%0 Journal Article %1 ÇAKIROĞLU2022 %A CAKIROGLU, Ünal %A KAHYAR, Sefa %D 2022 %J Education and Information Technologies %K LMS communityofinquiry correspondenceanalysis data interaction learninganalytics onlineeducation %R 10.1007/s10639-022-10950-8 %T Modelling online community constructs through interaction data: A learning analytics based Approach %U https://doi.org/10.1007/s10639-022-10950-8 %X This study aims to use LMS log data to suggest a way to understand CoI constructs. Students' interactions in Moodle components were weighted for indicators of cognitive, teaching and social presences. Traces reflecting students' online interactions were obtained from the Moodle LMS and analyzed through learning analytics techniques. The data is examined with the Euclidean Distance Model, and Correspondence Analysis methods to evaluate the levels of interactions and presences. The results indicated that, cognitive presence is at the center of the CoI constructs, and student-content interaction, is found is more prominent than other interactions in terms of its relation to cognitive presence. Social presence scores were mostly related with student-student and student-teacher interaction scores. In addition, teaching presence scores were found in parallel with student-system interaction scores.

@article{ÇAKIROĞLU2022, abstract = {This study aims to use LMS log data to suggest a way to understand CoI constructs. Students' interactions in Moodle components were weighted for indicators of cognitive, teaching and social presences. Traces reflecting students' online interactions were obtained from the Moodle LMS and analyzed through learning analytics techniques. The data is examined with the Euclidean Distance Model, and Correspondence Analysis methods to evaluate the levels of interactions and presences. The results indicated that, cognitive presence is at the center of the CoI constructs, and student-content interaction, is found is more prominent than other interactions in terms of its relation to cognitive presence. Social presence scores were mostly related with student-student and student-teacher interaction scores. In addition, teaching presence scores were found in parallel with student-system interaction scores.}, added-at = {2022-03-26T12:30:34.000+0100}, author = {{\c{C}}AKIRO{\u{G}}LU, {\"U}nal and KAHYAR, Sefa}, biburl = {https://www.bibsonomy.org/bibtex/219430a7481119c1adf946ef42fa5448e/ereidt}, day = 04, doi = {10.1007/s10639-022-10950-8}, interhash = {ce05277622e3f8aefd1653824291565f}, intrahash = {19430a7481119c1adf946ef42fa5448e}, issn = {1573-7608}, journal = {Education and Information Technologies}, keywords = {LMS communityofinquiry correspondenceanalysis data interaction learninganalytics onlineeducation}, month = mar, timestamp = {2022-03-26T12:30:34.000+0100}, title = {Modelling online community constructs through interaction data: A learning analytics based Approach}, url = {https://doi.org/10.1007/s10639-022-10950-8}, year = 2022 }