessaloniki, Greece
• 5: Christopher Douce , David Livingstone , James Orwell, Automatic test-based assessment of programming: A review, Journal on Educational Resources in Computing (JERIC), v.5 n.3, p.4-es, September 2005 [doi>10.1145/1163405.1163409]
• 6: Heidi J. C. Ellis, Self-grading: an approach to supporting self-directed learning, Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education, June 26-28, 2006, Bologna, Italy
• 7: S. Habeshaw, G. Gibbs, and T. Habeshaw. 53 Problems With Large Classes. Technical and Educational Services Ltd., Bristol, U.K., 1992.
• 8: John Hamer , Catherine Kell , Fiona Spence, Peer assessment using aropä, Proceedings of the ninth Australasian conference on Computing education, p.43-54, January 30-February 02, 2007, Ballarat, Victoria, Australia
• 9: A. Trivedi , Dulal C. Kar , Holly Patterson-McNeill, Automatic assignment management and peer evaluation, Journal of Computing Sciences in Colleges, v.18 n.4, p.30-37, April 2003
• 10: T. Winters and T. Payne. Computer aided grading with Agar. In H. R. Arabnia, editor, FECS, pages 245--251. CSREA Press, 2006.

INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education

General Terms:
Algorithms, Design, Human Factors

Keywords:
assessment, grading, programming, rubrics, teaching