Table VII Summary of predictive model... | Emerald Publishing

Table VII

Summary of predictive model and intervention solution for selected case studies

Institution	Learning analytics system (s)	Predictive model	Intervention solution
Georgia Institute of Technology and Carnegie Mellon University (Wang et al., 2016)	Interactive-Constructive-Active-Passive (ICAP) framework	It was predicted that engaging in higher-order thinking behaviours results in better learning outcomes than paying general or focussed attention to course materials	Students’ online discussion behaviours were categorized into three types: Higher-order – the student has contributed at least one constructive or interactive post during a course Paying-attention – the student has contributed at least one active post during the course but has not displayed any constructive or interactive posts No contribution to any on-topic discussion during the course Together with the students’ other persistent characteristics, treatment and control groups were formed to investigate differences in their learning outcomes
Hong Kong Institute of Education (Wong and Li, 2016)	KeyGraph algorithm and Polaris (a software tool)	A test-mining analytical tool was used to predict students’ academic performance. The tool visualizes the hidden patterns and linkages among students’ learning activities. The findings of the study showed that this approach can provide insights into predicting students’ performance, and students with a higher grade tended to contribute more in-depth contents in an online learning environment	Students’ posts in an online learning forum were extracted and analysed – how the students presented concepts, specifically whether they can make linkage among various concepts. Such a pattern was correlated with the grades they obtained. The findings can be used to guide interventions on students’ learning process, and inform ways to give feedback to improve teaching and learning
Marist College (Jayaprakash et al., 2014)	Open Academic Analytics Initiative	A machine learning algorithm and logistic regression were used to predict whether students are at risk based on their demographic details, aptitude data, and various aspects of their usage of the virtual learning environment obtained from the LA system	An online academic support environment was developed containing study skills materials and community support for specialists and student mentors. At-risk students identified by the predictive model were directed to the support environment
Nottingham Trent University (Sclater et al., 2016)	NTU Student Dashboard	Students’ engagement was assessed using indicators, such as door swipes into academic buildings, visits to the virtual learning environment, the submission of assignments, and the frequency of borrowing library resources. Each student received one of five engagement ratings: high, good, partial, low and not fully enroled	Tutors are prompted to contact students to give assistance when the students’ engagement drops off. Students can view their own engagement scores on the dashboard so that they will be self-motivated
Paul Smith’s College (McAleese and Taylor, 2012)	Rapid Insight’s Veera, Starfish EARLY ALERT, and CONNECT	Rapid Insight’s Veera combines different file types and uses automatic analyses and predictive modelling to identify at-risk students prior to their enrolment. Starfish EARLY ALERT automates data collection and uses analytics to increase the identification of at-risk students	The Starfish EARLY ALERT and CONNECT automatically prioritize students who are identified as at-risk and facilitate intervention and outreach
Purdue University (Arnold and Pistilli, 2012)	Course Signal System	The Course Signal System predicted students’ performance relying on a series of variables, including students’ demographic characteristics, academic performance, past academic history, and students’ efforts devoted to study	Instructors provided real-time personalized feedback to each student based on the outcomes generated from LA, in which the student is informed about how he/she is doing

Institution	Learning analytics system (s)	Predictive model	Intervention solution
Georgia Institute of Technology and Carnegie Mellon University (Wang et al., 2016)	Interactive-Constructive-Active-Passive (ICAP) framework	It was predicted that engaging in higher-order thinking behaviours results in better learning outcomes than paying general or focussed attention to course materials	Students’ online discussion behaviours were categorized into three types: Higher-order – the student has contributed at least one constructive or interactive post during a course Paying-attention – the student has contributed at least one active post during the course but has not displayed any constructive or interactive posts No contribution to any on-topic discussion during the courseTogether with the students’ other persistent characteristics, treatment and control groups were formed to investigate differences in their learning outcomes
Hong Kong Institute of Education (Wong and Li, 2016)	KeyGraph algorithm and Polaris (a software tool)	A test-mining analytical tool was used to predict students’ academic performance. The tool visualizes the hidden patterns and linkages among students’ learning activities. The findings of the study showed that this approach can provide insights into predicting students’ performance, and students with a higher grade tended to contribute more in-depth contents in an online learning environment	Students’ posts in an online learning forum were extracted and analysed – how the students presented concepts, specifically whether they can make linkage among various concepts. Such a pattern was correlated with the grades they obtained. The findings can be used to guide interventions on students’ learning process, and inform ways to give feedback to improve teaching and learning
Marist College (Jayaprakash et al., 2014)	Open Academic Analytics Initiative	A machine learning algorithm and logistic regression were used to predict whether students are at risk based on their demographic details, aptitude data, and various aspects of their usage of the virtual learning environment obtained from the LA system	An online academic support environment was developed containing study skills materials and community support for specialists and student mentors. At-risk students identified by the predictive model were directed to the support environment
Nottingham Trent University (Sclater et al., 2016)	NTU Student Dashboard	Students’ engagement was assessed using indicators, such as door swipes into academic buildings, visits to the virtual learning environment, the submission of assignments, and the frequency of borrowing library resources. Each student received one of five engagement ratings: high, good, partial, low and not fully enroled	Tutors are prompted to contact students to give assistance when the students’ engagement drops off. Students can view their own engagement scores on the dashboard so that they will be self-motivated
Paul Smith’s College (McAleese and Taylor, 2012)	Rapid Insight’s Veera, Starfish EARLY ALERT, and CONNECT	Rapid Insight’s Veera combines different file types and uses automatic analyses and predictive modelling to identify at-risk students prior to their enrolment. Starfish EARLY ALERT automates data collection and uses analytics to increase the identification of at-risk students	The Starfish EARLY ALERT and CONNECT automatically prioritize students who are identified as at-risk and facilitate intervention and outreach
Purdue University (Arnold and Pistilli, 2012)	Course Signal System	The Course Signal System predicted students’ performance relying on a series of variables, including students’ demographic characteristics, academic performance, past academic history, and students’ efforts devoted to study	Instructors provided real-time personalized feedback to each student based on the outcomes generated from LA, in which the student is informed about how he/she is doing