Obtaining teaching expertise from F2F learning interactions

Learning environments are commonly used nowadays, but they exclude face-to-face interaction among teachers and students what is a successful basis of traditional education. On the other hand, in many cases teachers are imposed to use technology, what they do in an intuitive way. That is, teachers “learn by doing” and do not fully exploit its potential benefits. Consequently, some questions arise: How do teachers use F2F interaction to guide learning session? How can technology help teachers and students in their day by day? Moreover, are teachers and students really opened to be helped by technology? In this paper we present the formal process carried out to obtain information about teachers’ expertise and necessities regarding the direct interactions with students. We expose the possibilities to cover those necessities and the willingness that teachers show to be helped.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Learning feedforward commands to muscles using time-shifted sensory feedback

In adapting to changing forces in the mechanical environment, humans change the force being applied by the limb by reciprocal changes in the activation of antagonistic muscles. However, they also cocontract these muscles when interaction with the environment is mechanically unstable to increase the mechanical impedance of the limb. We have postulated that appropriate patterns of muscle activation could be learned using a simple scheme in which the naturally occurring stretch reflex is used as a template to adjust feedforward commands to muscles. Feedforward commands are modified iteratively by shifting a scaled version of the reflex response forward in time and adding it to the previous feedforward command. We show that such an algorithm can account for the principal features of changes in muscle activation observed when human subjects adapt to instabilities in the mechanical environment. © 2006.

On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.

Reinforcement learning with reference tracking control in continuous state spaces

The contribution described in this paper is an algorithm for learning nonlinear, reference tracking, control policies given no prior knowledge of the dynamical system and limited interaction with the system through the learning process. Concepts from the field of reinforcement learning, Bayesian statistics and classical control have been brought together in the formulation of this algorithm which can be viewed as a form of indirect self tuning regulator. On the task of reference tracking using a simulated inverted pendulum it was shown to yield generally improved performance on the best controller derived from the standard linear quadratic method using only 30 s of total interaction with the system. Finally, the algorithm was shown to work on the simulated double pendulum proving its ability to solve nontrivial control tasks. © 2011 IEEE.

On-line policy optimisation of Bayesian spoken dialogue systems via human interaction

A partially observable Markov decision process has been proposed as a dialogue model that enables robustness to speech recognition errors and automatic policy optimisation using reinforcement learning (RL). However, conventional RL algorithms require a very large number of dialogues, necessitating a user simulator. Recently, Gaussian processes have been shown to substantially speed up the optimisation, making it possible to learn directly from interaction with human users. However, early studies have been limited to very low dimensional spaces and the learning has exhibited convergence problems. Here we investigate learning from human interaction using the Bayesian Update of Dialogue State system. This dynamic Bayesian network based system has an optimisation space covering more than one hundred features, allowing a wide range of behaviours to be learned. Using an improved policy model and a more robust reward function, we show that stable learning can be achieved that significantly outperforms a simulator trained policy. © 2013 IEEE.

The Role of Chemical Mechanisms in Neural Computation and Learning

Most computational models of neurons assume that their electrical characteristics are of paramount importance. However, all long-term changes in synaptic efficacy, as well as many short-term effects, are mediated by chemical mechanisms. This technical report explores the interaction between electrical and chemical mechanisms in neural learning and development. Two neural systems that exemplify this interaction are described and modelled. The first is the mechanisms underlying habituation, sensitization, and associative learning in the gill withdrawal reflex circuit in Aplysia, a marine snail. The second is the formation of retinotopic projections in the early visual pathway during embryonic development.

Adapting Home Service Robot Behaviours by Experience Reuse and Interaction with Humans

Meng, Q., Lee, M. (2003). Adapting Home Service Robot Behaviours by Experience Reuse and Interaction with Humans. 673-678. Paper presented at IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), Port Island, Kobe, Japan

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

Memorability as a measure of processing: a unit analysis of prose and list learning.

The percentage of subjects recalling each unit in a list or prose passage is considered as a dependent measure. When the same units are recalled in different tasks, processing is assumed to be the same; when different units are recalled, processing is assumed to be different. Two collections of memory tasks are presented, one for lists and one for prose. The relations found in these two collections are supported by an extensive reanalysis of the existing prose memory literature. The same set of words were learned by 13 different groups of subjects under 13 different conditions. Included were intentional free-recall tasks, incidental free recall following lexical decision, and incidental free recall following ratings of orthographic distinctiveness and emotionality. Although the nine free-recall tasks varied widely with regard to the amount of recall, the relative probability of recall for the words was very similar among the tasks. Imagery encoding and recognition produced relative probabilities of recall that were different from each other and from the free-recall tasks. Similar results were obtained with a prose passage. A story was learned by 13 different groups of subjects under 13 different conditions. Eight free-recall tasks, which varied with respect to incidental or intentional learning, retention interval, and the age of the subjects, produced similar relative probabilities of recall, whereas recognition and prompted recall produced relative probabilities of recall that were different from each other and from the free-recall tasks. A review of the prose literature was undertaken to test the generality of these results. Analysis of variance is the most common statistical procedure in this literature. If the relative probability of recall of units varied across conditions, a units by condition interaction would be expected. For the 12 studies that manipulated retention interval, an average of 21% of the variance was accounted for by the main effect of retention interval, 17% by the main effect of units, and only 2% by the retention interval by units interaction. Similarly, for the 12 studies that varied the age of the subjects, 6% of the variance was accounted for by the main effect of age, 32% by the main effect of units, and only 1% by the interaction of age by units.(ABSTRACT TRUNCATED AT 400 WORDS)

Interaction design for teaching visually impaired students

This paper reports on the findings for a study on improving interaction design for teaching visually impaired students. The crux of the problem is the ability to draw and understand diagrams. The cognitive issues are often underestimated with insufficient attention being given to the use of metaphors, etc. and "one size fits all solutions" are often the norm. The findings of the original seed funded project have led to design criteria and to an application for a large scale project, to produce generic tools and to enable multi-modal teaching and learning, with connotations for the mentally as well as physically impaired.

Web-based course support systems: an essential tool for supporting flexible learning environment

In Higher Education web-based course support systems are essential for supporting flexible learning environments. They provide tools to enable the interaction between student and tutor to reinforce transfer of theory to understanding particularly in an academic environment, therefore this paper will examine issues associated with the use of curriculum and learning resources within Web-based course support systems and the effectiveness of the resulting flexible learning environments This paper is a general discussion about flexible learning and in this case how it was applied to one of the courses at undergraduate level one. The first section will introduce what is flexible learning and the importance of flexible learning in Higher Education followed by the description of the course and why the flexible learning concepts is important in such a course and finally, how the flexibility was useful for this particular instance.

Student Experience of E-Learning (SEEL)

The Student Experience of E-Learning project (SEEL) was an institutional response to the university’s HEA/JISC Benchmarking exercise (Ryan and Kandler, 2007). The study had a social constructivist approach which recognised the importance of listening to the student voice (JISC 2007) within the University of Greenwich context, to interpret the student experience of e-learning. Nearly 1000 students responded to an online survey on their approaches to, and their use of, learning technology. The quantitative and qualitative questions used included identifying study patterns, using specific online tools, within the context of learning and beyond, and student’s attitudes towards using e-learning in their studies. Initially, individual responses to questions were analysed in depth, giving a general indication of the student experience. Further depth was applied through a filtering mechanism, beginning with a cross-slicing of individual student responses to produce cameos. Audio logs and individual interviews were drawn from these cameos. Analysis of the cameos is in progress but has already revealed some unexpected results. There was a mismatch between students’ expectations of the university’s use of technology and their experiences and awareness of its possible use in other contexts. Students recognised the importance of social interaction as a vehicle for learning (Vygotsky 1978, Bruner 2006) but expressed polarised views on the use of social networking sites such as Facebook for e-learning. Their experiences in commercial contexts led them to see the university VLE as unimaginative and the tutors’ use of it as lacking in vision. Whereas analysis of the individual questions provided a limited picture, the cameos gave a truer reflection of the students lived experiences and identified a gulf between the university’s provision and the students’ expectation of e-learning and their customary use of technology. However it is recognised that the very nature of an online survey necessarily excludes students who chose not to engage, either through lack of skills or through disillusionment and this would constitute a separate area for study.

Making sense of game based user data: learning analytics in applied games

Digital learning games are useful educational tools with high motivational potential. With the application of games for instruction there comes the need of acknowledging learning game experiences also in the context of educational assessment. Learning analytics provides new opportunities for supporting assessment in and of educational games. We give an overview of current learning analytics methods in this field and reflect on existing challenges. An approach of providing reusable software assets for interaction assessment and evaluation in games is presented. This is part of a broader initiative of making available advanced methodologies and tools for supporting applied game development.

Towards Real-time Speech Emotion Recognition for Affective E-Learning

The original article is available as an open access file on the Springer website in the following link: http://link.springer.com/article/10.1007/s10639-015-9388-2