Technology-enhanced Personalised Learning

This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it is not clear yet whether practical implementation actually works.The promise of technology-enhanced personalised learning is worth pursuing, and some tools have been built that respond to individual learning needs, aiming to reduce achievement gaps, or to put the student in control of their learning. However, the evidence also clearly shows that technology-enhanced personalised learning is not a silver bullet. We hope this study can make a valuable contribution to improving the quality of teaching in German schools and elsewhere.

×

Motivation

The educational system in Germany and elsewhere has not yet succeeded in providing all students with equal educational opportunities. Consequently, the call for personalisation of the learning experience is frequently heard.

In real life, however, this approach entails major challenges, both for students and teachers. It also remains unclear whether technology can help. In fact, technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with untangling the evidence.

This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it is not clear yet whether practical implementation actually works. Even in countries that lead the way, there are few robust evaluation studies.

We hope this study can make a valuable contribution to improving the quality of teaching in German schools and elsewhere. We are also convinced that high-quality teaching will remain crucial – to give all learners equal opportunities to succeed in this new world.

The promise of technology-enhanced personalised is worth pursuing, and some extraordinary tools have been built that respond to individual learning needs, aiming to reduce achievement gaps, or to put the student in control of their learning. However, the evidence also clearly shows that technology-enhanced personalised learning is not a silver bullet.

Time, effort, resources and a cultural shift are needed to realise and implement the necessary reforms, in order that schools, teachers and students can best leverage the many potential benefits of technology-enhanced personalised learning. Put simply, it is important not to be seduced by exciting technologies, especially if there is little supporting evidence, and to always start with the learning.

×

Untangling the Evidence

Whether or not technology can help deliver personalised learning in the classroom remains an open but important question. There is much promising evidence. However, if we are to enable educators and policymakers to make good informed decisions about how best to personalise learning with technology in schools, we need to go beyond the face-value of that evidence (which all too often can be uncritically positive).

Technology-enhanced personalised learning can also be at the expense of social learning opportunities for students, while algorithms can all too easily reinforce stereotypes.

In this report, we aim to support decision making by providing an illustrated framework of analysis for individual technology-enhanced personalised learning tools and a set of evidenced-based principles for implementing such tools. These have been designed to enable teachers and policy makers to draw their own conclusions about any technology-enhanced personalised learning tools that they encounter.

×

About the Project

This report and website was commissioned by the Robert Bosch Stiftung.

The Robert Bosch Stiftung GmbH  is one of Europe’s largest foundations associated with a private company. In its charitable work, it addresses social issues at an early stage and develops exemplary solutions. It is active in the areas of health, science, society, education, and international relations. Since it was established in 1964, the Robert Bosch Stiftung has invested more than 1.4 billion euros in charitable work.

The report was written by Dr Wayne Holmes and Dr Stamatina Anastopoulou (with Institute of Educational Technology, The Open University, UK), Dr. Heike Schaumburg (with Institut für Erziehungswissenschaften der Humboldt-Universität zu Berlin), and Dr Manolis Mavrikis (with UCL Knowledge Lab, UCL, UK).

Dr Wayne Holmes is a Lecturer (Assistant Professor) in Learning Sciences and Innovation. He received his PhD in Education from the University of Oxford and is currently leading several learning and innovation research projects.

Dr Stamatina Anastopoulou is a Lecturer (Assistant Professor) in Evaluation of Technology-enhanced Learning at Scale. She has recently achieved a prestigious Marie Sklodowska-Curie Award to research personalised learning inquiries in museums.

Dr Heike Schaumburg teaches and conducts research at the Department of Education at the Humboldt University Berlin. She has led research into the influence of digital media on teaching in laptop classes, the conditions of the integration of digital media in school and their learning effectiveness.

Dr Manolis Mavrikis is an Associate Professor in Learning Technologies. His research centres on designing evidence-based intelligent technologies that provide direct feedback to learners, and in employing learning analytics to help understand the processes involved in learning.

The authors would like to thank Professor Nikol Rummel, Dr Mutlu Cukurova, Dr Junaid Mubeen, Dr Liz FitzGerald, Professor Neil Heffernan, Professor Denise Whitelock and Professor Eileen Scanlon for their critical support.

Graphics copyright Pen Mendonça www.penmendonca.com

This website has been created by Dr Manolis Mavrikis, a researcher at University College London (UCL), and Dr Wayne Holmes, a researcher at The Open University (OU) with Technology Research and Experimentation Ltd that contributed in the design, implemented and maintains it. It is based on the report “Technology-enhanced Personalised Learning. Untangling the Evidence” and was funded by Robert Bosch Stiftung GmbH (RBSG). The website and its materials do not represent the views of RBSG or UCL, OU or TREX.

Warranties

The website and its materials are provided for general information purposes only. We make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability or availability with respect to the website or the information or related graphics contained on the website for any purpose. Any reliance you place on such information is therefore strictly at your own risk.

In no event will the website creators or RBSG or UCL or OU be liable for any loss or damage including without limitation, indirect or consequential loss or damage, or any loss or damage whatsoever arising from loss of data or profits arising out of, or in connection with, the use of this website.

GDPR

This website does not collect any personal data. We employ Google Analytics for tracking the usage of the website for the purposes of monitoring dissemination and potential impact. Google Analytics is customised to anonymise the IP addresses of the user. As such it is an “information only” static website that does not collect “any information that relates to an identified or identifiable living individual”, https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en . Accordingly, the provisions of GDPR do not apply.

Click on the image to learn more

Download the report

Dimensions of Technology-enhanced Personalised Learning

What is meant by personalised learning is complex. For example, personalised learning can mean different things (such as independent learning or individual competencies) in different contexts, while it particularly depends on who makes the decisions (policymakers, teachers or students).
One way through this complexity is to consider personalised learning in terms of its multiple dimensions, that is the personalisation of why, how, what, when, who, and where.

To learn more about this and other issues, read the report.

Back to the top

Click on the jigsaw pieces to learn more

Understanding Technology-enhanced Personalised Learning

Why?

The personalisation of why something is to be learned (the learning aims, which are typically the decisions of policymakers) extends from preparing for exams to enabling learners to have agency when making choices.



How?

The personalisation of how something is to be learned (the learning approach) extends from involving whole-class instruction to providing opportunities for student-led learning and helping them to self-regulate their learning.



What? (content)

The personalisation of what is to be learned (the learning content) extends from subjects such as mathematics, where problems and solutions are clearly defined, to life-skills which are not so well defined.



What? (pathways)

The personalisation of what is to be learned (pathways) extends from whole-class pathways to personal learning pathways to address each student’s difficulties or successes.



When?

The personalisation of when something is to be learned (the learning pace) extends from a single pace for the whole class to different speeds for each individual students (building on the learner’s cognitive strengths, weaknesses and learning goals).



Who?

The personalisation of who is involved in the learning (the learning group) extends from whole classes, to smaller groups, to individual students.



Where?

The personalisation of where the learning takes place (the learning context) extends from inside classrooms to outside, across multiple classes or out of school.
Download the report

Implementation Challenges

  1. Implementation needs to be considered as part of whole-school reform, with training and time allowances for new practices to settle.
  2. Regardless of the vision, existing inequalities may be reinforced not reduced.
  3. Protecting student Internet safety can be in opposition to enabling students to take active control of their own learning.
  4. Technology-enhanced personalised learning can be at the expense of inclusive support for the class, and social learning opportunities for all students.
  5. Infrastructure requirements can be a huge challenge when implementing technology-enhanced personalised learning (e.g., what does a teacher do when the technology stops working, as it all too often can do).
  6. Technology-enhanced personalised learning can require massive amounts of student data, which can compromise student privacy.
  7. Algorithms can reproduce existing stereotypes.
  8. Technology-enhanced personalised learning has nothing to say about the why question (why something in particular needs to be learned).
  9. Technology-enhanced personalised learning also has nothing to say about the how question (how something is to be learned). Many use a didactic or instructionist approach to learning that in conventional classrooms is often avoided.

To learn more about this and other issues, read the report.

Back to the top

Download the report

Things to question

+ Question the technology

  • How will this particular technology help me achieve curriculum aims, and how is it aligned to curriculum objectives and assessment criteria?
  • How will this particular technology support students to achieve their full potential?
  • How does the proposed technology work in simple terms, what do its algorithms do, what data it does it use, and how does it use that data?

+ Question the integration

  • How will this particular technology fit with existing infrastructure (e.g. Internet bandwidth or existing computers), and what will need to be adjusted or replaced?
  • Ask about logins (for example, how are student passwords, or other methods of secure access, managed?).
  • Who would be accountable for dealing with everyday queries from teachers and troubleshooting?
  • Ask about the costs (the technology, your investment in time, students’ investment in time, technical support).

+ Question the professional development

  • How much time is recommended for professional development? Is it included in the purchase costs?
  • How can I be supported in planning my lessons, so that I use the technology to best effect?
  • How can I monitor student usage (not just how much time they are using it, but their achievements and misconceptions) without impacting on student agency?

+ Question how the TePL addresses the dimensions of personalised learning

  • How does the system personalise the learning approach (the how dimension)?
  • How the system personalises the learning content and the learning pathways (the what dimension)?
  • How does the system enable appropriate learning groups (the who dimension)?
  • How does the system enable appropriate learning contexts (the where dimension)?
  • Always recognise that the personalisation of why something is to be learned depends on the policymakers.

To learn more about this and other issues, read the report.

Back to the top

Recommendations for research and practice

Most funded innovation projects usually aim at the development of a technology rather than its robust evaluation. Evaluations tend to focus on usability and student attitudes in small settings, rather than effectiveness and sustainability at scale.

  • Developers and researchers should consider using a design-based research approach, to produce effective educational interventions combined with theory.
  • Teachers, students, and parents should be involved in the heart of the research and development process.
  • The research and development process should include both ongoing formative and summative evaluations at scale, using both qualitative and quantitative approaches.

Recommendations for policy-makers, foundations and NGOs

  • Governments and philanthropists should consider guaranteeing longer-term funding and a market for TEPL tools that have been shown to work in real life settings (albeit at small scale).
  • Policy-makers and funders should consider funding capacity building (including teacher training), helping to ensure that technology-enhanced personalised learning research is both sustainable and cumulative: projects should be funded that build on previous innovations, as well as intelligent evaluation in real world settings at scale.
  • A range of stakeholders should be involved throughout a research project’s life.

To learn more about this and other issues, read the report.

Back to the top