We devised SP3, a novel observational scheme that can be used to understand children’s solo interactions with MBLT, and applied it to identify and extract children’s evoked play and problem-solving behaviour. We continuously and unobtrusively monitored children’s learning experiences using MMD collection via eye-trackers, wristbands, Kinect joint tracking, and a web camera. We present an in-situ study where 26 children, ages 10–12, solved a motion-based sorting task for learning geometry. However, the use of an MMD mixed methods approach that combines qualitative and MMD data to understand children’s behaviours during engagement with MBLT is rather unexplored. Combining MMD with more traditionally exercised assessment tools, such as video content analysis, provides a more holistic understanding of children’s learning experiences and has the potential to enable the design of educational technologies capable of harmonising children’s cognitive, affective and physiological processes, while promoting appropriately balanced play and problem-solving efforts.
Such data can be used to uncover cognitive, affective and physiological processes during learning activities. The proliferation of sensor technology has driven the field of learning technology toward the development of tools and methods that may benefit from the produced Multi-Modal Data (MMD). Motion-Based Learning Technologies (MBLT) offer a promising approach for integrating play and problem-solving behaviour within children’s learning. The study has demonstrated literature analysis from various perspectives, which will provide evidence for researchers to devise novel solutions in the field. The current study has considered presenting a detailed overview of the literature available in the area of research. Learners can be provided with a very reliable and interactive environment based on artificial intelligence. Due to AIM, there are a lot of intelligent and interactive tools available for the efficient and effective learning of music. For the better creation and composition of music, a good quality of knowledge about musicology is essential. The practical and productive audio of a game can guide visually impaired people during other events in the game.
With computer-assisted technologies, game designers can create sounds for different scenarios or situations like horror and suspense and provide gamers with information.
The quality of the sounds in the game directly impacts the productivity and experience of the player. Sound effects in games are very effective and can be made more attractive by implementing AI approaches. Artificial intelligence and music (AIM) is one of the emerging fields used to generate and manage sounds for different media like the Internet, games, etc. It is very convenient for composers to compose music of high quality using these technologies. AI-based innovative and intelligent techniques are revolutionising the music industry. Among these applications, music is one that has gained attention in the last couple of years. With the development and advancement of information technology, artificial intelligence (AI) and machine learning are applied in every sector of life.