Singapore will be piloting a personalised adaptive learning Mathematics solution for students, developed by Marshall Cavendish Education in partnership with Knewton. Supported by the Infocomm Development Authority of Singapore (IDA), the pilot project will begin in 2016 with interested schools.
The solution aims to transform students’ learning experience by recognising the different abilities of students and their diverse levels of understanding of mathematical concepts. It will adjust content to suit each student’s ability, automatically recommending pieces of content across grade levels and ensuring that students get the advanced or remedial content they need. This personalisation of content and learning pathways will encourage students to become more confident learners and will help promote a deeper understanding of Mathematics concepts.
eGov Innovation conducted an interview with Lee Fei Chen, Head of Publishing Group, Marshall Cavendish Publishing Group, and the Infocomm Development Authority of Singapore (IDA) on the new adaptive mathematics learning solution.
What challenges do teachers face in the classroom with regards to differentiated learning?
Lee: Having worked closely with teachers and schools in the development of teaching and learning solutions, Marshall Cavendish Education has often heard feedback that it is a challenge to attend to the diverse needs of individual students, especially with Singapore class sizes averaging 30–40 students in each class and a limited timespan to complete school activities. As such, in a mixed-ability classroom, teachers often focus on struggling learners and conduct remedial or supplementary lessons beyond normal school hours to help them catch up with their peers. Whilst this group of students may eventually get up to speed, their on-level peers may not be pushed further than they can achieve.
How does the adaptive learning solution work?
Lee: The Marshall Cavendish Education Adaptive Learning Solution will be powered by a content recommendation and learning analytics engine provided by our partner, leading U.S. adaptive learning technology company, Knewton. The solution will be accessible via web browsers through Windows and Mac devices.
Schools will have the flexibility of planning their own curriculum and integrating it into the system, which will deliver Marshall Cavendish Education’s content to students based on the school’s indicated Scheme of Work (SOW). Based on the schedule captured in the SOW module, the solution will automatically push instructional and assessment resources to the students for them to work on independently, which the solution will intuitively adjust according to students’ abilities and problem areas. At the same time, students’ performance will be analysed and reported back to them, giving them an overview of their own progress and empowering them to self-remediate where needed.
Teachers will also be able to monitor their class’ performance through a teacher dashboard that enables them to gain deep insights into each student’s learning progress, triggering early intervention for students who need extra support, and helping teachers enhance their existing learning programmes based on the findings.
What is the philosophy behind adaptive, differentiated learning?
Lee: Every student learns differently – the best way to help each student reach his or her maximum potential lies in understanding his or her strengths, weaknesses and skillsets so that the way the content is taught and the way it is reinforced is differentiated to bring out the best of each student’s abilities. As each student grows, their learning capacities change, resulting in the need to continuously adapt the content and teaching processes with students’ learning development. The key benefit is the ability to help bring struggling learners up to pace, stretch on-level learners and challenge the advanced ones further.
Learning analytics can support effective differentiated learning for every learner by providing an analysis and reporting of information within a digital learning product, which will in turn reveal information and connections that can predict and advise on learning. Drawing on students’ performance in assessments and practices, learning analytics will provide real-time inferences for predictive analytics and student reports. For example, the analytics engine could flag topic areas that merit a class-wide discussion or clarification, identify students who are struggling or excelling, or allow teachers to easily and logically group students for in-class activities whilst ensuring they stay aligned to the class curriculum. In this way, learning analytics will focus on addressing each student’s identified needs in class by giving teachers the insights to adapt and differentiate their curriculum and teaching approach for each student.
How does the solution benefit students and teachers?
Lee: Through recognising the different abilities of students and their diverse levels of understanding of concepts, the solution will automatically recommend the pieces of content a student should work on, keeping students attentive and interested in taking charge of their own learning. The solution will also recommend content across grade levels to ensure that students get the advanced or remedial content they need at precisely the moment they need it, keeping them continuously engaged and motivated to succeed whilst promoting a deeper understanding of concepts.
With a detailed dashboard, teachers will be provided with insights into how students are learning and where students need further support, empowering teachers to prioritise their limited time whilst better supporting learning for every student, in every class they teach. This information will be useful for teachers as they can devise teaching strategies to cater to the different students in their lesson preparation and the technology will enable them to consistently monitor and evaluate the effectiveness of their lessons through student responses. In this way, not only teachers but parents too can be assured that their children are receiving appropriate and relevant resources that will help them learn to mastery, and will be aware of their children’s weak areas that they can work on together at home.
What are some future plans for this technology?
Lee: With the findings from the pilot, Marshall Cavendish Education will be able to evaluate and determine how best to make this solution available to our customers. Though the solution and its pilot are still in early stages of development, we hope to be able to reach out to more schools, using what we’ve learnt from the pilot to continuously enhance the solution and provide teachers and students alike with holistic, yet personalised support throughout the teaching and learning journey.
How is the adaptive learning solution in line with IDA’s initiative to transform education with smart technology?
IDA: In line with the Infocomm Media 2025 plan to build Singapore’s Smart Nation, IDA explores the innovative use of technologies for teaching and learning. For example, learning analytics can provide educators with crucial insights into students’ learning strengths and difficulties. Marshall Cavendish Education’s adaptive learning technology helps create a learning experience that is highly tailored to each student, which will change the way we educate our children and help deepen the learning of Mathematics. With more targeted resources and experience delivered to students, they will be more motivated to learn on their own and track their own progress to meet the learning objectives. Teachers and even parents will be able to see their children’s potential strengths and weaknesses, and provide more specific guidance in their learning.
This solution will empower educators in Singapore to better support learning for every student in every Mathematics class they teach and more easily differentiate instructions. A detailed dashboard will provide teachers with insights on how students are learning and where students need further support, allowing teachers to maximise their limited time.