A well-educated citizenry is an economic and social necessity, especially for countries like Singapore where its people double as valuable human capital. And through the years, Singaporeans’ educational achievements have risen sharply.
According to a recent study by the Ministry of Finance, 79% of Singaporean citizens born between 1970 and 1979 have completed post-secondary education, as compared to just 22% of citizens born between 1940 and 1949. While this marked improvement can be attributed to better standard of living, the government’s concerted efforts to enhance the quality of education has also been one of the driving forces for change.
As globalisation keeps lowering the barriers for international competition, it is more important than ever for us to continue providing our students with the best yet affordable education. Underpinned by the widespread adoption of Industry 4.0 technologies, adoption of Educational Technologies (EdTech) is also growing on a global scale. Asia-Pacific is also expected to account for 54% of EdTech revenue by 2020.
Technologies such as learning analytics is gaining popularity to help universities to raise students’ performance, improve their learning experience and give them a better shot at excelling in school.
Empowered learning with data analytics
From the time they fill in their application forms to interacting with the learning management system, participating in academic and professional activities and all the way through to final exams, students are continually generating data within their universities.
Schools are starting to take notice of this growing trove of valuable information, including student demographics, prior educational background, study behaviour and academic performance. This data can be leveraged to uncover previously unseen patterns, revealing determinants of academic performance that can be used to facilitate more effective teaching and learning.
Otherwise referred to as ‘Learning Analytics’, this data enables schools to experience unprecedented advantages such as:
1. Making a difference in students’ learning processes when it matters most
With learning analytics, universities can generate predictions of students’ performance with greater precision. These predictions are then fed into an early alert system which includes other information like students’ current study load and their previous semesters’ cumulative grade point average. School staff members are in turn empowered to formulate effective intervention strategies to support individual students at the right time.
When these targeted interventions are delivered when they matter the most, faculty members and students will get the needed support to achieve better learning outcomes.
2. Leveraging data-driven insights to offer tools that matter to students
Many universities, like the Singapore University of Social Sciences (SUSS), are using learning analytics to help identify determinants of better academic performance.
Several determinants stood out from the analysis. Learners who achieve a cumulative grade point average of more than four out of five include those who clear their core modules early and access their online learning materials earlier and more regularly.
With this understanding, SUSS has been able to develop student advisories that are available to students at opportune times. Such insight-driven tips in the advisories serve as valuable reminders to students – many of whom are working adults who have been out of the mainstream education system for a few years – to help them do better academically.
3. Improving knowledge retention with a better learning environment
Students’ perception of a caring learning environment is also a strong motivator for them to perform better.
At Purdue University in the U.S., for example, a ‘Signals’ system allows professors to post green, orange or red traffic signals to individual students accompanied by an email or text providing academic advice.
In the United Kingdom, the Open University also experiments with weekly predictive analytics reports on selected students.
These systematic, timely and personalised interventions improve communication between students and faculty. As students’ perceptions of faculty members who are committed to tracking their performance improve, both their academic performance and knowledge retention also improves.
‘Human factor’ needed to unleash the potential of learning analytics
Against this backdrop of advantages, it is important to note that learning analytics and other educational technologies are not meant to be a panacea for quality education.
Providing proper student learning support requires human involvement, where lecturers are willing and able to provide a caring and nurturing learning environment.
Students, too, need to do their part in a world where self-directed and lifelong learning is a necessity and not a luxury. It is only through the collaborative efforts of these stakeholders that the potential of learning analytics to improve student performance can be fully realised.
This article has been adapted from an earlier commentary: "Analytics can help universities better support students’ learning” by Professor Koh Hian Chye, Director of Business Intelligence & Analytics, Singapore University of Social Sciences (SUSS).