Personalized learning

For years, educators have advocated the transformative potential of personalized learning, which can be broadly defined as an approach centred on the individual learner. Flexible and responsive to their needs, its promise lies in the ability of tech to tailor learning based on the needs and performances of individuals.

So in the last decade, advancements in information and communications technology, data science and machine learning have led to an explosion in the development of tech-enabled solutions, helping to boost the popularity of personalized learning.

These solutions have shown some evidence in improving learning outcomes in developing countries, especially in terms of closing the education gap for lower-attaining students. However, evidence from developing countries is still emerging, and research remains thin.

UNICEF Innocenti wanted to dig deeper, so began a landscape analysis of personalized learning solutions in low- and middle-income countries (LMICs).

The objective of the analysis is to:

• Build understanding of how tech-enabled personalized learning solutions are being operationalized in LMICs, including positive trends, promising practices, and gaps.
   
• Cut through the hype and provide balanced insights to inform policy discourse and markets on personalized learning design and implementation.