Using social network analysis to measure the effect of learning analytics in computing education

Daniel Olivares, Olusola Adesope, Christopher Hundhausen, Rafael Ferreira , Vitor Rolim, Dragan Gasevic

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

Abstract

Student retention and learning in STEM disciplines is a growing problem. The 2012 report by the US President's Council of Advisors on Science and Technology (PCAST) predicts a future deficit in science, engineering, and mathematics (STEM) in the following decade and emphasizes the importance of addressing this issue. With this as a motivating factor, the OSBLE+ Social Programming Environment (SPE) was used to leverage social and programming data for the basis of automatically generated prompts inserted into the SPE. These prompts were designed to stimulate help-seeking, help-giving, and social interaction in the learning environment. A social network analysis was performed in order to determine whether exposure to the automated interventions would positively affect the relationship among students over time. Results of this study suggest that students in the experimental treatment who were presented with automated prompts developed more connected and social networks than those in the control treatment.

Original languageEnglish
Title of host publicationProceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019
EditorsMaiga Chang, Demetrios G Sampson, Ronghuai Huang, Alex Sandro Gomes, Nian-Shing Chen, Ig Ibert Bittencourt, Kinshuk , Diego Dermeval, Ibsen Mateus Bittencourt
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages145-149
Number of pages5
ISBN (Electronic)9781728134857
ISBN (Print)9781728134864
DOIs
Publication statusPublished - 2019
EventIEEE International Conference on Advanced Learning Technologies 2019 - Maceio, Brazil
Duration: 15 Jul 201918 Jul 2019
Conference number: 19th
http://www.ic.ufal.br/evento/icalt2019/

Conference

ConferenceIEEE International Conference on Advanced Learning Technologies 2019
Abbreviated titleICALT 2019
CountryBrazil
CityMaceio
Period15/07/1918/07/19
Internet address

Keywords

  • Computer science education
  • Computer Supported Collaborative Learning
  • Learning analytics
  • Social network analysis

Cite this

Olivares, D., Adesope, O., Hundhausen, C., Ferreira , R., Rolim, V., & Gasevic, D. (2019). Using social network analysis to measure the effect of learning analytics in computing education. In M. Chang, D. G. Sampson, R. Huang, A. S. Gomes, N-S. Chen, I. I. Bittencourt, K., D. Dermeval, & I. M. Bittencourt (Eds.), Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019 (pp. 145-149). [8820924] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICALT.2019.00044