Improve Student Participation in Peer Assessment to Influence Learning Outcomes: A Case Study

  • Antonella CarbonaroEmail author
  • Roberto Reda
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


A variety of technology enhanced teaching strategies and learning activities have been applied in education, including assessment mechanisms. In this paper, we aim to examine the extent to which peer assessment promotes deep learning and favours the incremental learning of the concepts presented throughout a traditional introductory programming course. In particular, we want to examine how the enactment of the peer-assessor and peer-assessee roles is associated with students’ learning improvements, after enacting reciprocal peer assessment. We illustrate a case study based on a novel web-based peer assessment tool to improve engagement and learning outcomes in a programming course. We want to assess whether programming skills are growing through peer review and whether exposure to different programming techniques is helpful to students. One of the most remarkable findings of our experience was that students reported that assessing others’ work was an extremely valuable learning activity.


  1. 1.
    Carbonaro, A.: Good practices to influence engagement and learning outcomes on a traditional introductory programming course. Interact. Learn. Environ. (2018)Google Scholar
  2. 2.
    Carbonaro, A.: Collaborative and semantic information retrieval for technology-enhanced learning. In: Social Information Retrieval for Technology-Enhanced Learning, p. 535 (2009)Google Scholar
  3. 3.
    Carbonaro, A., Ravaioli, M.: Peer assessment to promote deep learning and to reduce a gender gap in the traditional introductory programming course. J. E-Learn. Knowl. Soc. 13(3) (2017)Google Scholar
  4. 4.
    Ala-Mutka, K.M.: A survey of automated assessment approaches for programming assignments. Comput. Sci. Educ. 15(2), 83–102 (2005)CrossRefGoogle Scholar
  5. 5.
    Surendra, G., Dubey, S.K.: Automatic assessment of programming assignment. Comput. Sci. Eng. 2(1), 67 (2012)Google Scholar
  6. 6.
    Ihantola, P., et al.: Review of recent systems for automatic assessment of programming assignments. In: Proceedings of the 10th Koli Calling International Conference on Computing Education Research. ACM, (2010)Google Scholar
  7. 7.
    Kulkarni, C., et al.: Peer and self assessment in massive online classes. In: Design thinking research, pp. 131-168. Springer International Publishing (2015)Google Scholar
  8. 8.
    Chinn, Donald: Peer assessment in the algorithms course. ACM SIGCSE Bulletin 37(3), 69–73 (2005)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., et al.: A multi-peer assessment platform for programming language learning: considering group non-consensus and personal radicalness. Interact. Learn. Environ. 1–20 (2015)Google Scholar
  10. 10.
    Carbonaro, A., Ferrini, R.: Personalized information retrieval in a semantic-based learning environment. In: Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively, pp. 270–288Google Scholar
  11. 11.
    Sitthiworachart, J., Joy, M.: Deepening computer programming skills by using web-based peer assessment. In: Proceedings of the 4th Annual Conference of the LTSN Centre for Information and Computer Sciences. LTSN Centre for Information and Computer Sciences (2003)Google Scholar
  12. 12.
    Andronico, A., Carbonaro, A., Colazzo, L., Molinari, A., Ronchetti, M., Trifonova, A.: Designing models and services for learning management systems in mobile settings. Mobile and Ubiquitous Information Access, pp. 90–106. Springer, Berlin Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Carbonaro, A., Ferrini, R.: Ontology-based video annotation in multimedia entertainment. In: Consumer Communications and Networking Conference, pp. 1087–1091 (2007)Google Scholar
  14. 14.
    Riccucci, S., Carbonaro, A., Casadei, G.: Knowledge acquisition in intelligent tutoring system: a data mining approach. In: Mexican International Conference on Artificial Intelligence, pp. 1195–1205. SpringerGoogle Scholar
  15. 15.
    Carbonaro, A.: WordNet-based summarization to enhance learning interaction tutoring. J. E-Learn. Knowl. Soc. 6(2), 67–74 (2010)Google Scholar
  16. 16.
    Carbonaro, A.: Interlinking e-learning resources and the web of data for improving student experience. J. E-Learn. Knowl. Soc. 8(2), 33–44 (2012)Google Scholar
  17. 17.
    Reda R., Piccinini, F., Carbonaro, A.: Towards consistent data representation in the IoT healthcare landscape. In: Proceedings of the 2018 International Conference on Digital Health, pp. 5–10. ACM (2018) Google Scholar
  18. 18.
    Carbonaro, A.: Improving web search and navigation using summarization process. Commun. Comput. Inf. Sci. 111 CCIS (PART 1), 131–138 (2010)Google Scholar
  19. 19.
    Kuh, D.G., Cruce, T.M., Shoup, R., Kinzie, J., Gonyea, R.M.: Unmasking the effects of student engagement on first-year college grades and persistence. J. High. Educ. 79(5) (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly

Personalised recommendations