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Table 1 Smartness level of Smart Learning Environments with activities, technologies etc. (Adapted from Uskov et al., 2015)

From: Standards for smart education – towards a development framework

SLE levels Smart Classroom Activities Technologies involved Standardization challenges
Ability to modify physical or behavioral characteristics to fit the environment or better survive in it.
• Communicate (local & remote)
• Share content
• View content in a preferred language
• Initiate session with voice/facial/gesture commands
• Ask questions
• Present (local & remote)
• Discuss
• Annotate
• Web technologies
• Session-based analytics
• Personal digital devices
• VR and AR systems
• Presentation technologies (Smartboards, etc)
• Social media
• Sensors (air, temperature, number of persons, participation roles, ….)
• Setting up a SLE meeting quality criteria defined in Smart Classroom standards
• Data governance
• Privacy
• Security
• Systems interoperability
Ability to identify, recognize, understand and/or become aware of phenomenon, event, object, impact, etc.
• Automatic adjustment of classroom environment (lights, AC, temperature, humidity, etc.)
• Real-time collection of student feedback from diverse contexts
• Monitoring student activity
• Process real-time classroom data
• Deliver custom support and scaffolding for special needs students
• Support agent-based systems
• Interact with smart systems
• Connect multi-location students
• Triggers actions, defined in assorted models (learner, school, teacher, Smart Classroom, etc.)
• Big Data
• Multiple interfaces and channels keyboard, screen, voice, agent, eye movements, gestures
• Data collection and storage
• Data governance
• Privacy
• Security
Ability to make logical conclusion(s) on the basis of raw data, processed information, observations, evidence, assumptions, rules and logic reasoning.
• Recognize every individual
• Process real-time classroom data
• Process incomplete classroom datasets
• Discuss presented learning content and assignments with remote students in real-time and using preferred language by each student
• Simple rule-based process engines
• More complex inference engines
• Natural language processors
• Pedagogical designs
• Student learner models
• Student activity data
• Specifying competence
Ability to acquire new or modify existing knowledge, experience, behavior to improve performance, effectiveness, skills, etc.
• Ability to suggest changes to the system
• Real-time skills assessment
• Real-time knowledge assessment
• Accommodate and enact multiple intelligences
• Artificial Intelligence
• Machine Learning
• Deep Learning
• Validating competence
• e-assessment
• Learning Design
Ability of thinking or reasoning to predict what is going to happen or what to do next.
  • Predictive engine (predictive analytics)  
Ability of a system to change its internal structure (components), self-regenerate and self-sustain in purposeful (non-random) manner under appropriate conditions but without an external agent/entity.
  • All above, with a strong AI component.