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

doi:10.1109/ICALT.2019.00044

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

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

6 Citations (Scopus)

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 language	English
Title of host publication	Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019
Editors	Maiga Chang, Demetrios G Sampson, Ronghuai Huang, Alex Sandro Gomes, Nian-Shing Chen, Ig Ibert Bittencourt, Kinshuk , Diego Dermeval, Ibsen Mateus Bittencourt
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	145-149
Number of pages	5
ISBN (Electronic)	9781728134857
ISBN (Print)	9781728134864
DOIs	https://doi.org/10.1109/ICALT.2019.00044
Publication status	Published - 2019
Event	IEEE International Conference on Advanced Learning Technologies 2019 - Maceio, Brazil Duration: 15 Jul 2019 → 18 Jul 2019 Conference number: 19th http://www.ic.ufal.br/evento/icalt2019/ https://ieeexplore.ieee.org/xpl/conhome/8809605/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Advanced Learning Technologies 2019
Abbreviated title	ICALT 2019
Country/Territory	Brazil
City	Maceio
Period	15/07/19 → 18/07/19
Internet address	http://www.ic.ufal.br/evento/icalt2019/ https://ieeexplore.ieee.org/xpl/conhome/8809605/proceeding (Proceedings)

Keywords

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

Access to Document

10.1109/ICALT.2019.00044

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

Olivares, Daniel ; Adesope, Olusola ; Hundhausen, Christopher ; Ferreira , Rafael ; Rolim, Vitor ; Gasevic, Dragan. / Using social network analysis to measure the effect of learning analytics in computing education. Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019. editor / Maiga Chang ; Demetrios G Sampson ; Ronghuai Huang ; Alex Sandro Gomes ; Nian-Shing Chen ; Ig Ibert Bittencourt ; Kinshuk ; Diego Dermeval ; Ibsen Mateus Bittencourt. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 145-149

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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.",

keywords = "Computer science education, Computer Supported Collaborative Learning, Learning analytics, Social network analysis",

author = "Daniel Olivares and Olusola Adesope and Christopher Hundhausen and Rafael Ferreira and Vitor Rolim and Dragan Gasevic",

year = "2019",

doi = "10.1109/ICALT.2019.00044",

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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, DG Sampson, R Huang, AS Gomes, N-S Chen, II Bittencourt, K, D Dermeval & IM Bittencourt (eds), Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019., 8820924, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 145-149, IEEE International Conference on Advanced Learning Technologies 2019, Maceio, Brazil, 15/07/19. https://doi.org/10.1109/ICALT.2019.00044

Using social network analysis to measure the effect of learning analytics in computing education. / Olivares, Daniel; Adesope, Olusola; Hundhausen, Christopher; Ferreira , Rafael; Rolim, Vitor; Gasevic, Dragan.

Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019. ed. / Maiga Chang; Demetrios G Sampson; Ronghuai Huang; Alex Sandro Gomes; Nian-Shing Chen; Ig Ibert Bittencourt; Kinshuk; Diego Dermeval; Ibsen Mateus Bittencourt. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 145-149 8820924.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Using social network analysis to measure the effect of learning analytics in computing education

AU - Olivares, Daniel

AU - Adesope, Olusola

AU - Hundhausen, Christopher

AU - Ferreira , Rafael

AU - Rolim, Vitor

AU - Gasevic, Dragan

N1 - Conference code: 19th

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Computer science education

KW - Computer Supported Collaborative Learning

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KW - Social network analysis

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BT - Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019

A2 - Chang, Maiga

A2 - Sampson, Demetrios G

A2 - Huang, Ronghuai

A2 - Gomes, Alex Sandro

A2 - Chen, Nian-Shing

A2 - Bittencourt, Ig Ibert

A2 - null, Kinshuk

A2 - Dermeval, Diego

A2 - Bittencourt, Ibsen Mateus

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE International Conference on Advanced Learning Technologies 2019

Y2 - 15 July 2019 through 18 July 2019

ER -

Olivares D, Adesope O, Hundhausen C, Ferreira R, Rolim V, Gasevic D. Using social network analysis to measure the effect of learning analytics in computing education. In Chang M, Sampson DG, Huang R, Gomes AS, Chen N-S, Bittencourt II, K, Dermeval D, Bittencourt IM, editors, Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 145-149. 8820924 https://doi.org/10.1109/ICALT.2019.00044

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

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