Learning number sense through digital games with intrinsic feedback

The paper proposes a new interdisciplinary approach to helping low attaining learners in basic mathematics. It reports on the research-informed design and user testing of an adaptive digital game based on constructionist tasks with intrinsic feedback. The approach uses findings from the neuroscience of dyscalculia, cognitive science research on conceptual understanding in mathematics, and mathematical education research to inform the detailed pedagogic design. It is interdisciplinary in the sense that it synthesises the results from multiple disciplines in the design principles. It then exploits the new capabilities of digital technologies to develop the design for testing with learners, and capturing appropriate data. The initial pilot has shown that the game supports learners age 5-7 years for independent learning of the kind that low attaining learners will need in order to keep pace with mainstream learners. The experimental work will evaluate this and similar games for learners of all ages who have low numeracy. In general, the approach is to (i) focus on a problem at the intersection of robust evidence in both education and neursocience; and (ii) use this data to design and test a digital intervention that fully exploits the adaptive and interactive features of learning technology

Informing the design of a multisensory learning environment for elementary mathematics learning

It is well known that primary school children may face difficulties in acquiring mathematical competence, possibly because teaching is generally based on formal lessons with little opportunity to exploit more multisensory-based activities within the classroom. To overcome such difficulties, we report here the exemplary design of a novel multisensory learning environment for teaching mathematical concepts based on meaningful inputs from elementary school teachers. First, we developed and administered a questionnaire to 101 teachers asking them to rate based on their experience the learning difficulty for specific arithmetical and geometrical concepts encountered by elementary school children. Additionally, the questionnaire investigated the feasibility to use multisensory information to teach mathematical concepts. Results show that challenging concepts differ depending on children school level, thus providing a guidance to improve teaching strategies and the design of new and emerging learning technologies accordingly. Second, we obtained specific and practical design inputs with workshops involving elementary school teachers and children. Altogether, these findings are used to inform the design of emerging multimodal technological applications, that take advantage not only of vision but also of other sensory modalities. In the present work, we describe in detail one exemplary multisensory environment design based on the questionnaire results and design ideas from the workshops: the Space Shapes game, which exploits visual and haptic/proprioceptive sensory information to support mental rotation, 2D–3D transformation and percentages. Corroborating research evidence in neuroscience and pedagogy, our work presents a functional approach to develop novel multimodal user interfaces to improve education in the classroom

The Effects of a Platform Digital Game-Based Learning Environment on Undergraduate Students Achievement and Motivation in a Multivariable Calculus Course

This study examined the effects of a researcher-designed digital game-based learning (DGBL) environment called Adventures of Krystal Kingdom on undergraduate students’ mathematics achievement and motivation in a Multivariable Calculus course. Multivariable Calculus is a specific area of computational and applied mathematics that focuses on the differentiation and integration of functions of several variables in fields like physics and engineering. The study employed a single exploratory embedded case study design with quantitative and qualitative techniques. A case study is the appropriate methodology for this study, which is a bounded system that facilitates a deeply contextualized understanding of a case through giving descriptions, analyses, and interpretations (Yin, 2014). The quantitative sample comprised 29 undergraduate students, and the qualitative sample included 6 students selected through stratified sampling based on the level of achievement. Quantitative data was collected using two surveys: demographic and motivation surveys, and two tests: academic achievement test and a game performance test. Analysis of quantitative data used a paired sample t-test. Qualitative data were collected from interviews, observations, and artifacts. Analysis of qualitative data used coding procedures suggested by Creswell (2014) where patterns were identified and grouped to allow the emergence of themes. The results of the study indicated no statistical significance in achievement (p=0.88 \u3e0.05), however, there was overall improvement found in achievement scores of the students who played the game. Three themes emerged from the study: 1) Undergraduate students saw the use of the Adventures of Krystal Kingdom as learning tool to enhance their understanding of concepts in Multivariable Calculus.; 2) Undergraduate students saw the use of the Adventures of Krystal Kingdom as a way to engage themselves in mathematical fun in a digital environment; and 3) Undergraduate students saw input semiotics, automated reflexes, Task Relevant Support and other core mechanics as components that affect students’ gameplay. Results of the interviews, observations, and artifacts revealed that students benefited from using DGBL as an alternative approach to learning mathematics and to use such advanced techniques in biology, engineering, and computational neuroscience. The overall results indicate that DGBL used in the study was an appropriate teaching and learning tool to improve students\u27 mathematics skills

Integration of psychological models in the design of artificial creatures

Artificial creatures form an increasingly important component of interactive computer games. Examples of such creatures exist which can interact with each other and the game player and learn from their experiences. However, we argue, the design of the underlying architecture and algorithms has to a large extent overlooked knowledge from psychology and cognitive sciences. We explore the integration of observations from studies of motivational systems and emotional behaviour into the design of artificial creatures. An initial implementation of our ideas using the “sim agent” toolkit illustrates that physiological models can be used as the basis for creatures with animal like behaviour attributes. The current aim of this research is to increase the “realism” of artificial creatures in interactive game-play, but it may have wider implications for the development of AI

"Sticky Hands": learning and generalization for cooperative physical interactions with a humanoid robot

"Sticky Hands" is a physical game for two people involving gentle contact with the hands. The aim is to develop relaxed and elegant motion together, achieve physical sensitivity-improving reactions, and experience an interaction at an intimate yet comfortable level for spiritual development and physical relaxation. We developed a control system for a humanoid robot allowing it to play Sticky Hands with a human partner. We present a real implementation including a physical system, robot control, and a motion learning algorithm based on a generalizable intelligent system capable itself of generalizing observed trajectories' translation, orientation, scale and velocity to new data, operating with scalable speed and storage efficiency bounds, and coping with contact trajectories that evolve over time. Our robot control is capable of physical cooperation in a force domain, using minimal sensor input. We analyze robot-human interaction and relate characteristics of our motion learning algorithm with recorded motion profiles. We discuss our results in the context of realistic motion generation and present a theoretical discussion of stylistic and affective motion generation based on, and motivating cross-disciplinary research in computer graphics, human motion production and motion perception

Reward-based contextual learning supported by anterior cingulate cortex

The anterior cingulate cortex (ACC) is commonly associated with cognitive control and decision making, but its specific function is highly debated. To explore a recent theory that the ACC learns the reward values of task contexts (Holroyd & McClure in Psychological Review, 122, 54-83, 2015; Holroyd & Yeung in Trends in Cognitive Sciences, 16, 122-128, 2012), we recorded the event-related brain potentials (ERPs) from participants as they played a novel gambling task. The participants were first required to select from among three games in one "virtual casino," and subsequently they were required to select from among three different games in a different virtual casino; unbeknownst to them, the payoffs for the games were higher in one casino than in the other. Analysis of the reward positivity, an ERP component believed to reflect reward-related signals carried to the ACC by the midbrain dopamine system, revealed that the ACC is sensitive to differences in the reward values associated with both the casinos and the games inside the casinos, indicating that participants learned the values of the contexts in which rewards were delivered. These results highlight the importance of the ACC in learning the reward values of task contexts in order to guide action selection

Independent circuits in basal ganglia and cortex for the processing of reward and precision feedback

In order to understand human decision making it is necessary to understand how the brain uses feedback to guide goal-directed behavior. The ventral striatum (VS) appears to be a key structure in this function, responding strongly to explicit reward feedback. However, recent results have also shown striatal activity following correct task performance even in the absence of feedback. This raises the possibility that, in addition to processing external feedback, the dopamine-centered reward circuit might regulate endogenous reinforcement signals, like those triggered by satisfaction in accurate task performance. Here we use functional magnetic resonance imaging (fMRI) to test this idea. Participants completed a simple task that garnered both reward feedback and feedback about the precision of performance. Importantly, the design was such that we could manipulate information about the precision of performance within different levels of reward magnitude. Using parametric modulation and functional connectivity analysis we identified brain regions sensitive to each of these signals. Our results show a double dissociation: frontal and posterior cingulate regions responded to explicit reward but were insensitive to task precision, whereas the dorsal striatum - and putamen in particular - was insensitive to reward but responded strongly to precision feedback in reward-present trials. Both types of feedback activated the VS, and sensitivity in this structure to precision feedback was predicted by personality traits related to approach behavior and reward responsiveness. Our findings shed new light on the role of specific brain regions in integrating different sources of feedback to guide goal-directed behavior

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about journal publication. Frontiers in Neuromorphic Engineering (2019