The learning styles neuromyth:when the same term means different things to different teachers

Alexia Barrable - ORCID: 0000-0002-5352-8330 https://orcid.org/0000-0002-5352-8330Although learning styles (LS) have been recognised as a neuromyth, they remain a virtual truism within education. A point of concern is that the term LS has been used within theories that describe them using completely different notions and categorisations. This is the first empirical study to investigate education professionals’ conceptualisation, as well as means of identifying and implementing LS in their classroom. A sample of 123 education professionals were administered a questionnaire consisting both closed- and open-ended questions. Responses were analysed using thematic analysis. LS were found to be mainly conceptualised within the Visual-Auditory-(Reading)-Kinaesthetic (VAK/VARK) framework, as well as Gardner’s multiple intelligences. Moreover, a lot of education professionals confused theories of learning (e.g., behavioural or cognitive theories) with LS. In terms of identifying LS, educators reported using a variety of methods, spanning from observation and everyday contact to the use of tests. The ways LS were implemented in the classroom were numerous, comprising various teaching aids, participatory techniques and motor activities. Overall, we argue that the extended use of the term LS gives the illusion of a consensus amongst educators, when a closer examination reveals that the term LS is conceptualised, identified and implemented idiosyncratically by different individuals. This study aims to be of use to pre-service and in-service teacher educators in their effort to debunk the neuromyth of LS and replace it with evidence-based practices.https://doi.org/10.1007/s10212-020-00485-236pubpub

Knock on Wood: The Effects of Material Choice on the Perception of Social Robots

Many people who interact with robots in the near future will not have prior experience, and they are likely to intuitively form their first impressions of the robot based on its appearance. This paper explores the effects of component material on people’s perception of the robots in terms of social attributes and willingness to interact. Participants watched videos of three robots with different outer materials: wood, synthetic fur, and plastic. The results showed that people rated the perceived warmth of a plastic robot lower than a wooden or furry robot. Ratings of perceived competence and discomfort did not differ between the three robots.QC 20191122</p

Trade Selection with Supervised Learning and Optimal Coordinate Ascent (OCA)

International audienceCan we dynamically extract some information and strong relationship between some financial features in order to select some financial trades over time? Despite the advent of representation learning and end-to-end approaches, mainly through deep learning, feature selection remains a key point in many machine learning scenarios. This paper introduces a new theoretically motivated method for feature selection. The approach that fits within the family of embedded methods, casts the feature selection conundrum as a coordinate ascent optimization with variables dependencies materialized by block variables. Thanks to a limited number of iterations, it proves efficiency for gradient boosting methods, implemented with XGBoost. In case of convex and smooth functions, we are able to prove that the convergence rate is polynomial in terms of the dimension of the full features set. We provide comparisons with state of the art methods, Recursive Feature Elimination and Binary Coordinate Ascent and show that this method is competitive when selecting some financial trades

Interactions between climate change, competition, dispersal, and disturbances in a tree migration model

Potentially significant shifts in the geographical patterns of vegetation are an expected result of climate change. However, the importance of local processes (e.g., dispersal, competition, or disturbance) has been often ignored in climate change modeling. We develop an individual-based simulation approach to assess how these mechanisms affect migration rate. We simulate the northward progression of a theoretical tree species when climate change makes northern habitat suitable. We test how the rate of progression is affected by (1) competition with a resident species, (2) interactions with disturbance regimes, (3) species dispersal kernel, and (4) the intensity of climate change over time. Results reveal a strong response of species’ expansion rate to the presence of a local competitor, as well as nonlinear effects of disturbance. We discuss these results in light of current knowledge of northern forest dynamics and results found in the climatic research literature. © Springer