Table III Use of LA which increased... | Emerald Publishing

Table III

Use of LA which increased cost-effectiveness

Institution	Major outcomes	Source
Bridgewater College	Notifications were automatically generated and sent to students and their parents to recognize students’ good performance	Sclater et al. (2016)
Drexel University	Faculty, programme developers, and programme administrators were able to analyse the connections between a specific programme outcome and data related to that outcome	Harvey (2013)
Georgia Institute of Technology and Carnegie Mellon University	High reliability was achieved for analysing students’ online discussion data	Wang et al. (2016)
Harvard University	A machine learning prediction model was shown to be effective for predicting students who would complete an online course	Robinson et al. (2016)
Lancaster University	Tutors could efficiently access various kinds of data for providing students with timely support	Sclater et al. (2016)
New York Institute of Technology	A dashboard simple and easy to use by staff was developed	Sclater et al. (2016)
Open University of Catalonia	Information could be updated and maintained automatically	Guitart et al. (2015)
Portland State University	Operation efficiency was increased, e.g. faster generation of reports The system could easily be modified to fit the needs of other institutions	Blanton (2012)
Purdue University	Students who had engaged with the LA system sought more help and resources than other students	Arnold and Pistilli (2012)
Rio Salado College	The likelihood of successful course completion was accurately assessed	Smith et al. (2012)
The Hong Kong Institute of Education	There was greater interaction between teachers and students	Wong and Li (2016)
University of Adelaide	Lecturers were allowed to assess and monitor students’ collaboration in an online environment, without having to traverse a large discussion forum	Tarmazdi et al. (2015)
University of Michigan	The system demonstrated high scalability and extensibility	Mattingly et al. (2012)
University of Salamanca	The system allowed the provision of learning support to students in an automatic manner	Cruz-Benito et al. (2014)
University of the South Pacific	The utilization of open source resources could be modified and adapted by anyone to meet specific user needs	Prasad et al. (2016)
University of Sydney	LA features such as instant feedback and auto-grading are especially useful for instructors teaching subjects in computer science education	Gramoli et al. (2016)

Institution	Major outcomes	Source
Bridgewater College	Notifications were automatically generated and sent to students and their parents to recognize students’ good performance	Sclater et al. (2016)
Drexel University	Faculty, programme developers, and programme administrators were able to analyse the connections between a specific programme outcome and data related to that outcome	Harvey (2013)
Georgia Institute of Technology and Carnegie Mellon University	High reliability was achieved for analysing students’ online discussion data	Wang et al. (2016)
Harvard University	A machine learning prediction model was shown to be effective for predicting students who would complete an online course	Robinson et al. (2016)
Lancaster University	Tutors could efficiently access various kinds of data for providing students with timely support	Sclater et al. (2016)
New York Institute of Technology	A dashboard simple and easy to use by staff was developed	Sclater et al. (2016)
Open University of Catalonia	Information could be updated and maintained automatically	Guitart et al. (2015)
Portland State University	Operation efficiency was increased, e.g. faster generation of reportsThe system could easily be modified to fit the needs of other institutions	Blanton (2012)
Purdue University	Students who had engaged with the LA system sought more help and resources than other students	Arnold and Pistilli (2012)
Rio Salado College	The likelihood of successful course completion was accurately assessed	Smith et al. (2012)
The Hong Kong Institute of Education	There was greater interaction between teachers and students	Wong and Li (2016)
University of Adelaide	Lecturers were allowed to assess and monitor students’ collaboration in an online environment, without having to traverse a large discussion forum	Tarmazdi et al. (2015)
University of Michigan	The system demonstrated high scalability and extensibility	Mattingly et al. (2012)
University of Salamanca	The system allowed the provision of learning support to students in an automatic manner	Cruz-Benito et al. (2014)
University of the South Pacific	The utilization of open source resources could be modified and adapted by anyone to meet specific user needs	Prasad et al. (2016)
University of Sydney	LA features such as instant feedback and auto-grading are especially useful for instructors teaching subjects in computer science education	Gramoli et al. (2016)