From standardisation to customisation
From industrial-era mass education to individualised, digitally-enabled learning paths.
- The industrial era required large numbers of workers with medium-level skills that could be acquired through standardised education systems. The rise of the knowledge-based economy saw a rise in the share of high-skilled occupations and a hollowing-out of medium-level skilled jobs.
- Modern-day economies are increasingly built around human capital (rather than industrial machines), and there is therefore a much stronger need to maximise everyone’s potential.
- One-size-fits-all is unlikely to work in this new environment. Individualised learning paths can help students and workers to develop their innate talents and capabilities.
- Personalisation of learning requires a major change in the organisation and delivery of education and on-the-job learning, recentring it around personal progress.
- Up till now, high costs were considered as an insurmountable obstacle to scaling up such approaches because introducing a truly individual approach on a wide scale would mean hiring a significant number of teachers or learning coaches. Thanks to the emergence of new technologies and resources this may no longer be the case.
Learning can be transformed into a customised experience where individuals approach problems in their own way, acquire knowledge and skills at their own pace, maximising outcomes not only for the most talented students, but also helping to bring those lagging behind up to speed.
The future is already here
Personalising classroom learning using data analytics: the case of Singapore
With the average primary school class consisting of up to 40 pupils, teachers have typically found it hard to personalise learning for each individual student.
The Government of Singapore is exploring innovative use of emerging technologies to enrich the learning experience and enhance the quality of teaching, enabling everyone to achieve their full potential.
A key feature includes personalised learning using analytics, whereby data relating to the student — on school attendance, test results, participation in class, as well as self-assessments and teacher assessments — is gathered and combined to draw out crucial insights into students’ learning strengths and difficulties. The goal is to help teachers build better pedagogical programmes, empower students to take an active part in their learning, target at-risk student populations through personalised interventions, and assess factors affecting completion and student success.