Learner's Cognitive State Research Articles

Automated discovery of symbolic laws governing skill acquisition from naturally occurring data.

Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack generalizability. This paper aims to unearth the laws of skill learning from large-scale training log data. A two-stage algorithm was developed to tackle the issues of unobservable cognitive states and an algorithmic explosion in searching. A deep learning model is initially employed to determine the learner's cognitive state and assess the feature importance. Symbolic regression algorithms are then used to parse the neural network model into algebraic equations. Experimental results show that the algorithm can accurately restore preset laws within a noise range in continuous feedback settings. When applied to Lumosity training data, the method outperforms traditional and recent models in fitness terms. The study reveals two new forms of skill acquisition laws and reaffirms some previous findings.

USING THE STUDENTS' STATE INDICES FOR DESIGN OF ADAPTIVE LEARNING SYSTEMS

The paper discusses what scientific ideas could be used to increase effectiveness of adaptive learning systems. Possibilities to use changes of learner's cognitive state indicators under influence of internal (heart rate and blood pressure) and external (speed and density of solar wind) factors are discussed. It is described experience of use of the learner's cognitive state assessment (accounting psychological, physiological and external parameters) to assess his/her cognitive changes over weeks. The experimental results demonstrated individual nature of subjects' psychological and physiological changes over observation time (1,5 month) and their relationship. The authors' approach is based on the model describing formation and functioning of the “functional system of cognitive activity”. The question discussed is: what indices of a human performance indicators (behavioral, internal and/or external) could be useful for model construction? A human cognitive load can be under influence of different external and internal factors that can provoke his/her performance degradation. The results demonstrated the similar tendency for studied groups of subjects in previous studies: individual nature of changes of cognitive and physiological indices in day-to-day performance, as well as potentially significant influence of parameters of solar wind on them. As a result, such type of measurements of internal (physiological) and external (solar physics) parameters in combination with test performance indices could be used for assessment and prediction of effectiveness of cognitive activity, and adaptation of learning process according to the particularly learner's readiness to learn on a particular day and time. Different approaches and methods were proposed to take into account a human state, abilities, individual features to plan a human activity. Looking at learning as a type of activity in human-system integration, it is possible to consider today’s learner as an operator-researcher who acts in digital environment. At the same time, a human and tools of activity need mutual adaptation in complex systems. Psychophysiological model of learning and cognitive abilities development could be a basis for more effective design of learning achievements, organization and process, their quality, namely for adaptive learning on the base of accounting a learner's current cognitive state indices. Those results could be applied in design of adaptive learning systems, as it was made for industry in the previous developments of the authors. Principles of use of student's state indices in adaptive learning systems are proposed.

SPARQL-based personalised E-Learning system designed using ontology (SPELSO): an architecture

Today the key issues in implementing an efficient E-Learning system are sharing and reusability of learning content, integrating heterogeneous resources available online and providing personalised content and services. Through our work we put forward SPELSO (SPARQL based personalised E-Learning system designed using ontology), an E-Learning system based on ontology of E-Learning (OntEL) as the underlying knowledge-modelling structure which is used to capture various pedagogical aspects of E-Learning like course/subject details, learning object type, learner profile information (including learner's cognitive state), access type, difficulty level of learning resources etc. We give an SPARQL based algorithm of generic and personalised search for learning content targeted on the knowledge base created through OntEL. Furthermore, we define the notion of current learning status of a learner which is used to capture his cognitive state at any point of time during learning process. Default and personalised learning path provides suggestive sequencing of learning entities to users.

Adaptive Learning with the LS-Plan System: A Field Evaluation

LS-Plan is a framework for personalization and adaptation in e-learning. In such framework an Adaptation Engine plays a main role, managing the generation of personalized courses from suitable repositories of learning nodes and ensuring the maintenance of such courses, for continuous adaptation of the learning material proposed to the learner. Adaptation is meant, in this case, with respect to the knowledge possessed by the learner and her learning styles, both evaluated prior to the course and maintained while attending the course. Knowledge and Learning styles are the components of the student model managed by the framework. Both the static, precourse, and dynamic, in-course, generation of personalized learning paths are managed through an adaptation algorithm and performed by a planner, based on Linear Temporal Logic. A first Learning Objects Sequence is produced based on the initial learner's Cognitive State and Learning Styles, as assessed through prenavigation tests. During the student's navigation, and on the basis of learning assessments, the adaptation algorithm can output a new Learning Objects Sequence to respond to changes in the student model. We report here on an extensive experimental evaluation, performed by integrating LS-Plan in an educational hypermedia, the LecompS web application, and using it to produce and deliver several personalized courses in an educational environment dedicated to Italian Neorealist Cinema. The evaluation is performed by mainly following two standard procedures: the As a Whole and the Layered approaches. The results are encouraging both for the system on the whole and for the adaptive components.

Beyond words: the importance of gesture to researchers and learners.

Gesture has privileged access to information that children know but do not say. As such, it can serve as an additional window to the mind of the developing child, one that researchers are only beginning to acknowledge. Gesture might, however, do more than merely reflect understanding-it may be involved in the process of cognitive change itself. This question will guide research on gesture as we enter the new millennium. Gesture might contribute to change through two mechanisms which are not mutually exclusive: (1) indirectly, by communicating unspoken aspects of the learner's cognitive state to potential agents of change (parents, teachers, siblings, friends); and (2) directly, by offering the learner a simpler way to express and explore ideas that may be difficult to think through in a verbal format, thus easing the learner's cognitive burden. As a result, the next decade may well offer evidence of gesture's dual potential as an illuminating tool for researchers and as a facilitator of cognitive growth for learners themselves.