In February 2013, the European Commission published a survey on “ICT use in Education” that revealed several key-findings, which shaped the European view on the use of computing devices for learning. One of the key findings is the need for such solutions to enter learning environments in a more interactive way; not only in the lesson’s preparation phase by the tutor as is the most common use nowadays, but also the need to have a more general ICT use that serves the educational process outside a dedicated learning venue (classroom, enterprise, etc.) as well. Moreover, in the special case of intellectually disabled children, the use of ICT has been extensively tested during the last decades and has now reached a level of maturity, where targeted solutions can be applied, going beyond the sphere of research and, thus, creating a new potential market.
MaTHiSiS will assist the educational process for learners and their tutors and caregivers by creating a novel and continuously adaptable "robot/machine/computer"-human interaction ecosystem to enhance vocational training, workplace learning and mainstream education for individuals with or without learning disabilities.
One of the core objectives of the project is to enter the learning environments and make use of computing devices in learning in a more interactive way, which will provide a product-system to be used in formal, non-formal and informal education. An ecosystem for assisting learners/tutors/caregivers for both regular learners and learners with special needs will be introduced and validated in 5 use cases: Autism Spectrum Case, Profound and Multiple Learning Disabilities Case, Mainstream Education Case, Industrial Training Case and Career Guidance Distance Learning Case.
MaTHiSiS product-system consists of an integrated platform, along with a set of re-usable learning components (educational material, digital educational artifacts etc.), which will respond to the needs of a future educational framework, and provide capabilities for: i) adaptive learning, ii) automatic feedback, iii) automatic assessment of learner’s progress and behavioral state, iv) affective learning and v) game-based learning.
Within MaTHiSiS an innovative structural tool of learning graphs is going to be introduced to guide the learner though the process of learning in the given scenario. To reach a learning objective, learner will have to “follow the path” of the learning graphs, built up on Smart Learning Atoms, which are a certain learning elements that carry defined learning materials.
To ensure barrier free integration in the market, MaTHiSiS will make use of a range of interaction devices, such as specialized robots, mobile devices and whiteboards. The consortium will ensure easy-to-use solution with e.g.specialized graphical editor-like tool, allowing to easily create educational materials as well as the reusability within both mainstream education and vocational training setups.
It will provide a “product-system” consisting of an integrated multi-agent interactive platform, along with a set of reusable learning components (educational material, digital educational artifacts, etc.) that will guide the deployment of the users’ learning activities. Its educational scheme will be based on custom-made and adaptable learning goals and educational material.
A Cloud-based Learner’s Space (CLS) will be developed to provide storage and interaction system for adaptation/personalization in learning, profiles storage, interaction, data acquisition and analysis as well as content creation on the fly. This is a core component of the MaTHiSIS system which include 4 crucial subsystems that create an innovative smart learning ecosystem: i) the experience engine, a graph-based interactive storytelling engine, that generates interactive content that is later sent to a device of tutor’s/learner’s choice; ii) the learning graph engine, responsible for adaptation of the Learning Graph based on learner’s behavior and interaction; iii) the Decision Support System (DSS) providing and collecting learning analytics and controlling synchronous and asynchronous interaction between devices; and iv) Profile Repository to store collected data and learning graphs for learners profile. To ensure constant educational flow and augmented learner engagement, the emotion recognition and context aware cognitive/behavioral status extraction tools are going to be introduced within the system addressed by the Sensorial component.
For the purpose of validating MaTHiSiS approaches in learning environment, a set of Smart Learning Atoms (SLA) is going to be created for defined use cases. Such SLAs will adapt to each learner in a different way based on her/his particular needs, cognitive affective state, relevance to specific learning requirements and previous performance. Further, an editor-like tool will be introduced to be able to transform educational material into SLAs. The learning graphs then are going to be deployed to interact with the CLS as well as a front-end tool for tutors and caregivers to enable creation, editing and authoring of the learning material.
It will support learning across a variety of learning contexts and, with the use of a variety of devices (robots, interactive boards and mobile devices), with personalized and adaptable, time and location independent learning paths, being transferred between the agents, always taking into consideration best knowledge and practices learnt from the previous device.
By the end of the project, it will introduce a marketable innovation, aimed at the re-usability of educational and training content and fostering the interactivity between technology and learners/tutors/caregivers.