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Cognitive technologies
Published in Alex Mihailidis, Roger Smith, Rehabilitation Engineering, 2023
Computer-driven software to assist with learning in an educational setting can be extremely helpful for children and young adults with learning disabilities who can benefit from customization and strategies aimed at maintaining engagement (Weng, Maeda, & Bouck, 2014). Additionally, students with disabilities can benefit from features within an e-learning environment that can adapt to their needs and assist in learning. The ideal educational computer technology or software not only provides accessible content, it also is accessible in the design and function of the computer or software. This includes functions for people with visual or hearing impairments, such as screen reading or closed captioning. It also includes personalization functions for people with other cognitive or mobility impairments, including preference changes for larger buttons or text, control settings, and compatibility with other assistive technology that allows users with cognitive impairments to interact with accessible learning content (Laabidi, Jemni, Jemni Ben Ayed, Ben Brahim, & Ben Jemaa, 2014). For educational purposes, computer-driven software should use proven instructional methods with adaptive preferences for personalization, be aimed at maintaining attention and engagement, and provide useful feedback, either to an instructor or a student (Weng et al., 2014).
Overview of AI in Education
Published in Prathamesh Churi, Shubham Joshi, Mohamed Elhoseny, Amina Omrane, Artificial Intelligence in Higher Education, 2023
Archana Bhise, Ami Munshi, Anjana Rodrigues, Vidya Sawant
A teacher in a typical educational system teaches a class of 30 to 60 students, all along the same learning path. One of the main challenges experienced by teachers is to address the needs of the fast and slow learners in a class simultaneously. Personalized learning is an educational approach that customizes the learning plan of each student according to their strengths, needs, skills, and interests (Somasundaram et al., 2020). Personalized Learning requires someone to design a learning journey that is curated or created specifically for a particular learner and/or learning objective. However, creating a customized plan, tracking the progress and providing timely feedback to each student is a huge challenge.
Faculty perception before, during and after implementation of standards-based grading
Published in Australasian Journal of Engineering Education, 2018
Eunsil Lee, Adam R. Carberry, Heidi A. Diefes-Dux, Sara A. Atwood, Matt T. Siniawski
SBG was first developed during the 1990s when all US states reformed public K-12 education by setting academic standards (Marzano 2011; Reeves 2003). The system is based on backward course design, which establishes clearly articulated course objectives at the beginning of the course with a system of well-developed rubrics (Post 2014). Student strengths and weaknesses in regards to the objectives are monitored and assessed using student work throughout the duration of a course. Personalised and meaningful feedback is provided to each student allowing them to track individual learning and development. The system has been shown to orient students towards learning, rather than towards a grade, by giving them the opportunity to self-evaluate and self-learn (Atwood, Siniawski, and Carberry 2014). This redirection redefines their role as a learner in the learning process (Heflebower and Hoegh 2014).
Constructing a Demand-Driven College English Learning Environment in Higher Education Institutions
Published in Applied Artificial Intelligence, 2023
There are several potential future directions for the development of smart learning environments that could significantly enhance the learning experience for students. Some of these directions include: Integration of Augmented Reality and Virtual Reality: Smart learning environments could incorporate augmented reality and virtual reality technologies to create more immersive and engaging learning experiences. Learners could interact with 3D models, simulations, and virtual environments, making learning more interactive and exciting.Gamification of Learning: Gamification is the use of game elements in non-game contexts, such as education. Smart learning environments could utilize gamification techniques to enhance student motivation and engagement. This could involve incorporating game elements such as points, badges, and leaderboards into the learning environment.Personalized Learning: Smart learning environments could use advanced algorithms to create a personalized learning experience for each student. These algorithms could analyze student performance data to identify individual learning needs, and provide tailored learning resources and feedback.Collaborative Learning: Smart learning environments could facilitate collaborative learning experiences among students by incorporating tools for online collaboration, such as discussion forums and video conferencing. This could create a more social and interactive learning environment, allowing students to learn from one another.Adaptive Learning: Adaptive learning involves using algorithms to adjust the learning experience based on student performance. Smart learning environments could incorporate adaptive learning techniques to provide students with personalized challenges and support based on their individual learning needs.