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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.