Enhancing Pastoral Care and Academic Monitoring Efforts using the AiE-PFP Learning Analytics Solutions
2023-09-14, 11:10– (Asia/Tokyo), 4F Mid-size Conference Room

In recent years, there has been growing interest amongst institutes of higher learning (IHLs) to adopt learning analytics solutions to enhance the teaching and learning experience of students. Due to this interest, the 5 polytechnics in Singapore undertook a project to explore the use of analytics in education in 2019. 2 analytics use cases resulted from the project - a predictive model and a personal tutor facing dashboard. The predictive model generates predicted learning needs of students by running machine learning and statistical rule-based algorithms through data such as attendance rates, consistency and past academic performance. The dashboard then displays the predicted student learning needs as well as other pertinent information about the students such as choice order of diploma (this is a proxy for motivation level of students), academic performance progress, participation in co-curricular activities (CCAs), activity in learning management system (LMS) and other behavioural indicators. The intent of the use cases is for personal tutors (lecturers who take care of pastoral care needs and monitor academic progress of students) to be able to glean insights and devise and apply appropriate interventions based on the vast amount of data presented. With better support and interventions, it is hoped that student academic outcomes will be optimised.

After the 1-year pilot which involved 104 personal tutors of 5 polytechnics, a comprehensive evaluation exercise was conducted amongst the students and personal tutors to ascertain the effectiveness of the use cases. Evaluation included focus group discussions amongst personal tutors and survey inputs from students and personal tutors. Reception amongst both students and personal tutors was generally positive. Personal tutors found the use cases to be helpful in getting to know their tutees better and identify areas that require support. Students perceived that personal tutors had provided them with sufficient pastoral care and guidance. A task analysis exercise that measured time spent before and after using the use cases also revealed that, after the implementation of the dashboard, personal tutors now save tremendous amount of time not having to consolidate data from disparate sources on their own accord.

The successful pilot led to a second phase of the project, which is to implement similar use cases to other diploma courses.

This paper describes the journey undertaken by a 5-polytechnic project team in collaboration with an external vendor in Singapore to produce 2 analytics use cases in education, a predictive model and a personal tutor facing dashboard. The intent of the use cases was to enhance pastoral care and academic support to students of the Polytechnic Foundation Program (PFP), a 1-year bridging course before students embark on their diploma studies. The paper will elaborate how the use cases were used to fulfil the intent for different student scenarios. It will also describe the comprehensive evaluation process that was carried out thereafter and the results that ensued.


learning analytics, predictive model, dashboard, personal tutors, student learning needs, interventions

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