How can the blow of math difficulty on elementary school children’s motivational, cognitive, and affective experiences be dampened? : The critical role of autonomy-supportive instructions

Although teachers are recommended to create a stimulating learning environment in which children can use, perfect, and extend their skills, this is far from easy. In many cases, identifying the optimal difficulty level of learning tasks involves a trial-and-error process during which teachers offer children too difficult tasks, with negative outcomes as a result. This experimental study investigated if autonomy-supportive instructions could dampen or even cancel out these presumed negative outcomes associated with math difficulty in elementary schoolchildren (N = 479; M-age = 9.41). After varying an autonomy-supportive versus a controlling instructional style through a comic book, children solved a series of either easy-medium or difficult math exercises, followed by the completion of questionnaires and the opportunity to choose the difficulty level of a final set of exercises to work on independently. Children who solved difficult, relative to easier, exercises reported less interest, more irritation, and more cognitive disengagement, while also seeking less challenge when asked to work independently. Need-based experiences of competence and autonomy accounted for these effects. Yet, the impairing impact of task difficulty could, at least partially, be dampened through the use of an autonomy-supportive relative to a controlling instructional style, which led to enhanced autonomy satisfaction. These findings largely occurred independent of children's motives for mathematics. The results have high practical value, especially for poor performers and children with mathematical learning disabilities, who find math to be harder overall. Limitations and implications for practice are discussed. Educational Impact and Implications Statement : Autonomy-supportive instructions (e.g., inviting language, meaningful rationale) were found to dampen the impairing effects of too difficult math tasks on children's motivational, cognitive, and affective experiences. This is especially important for poor performers and children with mathematical learning disabilities, who find math to be harder overall. An autonomy-supportive environment and avoiding too hard learning material may stimulate children to accept new challenges, thereby possibly improving chances for later academic/job success

Supporting Focus and Context Awareness in 3D Modeling Using Multi-Layered Displays

Although advances in computer technology over the past few decades have made it possible to create and render highly realistic 3D models these days, the process of creating these models has remained largely unchanged over the years. Modern 3D modeling software provide a range of tools to assist users with creating 3D models, but the process of creating models in virtual 3D space is nevertheless still challenging and cumbersome. This thesis, therefore, aims to investigate whether it is possible to support modelers more effectively by providing them with alternative combinations of hardware and software tools to improve their 3D modeling tasks. The first step towards achieving this goal has been to better understand the type of problems modelers face in using conventional 3D modeling software. To achieve this, a pilot study of novice 3D modelers, and a more comprehensive study of professional modelers were conducted. These studies resulted in identifying a range of focus and context awareness problems that modelers face in creating complex 3D models using conventional modeling software. These problems can be divided into four categories: maintaining position awareness, identifying and selecting objects or components of interest, recognizing the distance between objects or components, and realizing the relative position of objects or components. Based on the above categorization, five focus and context awareness techniques were developed for a multi-layer computer display to enable modelers to better maintain their focus and context awareness while performing 3D modeling tasks. These techniques are: object isolation, component segregation, peeling focus, slicing, and peeling focus and context. A user study was then conducted to compare the effectiveness of these focus and context awareness techniques with other tools provided by conventional 3D modeling software. The results of this study were used to further improve, and evaluate through a second study, the five focus and context awareness techniques. The two studies have demonstrated that some of these techniques are more effective in supporting 3D modeling tasks than other existing software tools

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is driven by reliable and safe production and supply, that need to account for flexibility to respond to the uncertainty in energy demand and also supply due to the stochasticity of Renewable Energy Sources (RESs); at the same time, accidents of severe consequences must be avoided for safety reasons. In this paper, we consider O&M strategies for CPES reliable and safe production and supply, and develop a Deep Reinforcement Learning (DRL) approach to search for the best strategy, considering the system components health conditions, their Remaining Useful Life (RUL), and possible accident scenarios. The approach integrates Proximal Policy Optimization (PPO) and Imitation Learning (IL) for training RL agent, with a CPES model that embeds the components RUL estimator and their failure process model. The novelty of the work lies in i) taking production plan into O&M decisions to implement maintenance and operate flexibly; ii) embedding the reliability model into CPES model to recognize safety related components and set proper maintenance RUL thresholds. An application, the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), is provided. The optimal solution found by DRL is shown to outperform those provided by state-of-the-art O&M policies

Modelling approaches for predictive control of large-scale sewage systems

In this report, model predictive control (MPC) of large-scale sewage systems is addressed considering different modelling approaches that include several inherent continuous/discrete phenomena (overflows in sewers and tanks) and elements (weirs) in the system that result in distinct behaviour depending on the dynamic state ( flow/volume) of the network. These behaviours can not be neglected nor can be modelled by a pure linear representation. In order the MPC controller takes into account these phenomena and elements, a modelling approach based on piece-wise linear functions is proposed and compared against a hybrid modelling approach previously reported by the authors. Control performance results and associated computation times of the closed-loop scheme considering both modelling approaches are compared by using a real case study based on the Barcelona sewer network.Preprin

ImageSpirit: Verbal Guided Image Parsing

Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixel. In this paper we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interests enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g. smart phones, Google Glass, living room devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the tradeoffs compared to traditional mouse based interactions, results are reported for both a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit

Human-in-the-Loop Model Predictive Control of an Irrigation Canal

Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].Junta de Andalucía P11-TEP-812

Students with Mathematics Learning Disabilities and Their Ways of Thinking in Fraction Learning

This chapter presents the result of research on ways of thinking of students with mathematics learning disabilities in fraction learning. We conducted a class of fraction learning with Lesh translation model. From the class discussion, interview, and students’ work, we then explore the students’ ways of thinking when they learn fraction. In the class, students with mathematics learning disabilities perform two mental acts with corresponding ways of thinking and ways of understanding; those are interpreting and problem-solving. We find some interesting findings and they are: (1) students know the common denominator method in the addition of fractions; however, they incorrectly apply the method; (2) students use the common denominator approach (for fraction addition) in the multiplication of fraction; and (3) in the division of fraction, students mistakenly apply the invert multiply algorithm