What we are learning | What we are reading: Digital Personalised Learning – How can technology be used to maximise the effectiveness of personalised learning and teaching at the level of the student?


This is Part 3 of a six-part blog series inspired by discussions with partners working in the EdTech space who recognise they do not have the information they need to make informed decisions and recommendations. So, we have put together a series summarising what we have learned so far in each of our focus areas: Data for Decisions, Digital Personalised Learning (DPL), Girls’ Education & Technology, Participation & Messaging, and Teacher Continuous Professional Development. We provide some key lessons, an outline of our ongoing work, and some additional resources (written by our partners and by us) for readers who want to delve deeper. You can read Part 1 and Part 2

In everyday practice, pedagogical approaches can personalise learning to varying degrees. For instance, teachers can provide students who are struggling in their learning with extra support. However, higher student-to-teacher ratios in low- and middle-income countries make it difficult for teachers to manage classrooms. As a result, teaching is often not targeted at the level of the student. A significant number of students do not learn effectively and hence become disengaged. These learning gaps can eventually lead to students dropping out, with marginalised learners at greater risk.

With the affordances of EdTech, digital personalised learning (DPL) can align students’ learning experiences with many aspects, including individual needs, attainment levels, interests, and cultural relevance. Significant financial resources are being invested in a wide range of DPL programmes, but there is no clear evidence on whether they are more effective as a complement or substitute to other learning content and how they might be used in low- and middle-income countries. 

If we can develop clear evidence on pedagogically appropriate, cost-effective DPL, we can influence how money is spent on personalised learning. By using evidence to guide investment in these programmes, we have the potential to see increased impact on learning outcomes.

We are working towards this goal through research, the production of global goods, and provision of advice with a number of partner organisations. Some of our ongoing efforts include:

  • Working with Oppia and EIDU in Kenya to carry out in-depth research into the questions of if and how DPL can improve learning, how DPL platforms can be used to implement and scale a Teaching at the Right Level approach, how to implement DPL best in low-resourced environments, and its cost.
  • Working with M-Shule in Kenya to understand if gender affects learning through SMS, how messages can be tailored to improve learning among girls, and determining the costs of good implementation.
  • Partnering with onebillion in Malawi to run a sandbox on the onetab solution to explore the opportunities and barriers associated with scaling digital personalised learning through tablets.

EdTech Hub is building evidence on personalised learning in low-resourced environments through funding at-scale research, in partnership with academics from world-renowned universities, and through testing the effectiveness and scalability of interventions in our focus countries. Our work in personalised learning can be mapped across two dimensions:

  1.  The level of personalisation
  2.  The level of technology 

The level of personalisation ranges from 1) a system that requires a facilitator to 2) an adaptive system that determines the level at every use to 3) an adaptive system that builds on previous usage through user accounts. The level of technology used across these interventions ranges from low-tech (phone / SMS / radio) to smartphones and tablets.

While our partnership with M-Shule unpacks how low-tech solutions (e.g. SMS) can be used to improve learning for girls in Kenya, our work on high-tech solutions addresses scalability challenges. Through our partnership with onebillion, we are finding that cost-effectiveness is a significant consideration when designing the implementation of a high-tech intervention. Two methods of reducing costs include increasing the number of children per device and the lifetime of hardware through effective maintenance.

What are some of the lessons from our work?

Technology-supported personalised learning has a statistically significant, positive effect on learning outcomes. Its interventions are similarly effective for mathematics and literacy and whether or not teachers also have an active role in the personalisation. Personalised approaches that adapt or adjust to the learner have been shown to lead to significantly greater impact.

However, whether these approaches warrant the additional investment likely to be necessary for implementation at scale needs to be investigated. 

In successful cases, there is evidence that personalised technology implementation of moderate duration and intensity can have similar positive effects to that of implementation of stronger duration and intensity, although further research is needed to confirm this.

Key lessons include:

  • Research shows promise but is relatively scarce.  Although research in DPL shows that personalisation is promising, the body of research is not as robust as in other EdTech Hub focus areas. 
  • Teaching at the right level seems to be the most effective. An approach that adjusts the intervention to the learner’s level seems more effective than approaches that have learners determine their own path or those that rely heavily on assessment. 
  • DPL does not have to be continuous or integrated to be effective. Short-term interventions of low intensity might have positive effects that are similar to longer-term interventions. 
  • DPL seems to have the potential for low-attaining groups. Low-attaining learners may have more learning gains from DPL than high-attaining learners, which creates opportunities for learners returning to school after a disruption or low-performing populations.

What should you read next?

  1. Rapid Evidence Review: Technology-supported personalised learning (EdTech Hub)
  2. Summary Brief: Problem Analysis and Focus of EdTech Hub’s Work (EdTech Hub)
  3. The potential of using technology to support personalised learning in low- and middle-income countries (EdTech Hub blog)
  4. Scaling personalised learning technology in Malawi (EdTech Hub sandbox, with onebillion)
  5. The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis (EdTech Hub funded)
  6. A literature synthesis of personalised technology-enhanced learning: what works and why (The Open University)

Coming Next: Our reading recommendations for one of our five focus areas – Girls’ Education & Technology. And you can always find more in our Evidence Library.

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