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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
Adapt 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
Sense 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
Infer 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
Learn 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
Anticipate Ability of thinking or reasoning to predict what is going to happen or what to do next.   • Predictive engine (predictive analytics)  
Self-organize 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.