%0 Journal Article %1 Bharara2017 %A Bharara, Sanyam %A Sabitha, Sai %A Bansal, Abhay %D 2017 %J Education and Information Technologies %K LMS academicanalytics applications educationaldatamining learninganalytics %R 10.1007/s10639-017-9645-7 %T Application of learning analytics using clustering data Mining for Students' disposition analysis %U https://doi.org/10.1007/s10639-017-9645-7 %X Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student's performance using Disposition analysis. In this project, K-means clustering data mining technique is used to obtain clusters which are further mapped to find the important features of a learning context. Relationships between these features are identified to assess the student's performance.

@article{Bharara2017, abstract = {Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student's performance using Disposition analysis. In this project, K-means clustering data mining technique is used to obtain clusters which are further mapped to find the important features of a learning context. Relationships between these features are identified to assess the student's performance.}, added-at = {2017-10-12T11:59:32.000+0200}, author = {Bharara, Sanyam and Sabitha, Sai and Bansal, Abhay}, biburl = {https://www.bibsonomy.org/bibtex/228c3191259e5d5ccf671d59c59fb6828/ereidt}, day = 05, doi = {10.1007/s10639-017-9645-7}, interhash = {a08fdb07e447e18cdc151988e95798e0}, intrahash = {28c3191259e5d5ccf671d59c59fb6828}, issn = {1573-7608}, journal = {Education and Information Technologies}, keywords = {LMS academicanalytics applications educationaldatamining learninganalytics}, month = oct, timestamp = {2017-10-12T11:59:32.000+0200}, title = {Application of learning analytics using clustering data Mining for Students' disposition analysis}, url = {https://doi.org/10.1007/s10639-017-9645-7}, year = 2017 }