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Student Performance Based on Activity Log on Social Network and e-Learning

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@article{IJASEIT8753,
   author = {- Agusriandi and Imas Sukaesih Sitanggang and Sony Hartono Wijaya},
   title = {Student Performance Based on Activity Log on Social Network and e-Learning},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {6},
   year = {2020},
   pages = {2276--2281},
   keywords = {activity log; e-learning; performance; social network},
   abstract = {Learning activities in social networks and e-learning platforms bring massive activity log in the database, making it challenging to measure students’ performance. Data mining technique and social network analysis provide some benefits in the field of education in discovering knowledge from hidden information of student’s activities on e-learning and social network environment. This study aims to identify dominant students on social network group based on centrality values and to analyze log data from the activities on e-learning using process mining technique. Centrality value was measured by analyzing data quality or data pre-processing, creating the network, measuring the network, and highlighting degrees and layouts. The process mining technique included data pre-processing, discovering process, and conformance checking. This study found that dominant students were identified from a high hub score and authority. This study also found a free-rider student. The presence of dominant students and free-riders made the collaboration of social network group are weak. This study also found that student performance on e-learning has been discovered where the student’s activity, namely, the course module viewed and course viewed, were more frequent than other activities. On the other hand, an optimum fitness value was obtained, i.e., 0.94 on all the processes of e-learning. This study provides insights that can be used to improve student collaboration and to enhance online learning activities.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8753},
   doi = {10.18517/ijaseit.10.6.8753}
}

EndNote

%A Agusriandi, -
%A Sitanggang, Imas Sukaesih
%A Wijaya, Sony Hartono
%D 2020
%T Student Performance Based on Activity Log on Social Network and e-Learning
%B 2020
%9 activity log; e-learning; performance; social network
%! Student Performance Based on Activity Log on Social Network and e-Learning
%K activity log; e-learning; performance; social network
%X Learning activities in social networks and e-learning platforms bring massive activity log in the database, making it challenging to measure students’ performance. Data mining technique and social network analysis provide some benefits in the field of education in discovering knowledge from hidden information of student’s activities on e-learning and social network environment. This study aims to identify dominant students on social network group based on centrality values and to analyze log data from the activities on e-learning using process mining technique. Centrality value was measured by analyzing data quality or data pre-processing, creating the network, measuring the network, and highlighting degrees and layouts. The process mining technique included data pre-processing, discovering process, and conformance checking. This study found that dominant students were identified from a high hub score and authority. This study also found a free-rider student. The presence of dominant students and free-riders made the collaboration of social network group are weak. This study also found that student performance on e-learning has been discovered where the student’s activity, namely, the course module viewed and course viewed, were more frequent than other activities. On the other hand, an optimum fitness value was obtained, i.e., 0.94 on all the processes of e-learning. This study provides insights that can be used to improve student collaboration and to enhance online learning activities.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8753
%R doi:10.18517/ijaseit.10.6.8753
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 6
%@ 2088-5334

IEEE

- Agusriandi,Imas Sukaesih Sitanggang and Sony Hartono Wijaya,"Student Performance Based on Activity Log on Social Network and e-Learning," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2276-2281, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.6.8753.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Agusriandi, -
AU  - Sitanggang, Imas Sukaesih
AU  - Wijaya, Sony Hartono
PY  - 2020
TI  - Student Performance Based on Activity Log on Social Network and e-Learning
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 6
Y2  - 2020
SP  - 2276
EP  - 2281
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - activity log; e-learning; performance; social network
N2  - Learning activities in social networks and e-learning platforms bring massive activity log in the database, making it challenging to measure students’ performance. Data mining technique and social network analysis provide some benefits in the field of education in discovering knowledge from hidden information of student’s activities on e-learning and social network environment. This study aims to identify dominant students on social network group based on centrality values and to analyze log data from the activities on e-learning using process mining technique. Centrality value was measured by analyzing data quality or data pre-processing, creating the network, measuring the network, and highlighting degrees and layouts. The process mining technique included data pre-processing, discovering process, and conformance checking. This study found that dominant students were identified from a high hub score and authority. This study also found a free-rider student. The presence of dominant students and free-riders made the collaboration of social network group are weak. This study also found that student performance on e-learning has been discovered where the student’s activity, namely, the course module viewed and course viewed, were more frequent than other activities. On the other hand, an optimum fitness value was obtained, i.e., 0.94 on all the processes of e-learning. This study provides insights that can be used to improve student collaboration and to enhance online learning activities.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8753
DO  - 10.18517/ijaseit.10.6.8753

RefWorks

RT Journal Article
ID 8753
A1 Agusriandi, -
A1 Sitanggang, Imas Sukaesih
A1 Wijaya, Sony Hartono
T1 Student Performance Based on Activity Log on Social Network and e-Learning
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 6
YR 2020
SP 2276
OP 2281
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 activity log; e-learning; performance; social network
AB Learning activities in social networks and e-learning platforms bring massive activity log in the database, making it challenging to measure students’ performance. Data mining technique and social network analysis provide some benefits in the field of education in discovering knowledge from hidden information of student’s activities on e-learning and social network environment. This study aims to identify dominant students on social network group based on centrality values and to analyze log data from the activities on e-learning using process mining technique. Centrality value was measured by analyzing data quality or data pre-processing, creating the network, measuring the network, and highlighting degrees and layouts. The process mining technique included data pre-processing, discovering process, and conformance checking. This study found that dominant students were identified from a high hub score and authority. This study also found a free-rider student. The presence of dominant students and free-riders made the collaboration of social network group are weak. This study also found that student performance on e-learning has been discovered where the student’s activity, namely, the course module viewed and course viewed, were more frequent than other activities. On the other hand, an optimum fitness value was obtained, i.e., 0.94 on all the processes of e-learning. This study provides insights that can be used to improve student collaboration and to enhance online learning activities.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8753
DO  - 10.18517/ijaseit.10.6.8753