TY - GEN
T1 - Continuous clustering in big data learning analytics
AU - Govindarajan, Kannan
AU - Somasundaram, Thamarai Selvi
AU - Kumar, Vivekanandan S.
AU - Kinshuk,
PY - 2013
Y1 - 2013
N2 - Learners' attainment of academic knowledge in postsecondary institutions is predominantly expressed by summative or formative assessment approaches. Recent advances in educational technology has hinted at a means to measure learning efficiency, in terms of personalization of learner competency and capacity in terms of adaptability of observed practices, using raw data observed from study experiences of learners as individuals and as contributors in social networks. While accurate computational models that embody learning efficiency remain a distant and elusive goal, big data learning analytics approaches this goal by recognizing competency growth of learners, at various levels of granularity, using a combination of continuous, formative and summative assessments. This study discusses a method to continuously capture data from students' learning interactions. Then, it analyzes and clusters the data based on their individual performances in terms of accuracy, efficiency and quality by employing Particle Swarm Optimization (PSO) algorithm.
AB - Learners' attainment of academic knowledge in postsecondary institutions is predominantly expressed by summative or formative assessment approaches. Recent advances in educational technology has hinted at a means to measure learning efficiency, in terms of personalization of learner competency and capacity in terms of adaptability of observed practices, using raw data observed from study experiences of learners as individuals and as contributors in social networks. While accurate computational models that embody learning efficiency remain a distant and elusive goal, big data learning analytics approaches this goal by recognizing competency growth of learners, at various levels of granularity, using a combination of continuous, formative and summative assessments. This study discusses a method to continuously capture data from students' learning interactions. Then, it analyzes and clusters the data based on their individual performances in terms of accuracy, efficiency and quality by employing Particle Swarm Optimization (PSO) algorithm.
KW - Big Data
KW - Hadoop
KW - K-Means Clustering
KW - Learning Analytics
KW - Particle Swarm Optimization (PSO)-based Clustering
UR - http://www.scopus.com/inward/record.url?scp=84896537236&partnerID=8YFLogxK
U2 - 10.1109/T4E.2013.23
DO - 10.1109/T4E.2013.23
M3 - Published Conference contribution
AN - SCOPUS:84896537236
SN - 9780769551418
T3 - Proceedings - 2013 IEEE 5th International Conference on Technology for Education, T4E 2013
SP - 61
EP - 64
BT - Proceedings - 2013 IEEE 5th International Conference on Technology for Education, T4E 2013
T2 - 2013 IEEE 5th International Conference on Technology for Education, T4E 2013
Y2 - 18 December 2013 through 20 December 2013
ER -