%0 Journal Article %1 Strang2016 %A Strang, Kenneth David %D 2016 %J Education and Information Technologies %K LMS Moodle bigdata highered learninganalytics %P 1--21 %R 10.1007/s10639-016-9464-2 %T Beyond engagement analytics: which online mixed-data factors predict student learning outcomes? %U http://dx.doi.org/10.1007/s10639-016-9464-2 %X This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large sample from an online undergraduate business course at an accredited American university (n = 228). Recent studies have discounted the ability to predict student learning outcomes from big data analytics but a few significant indicators have been found by some researchers. Current studies tend to use quantitative factors in learning analytics to forecast outcomes. This study extends that work by testing the common quantitative predictors of learning outcome, but qualitative data is also examined to triangulate the evidence. Pre and post testing of information technology understanding is done at the beginning of the course. First quantitative data is collected, and depending on the hypothesis test results, qualitative data is collected and analyzed with text analytics to uncover patterns. Moodle engagement analytics indicators are tested as predictors in the model. Data is also taken from the Moodle system logs. Qualitative data is collected from student reflection essays. The result was a significant General Linear Model with four online interaction predictors that captured 77.5 \% of grade variance in an undergraduate business course.

@article{Strang2016, abstract = {This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large sample from an online undergraduate business course at an accredited American university (n = 228). Recent studies have discounted the ability to predict student learning outcomes from big data analytics but a few significant indicators have been found by some researchers. Current studies tend to use quantitative factors in learning analytics to forecast outcomes. This study extends that work by testing the common quantitative predictors of learning outcome, but qualitative data is also examined to triangulate the evidence. Pre and post testing of information technology understanding is done at the beginning of the course. First quantitative data is collected, and depending on the hypothesis test results, qualitative data is collected and analyzed with text analytics to uncover patterns. Moodle engagement analytics indicators are tested as predictors in the model. Data is also taken from the Moodle system logs. Qualitative data is collected from student reflection essays. The result was a significant General Linear Model with four online interaction predictors that captured 77.5 {\%} of grade variance in an undergraduate business course.}, added-at = {2017-02-24T16:46:22.000+0100}, author = {Strang, Kenneth David}, biburl = {https://www.bibsonomy.org/bibtex/2a9276e2acadffbc8d79b32e735af974d/ereidt}, doi = {10.1007/s10639-016-9464-2}, interhash = {eb10e3d97bb72bba8877689b16126fae}, intrahash = {a9276e2acadffbc8d79b32e735af974d}, issn = {1573-7608}, journal = {Education and Information Technologies}, keywords = {LMS Moodle bigdata highered learninganalytics}, pages = {1--21}, timestamp = {2017-02-24T16:46:22.000+0100}, title = {Beyond engagement analytics: which online mixed-data factors predict student learning outcomes?}, url = {http://dx.doi.org/10.1007/s10639-016-9464-2}, year = 2016 }