Table IV Use of LA which helped in... | Emerald Publishing

Table IV

Use of LA which helped in understanding students’ learning behaviours

Institution	Major outcomes	Source
Ball State University	Data analyses showed the consistent predictive power of the LA system on students’ academic performance, persistence, retention and graduation	Jones and Woosley (2011)
Georgia Institute of Technology and Carnegie Mellon University	Students who displayed more higher-order thinking behaviours learnt more through deeper engagement with course materials displayed by their discussion behaviours These students in turn also learnt more than students who were constantly off topic in the forums Social-oriented topics triggered richer discussion compared with biopsychology oriented topics, and higher-order thinking behaviours tended to appear together within threads in the forums	Wang et al. (2016)
McGill University	It provides an unprecedented opportunity to use data from real learners in authentic learning situations to better understand learning processes The study demonstrated how to detect learner misconceptions Prediction precision and weighted relative accuracy were significantly increased	Poitras et al. (2016)
Oxford Brookes University	Problems were identified with ethnic minority students in particular courses	Sclater et al. (2016)
The Hong Kong Institute of Education	Potential indicators were found for predicting student performance, such as the contribution of in-depth contents in online discussion	Wong and Li (2016)
The Open University (UK)	Common pedagogical patterns were identified from learning designs, showing the relationship between learning activities and students’ learning outcomes	Toetenel and Rienties (2016)
The Technical University of Madrid	Relationship between student interaction and individual performance was identified	Fidalgo-Blanco et al. (2015)
The University of Melbourne	Relationships among students’ motivation, participation and performance in MOOCs were found	Barba et al. (2016)
The University of Melbourne	Learners’ learning progress could be visualized showing their development from novice to expert	Milligan (2015)
University of Adelaide	Lecturers could track the evolution of team roles across each study group and identify various sentiments within each group	Tarmazdi et al. (2015)
University of Edinburgh	Patterns of students’ engagement in MOOC learning activities were found, showing differences in their learning behaviours between enrolments in the same courses	Kovanović et al. (2016)
University of North Bengal	Factors leading to students’ dropout were identified, such as pregnancy and the remoteness of residence locations	Yasmine (2013)
University of Rijeka	Student activities on the learning management system (e.g. assignment uploads and course views) were shown as predictors of academic success	Sisovic et al. (2015)
University of Santiago de Compostela	Teachers could understand more clearly how students behave during a course that facilitated the evaluation process	Gewerc et al. (2014)

Institution	Major outcomes	Source
Ball State University	Data analyses showed the consistent predictive power of the LA system on students’ academic performance, persistence, retention and graduation	Jones and Woosley (2011)
Georgia Institute of Technology and Carnegie Mellon University	Students who displayed more higher-order thinking behaviours learnt more through deeper engagement with course materials displayed by their discussion behavioursThese students in turn also learnt more than students who were constantly off topic in the forumsSocial-oriented topics triggered richer discussion compared with biopsychology oriented topics, and higher-order thinking behaviours tended to appear together within threads in the forums	Wang et al. (2016)
McGill University	It provides an unprecedented opportunity to use data from real learners in authentic learning situations to better understand learning processesThe study demonstrated how to detect learner misconceptionsPrediction precision and weighted relative accuracy were significantly increased	Poitras et al. (2016)
Oxford Brookes University	Problems were identified with ethnic minority students in particular courses	Sclater et al. (2016)
The Hong Kong Institute of Education	Potential indicators were found for predicting student performance, such as the contribution of in-depth contents in online discussion	Wong and Li (2016)
The Open University (UK)	Common pedagogical patterns were identified from learning designs, showing the relationship between learning activities and students’ learning outcomes	Toetenel and Rienties (2016)
The Technical University of Madrid	Relationship between student interaction and individual performance was identified	Fidalgo-Blanco et al. (2015)
The University of Melbourne	Relationships among students’ motivation, participation and performance in MOOCs were found	Barba et al. (2016)
The University of Melbourne	Learners’ learning progress could be visualized showing their development from novice to expert	Milligan (2015)
University of Adelaide	Lecturers could track the evolution of team roles across each study group and identify various sentiments within each group	Tarmazdi et al. (2015)
University of Edinburgh	Patterns of students’ engagement in MOOC learning activities were found, showing differences in their learning behaviours between enrolments in the same courses	Kovanović et al. (2016)
University of North Bengal	Factors leading to students’ dropout were identified, such as pregnancy and the remoteness of residence locations	Yasmine (2013)
University of Rijeka	Student activities on the learning management system (e.g. assignment uploads and course views) were shown as predictors of academic success	Sisovic et al. (2015)
University of Santiago de Compostela	Teachers could understand more clearly how students behave during a course that facilitated the evaluation process	Gewerc et al. (2014)