Abstract
With the massive growth of online learning, there has been a decrease in students' face-to-face interactions, leading to rising feelings of isolation. This in turn contributes to several issues such as motivation loss, increased course attrition rates and poor learning experiences. Strong Online Learning Communities (OLCs) have been suggested as a means to help improve the situation, however the formation of OLCs is strongly influenced by learners' individual characteristics and their preferences regarding how and with whom they would want to form study groups. Taking students as its focus, this research attempts to develop a learning partner recommender system (LPRS) to facilitate finding compatible study peers in order to promote informal learning communities among students. From a synthesis of related literature and using data from a study of the student' preferences, a collection of learners' individual characteristics has been identified as a set of matching criteria in our LPRS model. A proof of concept based on the conceptual model has been developed and evaluated with a small group of target users. Results of the investigation showed positive feedback from participants and good prospects of the recommender system.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 50th ACM Technical Symposium on Computer Science Education |
| Editors | Sarah Heckman, Jian Zhang |
| Place of Publication | New York NY USA |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1102-1108 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781450358903 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | ACM Technical Symposium on Computer Science Education 2019 - Minneapolis, United States of America Duration: 27 Feb 2019 → 2 Mar 2019 Conference number: 50th https://sigcse2019.sigcse.org/ |
Conference
| Conference | ACM Technical Symposium on Computer Science Education 2019 |
|---|---|
| Abbreviated title | SIGCSE 2019 |
| Country | United States of America |
| City | Minneapolis |
| Period | 27/02/19 → 2/03/19 |
| Internet address |
Keywords
- Learning Partners
- Online Learning Communities
- Recommender Systems
Cite this
}
Perfect match : facilitating study partner matching. / Nguyen Thanh, Tam; Butler, Matthew; Morgan, Michael; Marriott, Kim.
Proceedings of the 50th ACM Technical Symposium on Computer Science Education. ed. / Sarah Heckman; Jian Zhang. New York NY USA : Association for Computing Machinery (ACM), 2019. p. 1102-1108.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Perfect match
T2 - facilitating study partner matching
AU - Nguyen Thanh, Tam
AU - Butler, Matthew
AU - Morgan, Michael
AU - Marriott, Kim
PY - 2019
Y1 - 2019
N2 - With the massive growth of online learning, there has been a decrease in students' face-to-face interactions, leading to rising feelings of isolation. This in turn contributes to several issues such as motivation loss, increased course attrition rates and poor learning experiences. Strong Online Learning Communities (OLCs) have been suggested as a means to help improve the situation, however the formation of OLCs is strongly influenced by learners' individual characteristics and their preferences regarding how and with whom they would want to form study groups. Taking students as its focus, this research attempts to develop a learning partner recommender system (LPRS) to facilitate finding compatible study peers in order to promote informal learning communities among students. From a synthesis of related literature and using data from a study of the student' preferences, a collection of learners' individual characteristics has been identified as a set of matching criteria in our LPRS model. A proof of concept based on the conceptual model has been developed and evaluated with a small group of target users. Results of the investigation showed positive feedback from participants and good prospects of the recommender system.
AB - With the massive growth of online learning, there has been a decrease in students' face-to-face interactions, leading to rising feelings of isolation. This in turn contributes to several issues such as motivation loss, increased course attrition rates and poor learning experiences. Strong Online Learning Communities (OLCs) have been suggested as a means to help improve the situation, however the formation of OLCs is strongly influenced by learners' individual characteristics and their preferences regarding how and with whom they would want to form study groups. Taking students as its focus, this research attempts to develop a learning partner recommender system (LPRS) to facilitate finding compatible study peers in order to promote informal learning communities among students. From a synthesis of related literature and using data from a study of the student' preferences, a collection of learners' individual characteristics has been identified as a set of matching criteria in our LPRS model. A proof of concept based on the conceptual model has been developed and evaluated with a small group of target users. Results of the investigation showed positive feedback from participants and good prospects of the recommender system.
KW - Learning Partners
KW - Online Learning Communities
KW - Recommender Systems
UR - http://www.scopus.com/inward/record.url?scp=85064389962&partnerID=8YFLogxK
U2 - 10.1145/3287324.3287344
DO - 10.1145/3287324.3287344
M3 - Conference Paper
SP - 1102
EP - 1108
BT - Proceedings of the 50th ACM Technical Symposium on Computer Science Education
A2 - Heckman, Sarah
A2 - Zhang, Jian
PB - Association for Computing Machinery (ACM)
CY - New York NY USA
ER -