Table I Use of LA which improved student... | Emerald Publishing

Table I

Use of LA which improved student retention

Institution	Major outcomes	Source
Bowie State University	More student activities and communication were initiated through the system	Chacon et al. (2012)
Edith Cowan University	The student retention rate for those who got support was higher than the university’s average rate	Atif et al. (2013)
Harvard University	The results demonstrate the potential for natural language processing to contribute to predicting student success in MOOCs and other forms of open online learning	Robinson et al. (2016)
New York Institute of Technology	An at-risk model of high predictive power was developed	Sclater et al. (2016)
Northern Arizona University	Student-instructor interaction was increased and personal interventions were given; and students showed better academic performance, retention and graduation rates	Star and Collette (2010)
Paul Smith’s College	Students devoting more efforts in their studies resulted in a higher chance of success, and better persistence and graduation rates	McAleese and Taylor (2012)
Rio Salado Community College	A 40% decrease in drop-out rate was obtained for students who received welcome e-mails compared with those who did not	Smith et al. (2012)
The Open University (UK)	A vast majority of students showed continuous engagement Student retention was at an average to good level Students demonstrated higher satisfaction	Rienties et al. (2016)
University of New England	The student attrition dropped from 18 to 12% Students demonstrated an increase in their sense of belonging to the learner community and learning motivation	Sclater et al. (2016)

Institution	Major outcomes	Source
Bowie State University	More student activities and communication were initiated through the system	Chacon et al. (2012)
Edith Cowan University	The student retention rate for those who got support was higher than the university’s average rate	Atif et al. (2013)
Harvard University	The results demonstrate the potential for natural language processing to contribute to predicting student success in MOOCs and other forms of open online learning	Robinson et al. (2016)
New York Institute of Technology	An at-risk model of high predictive power was developed	Sclater et al. (2016)
Northern Arizona University	Student-instructor interaction was increased and personal interventions were given; and students showed better academic performance, retention and graduation rates	Star and Collette (2010)
Paul Smith’s College	Students devoting more efforts in their studies resulted in a higher chance of success, and better persistence and graduation rates	McAleese and Taylor (2012)
Rio Salado Community College	A 40% decrease in drop-out rate was obtained for students who received welcome e-mails compared with those who did not	Smith et al. (2012)
The Open University (UK)	A vast majority of students showed continuous engagementStudent retention was at an average to good levelStudents demonstrated higher satisfaction	Rienties et al. (2016)
University of New England	The student attrition dropped from 18 to 12%Students demonstrated an increase in their sense of belonging to the learner community and learning motivation	Sclater et al. (2016)