Use the future to build the present
Educational Sensing
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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:

4.3.2Educational Sensing

We can now observe and examine learning practices using digital technologies. By gathering and analysing anonymised data using computer-based vision technology, student-held devices and other tools, researchers are beginning to make sense of the best practices in teaching, and to understand what enables effective learning.4

When focussed on the science of teaching, sensing tools allow us to expand our understanding of teachers as learners and as agents of change in education. They also facilitate the provision of constructive feedback about their instruction, avoiding the pitfalls of memory limitations and bias.5 When focussed on learners, digital tools combined with machine learning algorithms, can provide a range of insights.6 They can, for example, differentiate students who are struggling from those who are just avoiding effort.7 Collected data can include factors such as student and teacher locations and proximity to one another, gaze direction, classroom conversations, student engagement, participation, facial expressions, and hand raises, all of which can help in improving learning outcomes.

As these tools improve, the insights gained can be applied in teacher-training programs and in the development of new teaching resources, as well as disseminated through teaching forums, professional development courses and other outlets for innovating in teaching practice. At the same time, care must be taken to build safeguards against both deliberate and inadvertent misuses of this powerful technology. It is also worth noting that, although these kinds of learning resources are currently likely to be available only where resources are plentiful, it is possible their use would greatly benefit practitioners in poorer areas of the globe.

Future Horizons:

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5-yearhorizon

Frameworks for sensing are established

Data-protection, privacy and ethics standards for sharing data are agreed. New metrics are developed to better understand how best to use information gathered in classrooms. Outcomes of classroom-based research begins to feed into teacher-training programs. Dashboards for students, parents/guardians and teachers lead to better understanding and deeper engagement.

10-yearhorizon

Sensing technology goes mainstream

Classrooms are routinely equipped with sensing technology to observe learning, while AI processes data in real time to offer suggestions for enhanced learning. Behavioural data from body and eye trackers will help fine tune teaching methods and help better understand learner characteristics such as executive function. New sensor technologies emerge that diversify from purely visual and audio input allow greater study of collaboration skills and how they can be learned.

25-yearhorizon

AI and wearables change the learning experience

Wearable technology enables teachers and students to receive real-time feedback, direction and assistance during learning. Machine learning algorithms process learning data and provide tailored learning journeys.

Educational Sensing - 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.

GESDA Best Reads and Key Resources