Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Towards Responsive Schools Supporting Better Schooling for Disadvantaged Children

Teaching/Communication/Extension/Profession,

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.

Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings

Gaming Your Mathematics Course: The Theory and Practice of Games for Learning

Learning through play is fundamental to humans and to many other animals. Game Based Learning is an interactive pedagogy that has as its foundation the tenet that games, by their very nature, increase learning through positive emotional experience. This article introduces readers to what games in mathematics classes have the potential to do, including to decrease anxiety, increase motivation, and deepen learning through immersive gaming. The article then connects this theory to practice, providing examples of both computer and non-computer games in introductory middle school, high school and college mathematics. The article analyses how these games work, and makes the distinction between intrinsic games, in which the concept being taught is an integral part of the game, and extrinsic games which can be used for a variety of classes and topics, and tend to be more about review than about learning new concepts

What works best: evidence based practices to help improve NSW student performance

\u27What works best\u27 brings together seven themes from the growing bank of evidence we have for what works to improve student educational outcomes. The seven themes addressed here are:  1. High expectations 2. Explicit teaching 3. Effective feedback 4. Use of data to inform practice 5. Classroom management 6. Wellbeing 7. Collaboration These themes offer helpful ways of thinking about aspects of teaching practice but they are not discrete. Rather, they overlap and connect with one another in complex ways. For example, providing timely and effective feedback to students is another element of explicit teaching – two of the more effective types of feedback direct students’ attention to the task at hand and to the way in which they are processing that task. Similarly, being explicit about the learning goals of a lesson and the criteria for success gives high expectations a concrete form, which students can understand and aim for. Wellbeing and quality teaching are mutually reinforcing – if students with high levels of general wellbeing are more likely to be engaged productively with learning, it is also true that improving intellectual engagement can improve wellbeing.  The seven themes are not confined to what happens in classrooms. While they offer sound strategies for individual teachers to consider as part of their repertoires, evidence suggests that their effectiveness is stronger when they are implemented as whole-school approaches. For example, the literature indicates that teachers are more likely to make effective use of student data when working together than when working alone. Ideally, everyone associated with a school – including school leaders, parents, students and community members – will share a commitment not only to the school’s vision for development but to the mechanisms for achieving these goals, and will engage collaboratively in responding to the challenge

Integrating Socially Assistive Robots into Language Tutoring Systems. A Computational Model for Scaffolding Young Children's Foreign Language Learning

Schodde T. Integrating Socially Assistive Robots into Language Tutoring Systems. A Computational Model for Scaffolding Young Children's Foreign Language Learning. Bielefeld: Universität Bielefeld; 2019.Language education is a global and important issue nowadays, especially for young children since their later educational success build on it. But learning a language is a complex task that is known to work best in a social interaction and, thus, personalized sessions tailored to the individual knowledge and needs of each child are needed to allow for teachers to optimally support them. However, this is often costly regarding time and personnel resources, which is one reasons why research of the past decades investigated the benefits of Intelligent Tutoring Systems (ITSs). But although ITSs can help out to provide individualized one-on-one tutoring interactions, they often lack of social support. This dissertation provides new insights on how a Socially Assistive Robot (SAR) can be employed as a part of an ITS, building a so-called "Socially Assistive Robot Tutoring System" (SARTS), to provide social support as well as to personalize and scaffold foreign language learning for young children in the age of 4-6 years. As basis for the SARTS a novel approach called A-BKT is presented, which allows to autonomously adapt the tutoring interaction to the children's individual knowledge and needs. The corresponding evaluation studies show that the A-BKT model can significantly increase student's learning gains and maintain a higher engagement during the tutoring interaction. This is partly due to the models ability to simulate the influences of potential actions on all dimensions of the learning interaction, i.e., the children's learning progress (cognitive learning), affective state, engagement (affective learning) and believed knowledge acquisition (perceived learning). This is particularly important since all dimensions are strongly interconnected and influence each other, for example, a low engagement can cause bad learning results although the learner is already quite proficient. However, this also yields the necessity to not only focus on the learner's cognitive learning but to equally support all dimensions with appropriate scaffolding actions. Therefore an extensive literature review, observational video recordings and expert interviews were conducted to find appropriate actions applicable for a SARTS to support each learning dimension. The subsequent evaluation study confirms that the developed scaffolding techniques are able to support young children’s learning process either by re-engaging them or by providing transparency to support their perception of the learning process and to reduce uncertainty. Finally, based on educated guesses derived from the previous studies, all identified strategies are integrated into the A-BKT model. The resulting model called ProTM is evaluated by simulating different learner types, which highlight its ability to autonomously adapt the tutoring interactions based on the learner's answers and provided dis-engagement cues. Summarized, this dissertation yields new insights into the field of SARTS to provide personalized foreign language learning interactions for young children, while also rising new important questions to be studied in the future