Use the future to build the present
Learning Analytics
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1Quantum Revolution& Advanced AI2HumanAugmentation3Eco-Regeneration& Geo-Engineering4Science& Diplomacy1.11.21.31.42.12.22.32.43.13.23.33.43.54.14.24.34.44.5HIGHEST ANTICIPATIONPOTENTIALAdvancedArtificial IntelligenceQuantumTechnologiesBrain-inspiredComputingBiologicalComputingCognitiveEnhancementHuman Applications of Genetic EngineeringRadical HealthExtensionConsciousnessAugmentation DecarbonisationWorldSimulationFuture FoodSystemsSpaceResourcesOceanStewardshipComplex Systems forSocial EnhancementScience-basedDiplomacyInnovationsin EducationSustainableEconomicsCollaborativeScience Diplomacy
1Quantum Revolution& Advanced AI2HumanAugmentation3Eco-Regeneration& Geo-Engineering4Science& Diplomacy1.11.21.31.42.12.22.32.43.13.23.33.43.54.14.24.34.44.5HIGHEST ANTICIPATIONPOTENTIALAdvancedArtificial IntelligenceQuantumTechnologiesBrain-inspiredComputingBiologicalComputingCognitiveEnhancementHuman Applications of Genetic EngineeringRadical HealthExtensionConsciousnessAugmentation DecarbonisationWorldSimulationFuture FoodSystemsSpaceResourcesOceanStewardshipComplex Systems forSocial EnhancementScience-basedDiplomacyInnovationsin EducationSustainableEconomicsCollaborativeScience Diplomacy

Sub-Field:

4.3.1Learning Analytics

In an age of big data, more use can and should be made of the digital information that is gathered in educational settings. Mining this data makes it possible to assess student progress, improve educational theory, prevent drop-out and create personalised learning programs, which are adaptable to suit the student’s strengths, goals and interests, and adaptive learning programs, which can deliver different kinds of content and different kinds of support through diverse means depending on the student’s situation, learning environment or even mood.

To fulfil the potential of this area, researchers need to work on a number of fronts. It is not yet clear, for example, whether using individual learner demographics as input to models will increase inequity. It is possible that predictive models may be too prescriptive about learners’ potential, and limit achievement expectations. It is also still necessary to identify exactly which datasets are most relevant to which aspects of education, and how best to mine and draw inferences from them.8

It is also important to find ways to present the results of data mining in ways that motivate and inspire teachers and learners to reflect on and understand their own learning processes and outcomes, and find ways to improve them.9 Clear and straightforward learning dashboards have enormous but as yet unrealised potential to have significant effects on educational outcomes.

Researchers are also looking to create tools that provide dynamic measurements of students’ cognitive states — including metacognition, emotion and motivation. These can assist in developing educational technologies that adapt learning goals and methods to a student’s state of mind, helping them to recognise, regulate and even create their own optimal learning state.10

Future Horizons:

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5-yearhorizon

Data-gathering becomes normalised

Educational institutions begin to see the results from data analysis and realise the benefits of increasing their data gathering and analysis. Analysis software becomes affordable and ubiquitous. Open data sharing and analysis platforms democratise the gains made through learning analytics. Digital platforms for teacher-to-teacher collaboration begin to emerge. The availability of data on which to test theories gives teachers the ability to perform “action research”, running their own experiments in their classrooms.

10-yearhorizon

Analytics help shape optimal careers

Students leave education with a digital portfolio of their learning journey, equipping them to make insightful next-step choices, and for employers to check aptitudes, skills and cognitive abilities without reliance on a few results from snapshot high-stakes tests.

25-yearhorizon

Smart tech optimises educational engagement

Machine learning algorithms with access to education datasets create and optimise personalised curricula and collaborative practices during progress through education to maximise engagement with and usefulness of educational opportunities.

Learning Analytics - Anticipation Scores

How the experts see this field in terms of the expected time to maturity, transformational effect across science and industries, current state of awareness among stakeholders and its possible impact on people, society and the planet. See methodology for more information.

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