A dropout is a situation where a user lacks the motivation to continue the learning interactions, thereby losing the engagement and eventually stopping the utilization of the Social Learning Management System (sLMS). The term was originally defined for students losing interest in using a sLMS but is generalized in this paper so the term can be applied to all users of sLMS. Several approaches involves artificial intelligence technique like fuzzy cognitive maps are used in conceptualizing feedback mechanisms that improved the students' situation awareness. Another approach might be based on actual user interactions and the computation of centrality scores from social network algorithms. This paper introduces the algorithm, which uses centrality measures to drive automated decision-making so an intervention can be selected when dropout is detected. The conceptualization of the algorithm is helpful in the adaption eforts of schools that are newly acquainted with learning technologies like the sLMS, to eliminate the dropout of the administrator, teachers, students, and parents.
encourages learning interactions from principal, faculty
members, students, and parents. The contribution of this
A social learning management system (sLMS) is an paper is on the utilization of centrality measures in
comapplication that provides functionalities for the conduct ing up the algorithm to engage the user encouraging
of online distance learning with features for social inter- learning interactions.
actions (i.e., video conference, chat, etc.) in one platform.
sLMS can be considered as the environment providing
the information and the users as the one that interprets 2. User Dropout and Interventions
and consequently use the information for present or
future actions. Engagement in sLMS soared high espe- Accordingly, infrastructure factors, cultural factors,
digicially during the time of the pandemic where many of tal inequality, and the threat to digital privacy were cited
the world’s leading institutions adopted the platform for for causing student dropout [
A dropout is a situation where a user lacks the mo- students’ situation awareness. The deployed and tested
tivation to continue the learning interactions, thereby system indicated promising results addressing dropout
losing engagement and eventually stopping utilization [
The intention of this paper is to present an algorithm Automated Intervention that utilizes centrality measures to perform tasks that
Joint Proceedings of the ACM IUI Workshops 2023, March 2023, Sydney, serve as another method for developing automated feedAustralia back mechanism. SNA requires representing social net*$Coorrrveesnp.ollnadnitnogs@augt.hmosru.iit.edu.ph (O. E. Llantos) works as a mathematical entity called graph into which 0000-0002-0787-0282 (O. E. Llantos) associated algorithms describe the properties of the net© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License work. A graph is composed of nodes (i.e., indiCPWrEooUrckReshdoinpgs IhStpN:/c1e6u1r3-w-0s.o7r3g ACttEribUutRion W4.0oInrtekrnsahtioonpal (PCCroBYce4.0e).dings (CEUR-WS.org) viduals in the networks) and edges (connections of the dropout of the administrator, teachers, students, and nodes), = (, ). The graph which resulted from parents. The efects of such an algorithm still needs the learning interactions of the users is expressed in the further investigation. equation ℎ = , ∪ , , where is the period the graph is formed at ℎ. ℎ is Acknowledgments composed of the learning interactions between the administrators to the teachers , , and teachers of
advisory class to the students and parents , ing this study, Ateneo Social Computing Science
Labo[
2 period as expressed in Equation, ⋃︀ ⋃︀ ℎ .
=1 =1
Calculating influence and betweenness centralities in the graph produces two graphs for each centrality which lead to the discovery of Equation 1.
Φ , > Φ , > Φ ,
(1) where Φ , is the centrality score of administrator in school , and Φ , is the centrality score of teacher in school and advisory section , and Φ , is the centrality score of students and parents under advisory section in school .
The centrality measures identify the important figure in the social network and how their interactions afect the ties and flow of information. 3.2. Conceptualization of the Algorithm Equation 1 will be the basis of the algorithm to send out reminders to concerned users once the requirement is not satisfied. Further data experimentation using social network analysis with interactions from 2019-2022 will help verify Equation 1 using two-column proofs as one of the preferred methods. User evaluation grounded on the technology acceptance model will follow once Equation 1 is integrated into the system.