Using ‘tangibles’ to promote novel forms of playful learning

Tangibles, in the form of physical artefacts that are electronically augmented and enhanced to trigger various digital events to happen, have the potential for providing innovative ways for children to play and learn, through novel forms of interacting and discovering. They offer, in addition, the scope for bringing playfulness back into learning. To this end, we designed an adventure game, where pairs of children have to discover as much as they can about a virtual imaginary creature called the Snark, through collaboratively interacting with a suite of tangibles. Underlying the design of the tangibles is a variety of transforms, which the children have to understand and reflect upon in order to make the Snark come alive and show itself in a variety of morphological and synaesthesic forms. The paper also reports on the findings of a study of the Snark game and discusses what it means to be engrossed in playful learning.     

Rethinking scaffolding in the information age

This paper addresses the use of scaffolding in learning contexts that incorporate technologically based novel problems. We suggest that in computer contexts extended conceptualisations of scaffolding are needed in order to gain greater insights into teaching and learning processes. Our work has revealed that traditional forms of scaffolding, based on the “expert’s” view of how the problem should be solved, need to be modified in order to accommodate the child’s perspective and that three different types of scaffolding which we refer to as cognitive, technical and affective can be conceptualized. This paper discusses the ways in which the performance of pairs of children is enhanced in such scaffolding contexts, to include more examples of metastrategic processes and strategies for problem-solving, than when the pairs are left to spontaneously solve the problems. This study provides additional support that cognitive, affective and technical scaffolding are beneficial for learning and that children are able to support each others learning via sharing strategies and articulating the reasons behind them to each other.     

Leonardo and Theoretical Mathematics

Leonardo’s mathematical notes bear witness to a work in progress and allow us to look directly into the mind of the writer. In Leonardo we find two of the three fundamental classical geometric problems: the duplication of the cube and the quadrature of the circle. While Leonardo is extremely familiar with two-dimensional geometry problems, and proposes playful graphic exercises of adding and subtracting polygonal surfaces of all kinds, he is still unable to solve the problem of the duplication of the cube. Numerous pages testify of the attempt to rise above planar geometry and reach the realm of the third dimension, but Leonardo always bumps against the limits of quantity calculation possibilities of his age.     

Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems.

Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.     

一种移动平台儿童教育游戏的最终用户编程语言

针对现有移动教育游戏无法满足快速增长的移动教育需求这个问题,利用领域工程的方法和最终用户编程(EUP)的技术,研究和设计了一种最终用户编程语言Kids。首先通过对移动平台儿童教育领域进行分析识别出儿童教育游戏的游戏元素,在此基础上对该领域进行特征的提取,得到该领域的特征模型。Kids语言以特征模型为基础进行设计,能够允许老师和家长为学龄前的儿童编写移动教育游戏。同时,开发了一个Kids的编程工具,包括可视化编辑器和代码生成引擎。该工具能够支持用户更加有效地使用Kids进行可视化编程,并自动生成Android代码。实验结果表明,Kids相比其他通用语言更加简洁,大部分最终用户能够很好地利用Kids开发工具开发出简单的儿童教育游戏。     

Preschool children’s learning with technology at home

We produced case studies of fourteen families based on nine rounds of data collection during the period from June 2008 to October 2009. We focused on fourteen children who were three years old when our visits started and used an ecocultural approach to examine their experiences of learning and playing with technologies at home. The study describes i) which technologies children encounter at home, ii) how family practices influence children’s encounters with technology, and iii) what children are learning through their interactions with technology. We present a framework of four areas of learning that could be supported by technology: acquiring operational skills, extending knowledge and understanding of the world, developing dispositions to learn, and understanding the role of technology in everyday life.     

Supporting visually impaired children with software agents in a multimodal learning environment

Visually impaired children have a great disadvantage in the modern society since their ability to use modern computer technology is limited due to inappropriate user interfaces. The aim of the work presented in this paper was to develop a multimodal software architecture and applications to support learning of visually impaired children. The software architecture is based on software agents, and has specific support for visual, auditory and haptic interaction. It has been used successfully with different groups of 7-8 year-old and 12 year-old visually impaired children. In this paper we discuss the enabling software technology and interaction techniques aimed to realize our goal and our experiences in the actual use of the system.     

Playful learning with sound-augmented toys: comparing children with and without visual impairment

Sound-augmented toys producing factual knowledge were thought to encourage incidental, playful learning in children with visual impairments (VIs). A group of 15 children with VIs and 22 sighted controls played with a sound-augmented savannah landscape and listened to an informative story in a counterbalanced order. Children's knowledge about savannah animals was assessed at baseline and after each condition in order to quantitatively compare knowledge gains between conditions. Results indicated that children with VIs gained more knowledge than sighted controls from playing with the sound-augmented toy. Furthermore, offering both the augmented toy and the informative story led to higher knowledge gains than a single medium, especially in children with VIs. Sound-augmented toys could therefore be a useful addition to the current curriculum in special education for children with VIs.     

一种基于强化学习的控制算法研究

该文在阐述了强化学习的基本机制的基础上，根据复杂工业过程的非线性、多变量、大时延、强耦合的特点，提出了一种将基于案例的学习和强化学习相结合的控制算法，并对重油分馏塔进行了控制效果的仿真实验，控制结果显示了算法能够很好地满足控制任务。     

A Dynamic-Bayesian Network framework for modeling and evaluating learning from observation

Learning from observation (LfO), also known as learning from demonstration, studies how computers can learn to perform complex tasks by observing and thereafter imitating the performance of a human actor. Although there has been a significant amount of research in this area, there is no agreement on a unified terminology or evaluation procedure. In this paper, we present a theoretical framework based on Dynamic-Bayesian Networks (DBNs) for the quantitative modeling and evaluation of LfO tasks. Additionally, we provide evidence showing that: (1) the information captured through the observation of agent behaviors occurs as the realization of a stochastic process (and often not just as a sample of a state-to-action map); (2) learning can be simplified by introducing dynamic Bayesian models with hidden states for which the learning and model evaluation tasks can be reduced to minimization and estimation of some stochastic similarity measures such as crossed entropy.     

Single-machine scheduling jobs with exponential learning functions

In a manufacturing system workers are involved in doing the same job or activity repeatedly. Hence, the workers start learning more about the job or activity. Because of the learning, the time to complete the job or activity starts decreasing, which is known as “learning effect”. In this paper, an exponential sum-of-actual-processing-time based learning effect is introduced into single-machine scheduling. By the exponential sum-of-actual-processing-time based learning effect, we mean that the processing time of a job is defined by an exponential function of the sum-of-the-actual-processing-time of the already processed jobs. Under the proposed learning model, we show that under a sufficient condition, the makespan minimization problem, the sum of the θth (θ > 0) power of completion times minimization problem, and some special cases of the total weighted completion time minimization problem and the maximum lateness minimization problem remain polynomially solvable.     

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners’ habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.     

Solving a two-agent single-machine scheduling problem considering learning effect

Scheduling with multiple agents and learning effect has drawn much attention. In this paper, we investigate the job scheduling problem of two agents competing for the usage of a common single machine with learning effect. The objective is to minimize the total weighted completion time of both agents with the restriction that the makespan of either agent cannot exceed an upper bound. In order to solve this problem we develop several dominance properties and a lower bound based on a branch-and-bound to find the optimal algorithm, and derive genetic algorithm based procedures for finding near-optimal solutions. The performances of the proposed algorithms are evaluated and compared via computational experiments.     

Hypermedia in the ambient wood

The Ambient Wood project, carried out as part of the Equator project, set out to provide an augmented learning experience for children in an outdoor environment. Using a variety of devices, the children gathered information about the woodland habitats performing basic scientific enquiry and hypothesis testing. In this paper, we describe the supporting information infrastructure used in the project, focusing on how hypermedia tools and techniques were used to structure and deliver the information to the children, helping to orchestrate the learning activities.     

A New Enhanced Learning Approach to Automatic Image Classification Based on Salp Swarm Algorithm

In this paper we propose a new image classification technique. According to this note that most research focuses on extraction of features in the frequency domain, location, and reduction of feature dimensions, in this research we focused on learning step in image classification. The main aim is to use the heuristic methods to increase the function of the estimator of the learning algorithm and continue to achieve the desired state, as well as categorization without user interference and automatically performed by the model produced from the above steps. So, in this paper, a new learning approach based on the Salp Swarm Algorithm was proposed that was implemented and evaluated on learning algorithm Decision Tree, K-Nearest Neighbors and Naïve Bayes. The results demonstrate the improvement of the performance of learning algorithms in all the achieved criteria by using the SSA algorithm in comparison with traditional learning algorithms. In the accuracy, sensitivity, classification error and F1 criterion, the best performance of the proposed model is using the Decision Tree learning method with values of 99.17%, 100%, 0.83% and 95.65% respectively. In the specificity and precision criterion, the best performance of the proposed model is based on K-Nearest Neighbors learning method with values of 100%.     

Evaluation of a hypermedia document as a learning tool

This paper describes a classroom‐based experimental evaluation of an electronic history 'book' presented in a hypermedia environment. It was compared with a paper‐based version of the same material. Observations of children's activities and interactions revealed that they had few problems using the hypermedia document as an information source and also rated it more positively, in terms of ease of use, than those children using the paper‐based version. However, no significant differences were found between the two groups in the number of questions children completed and answered correctly, their interest in the material, and task difficulty. The implications of these findings and the role of evaluation in determining the educational value of computer‐based activities are discussed.     

Learning classifier systems: a survey

Learning classifier systems (LCSs) are rule- based systems that automatically build their ruleset. At the origin of Holland’s work, LCSs were seen as a model of the emergence of cognitive abilities thanks to adaptive mechanisms, particularly evolutionary processes. After a renewal of the field more focused on learning, LCSs are now considered as sequential decision problem-solving systems endowed with a generalization property. Indeed, from a Reinforcement Learning point of view, LCSs can be seen as learning systems building a compact representation of their problem thanks to generalization. More recently, LCSs have proved efficient at solving automatic classification tasks. The aim of the present contribution is to describe the state-of- the-art of LCSs, emphasizing recent developments, and focusing more on the sequential decision domain than on automatic classification.     

Motivational aspects of different learning contexts: “My mom won’t let me play this game…”

Learning motivation should describe mechanisms that direct toward the learning goals, initiate learning activity and hold one‘s learning ability on a level that enables person to attain new information and more complex knowledge. Educational games are emerging as a new form of learning that should improve student’s desire for knowledge. Idea that educational games can be used as an effective teaching tool is what inspires our work in this field. In our study we attempted to explore the connection between motivational effects and application of educational game design as a learning tool. Additionally, we took into consideration effects of this approach on improving final exam results. Our results indicate that designing computer programs can raise enough curiosity and can be seen as a relevant learning tool for students.     

Single-machine and flowshop scheduling with a general learning effect model

Learning effects in scheduling problems have received growing attention recently. Biskup [Biskup, D. (2008). A state-of-the-art review on scheduling with learning effect. European Journal of Operational Research, 188, 315–329] classified the learning effect scheduling models into two diverse approaches. The position-based learning model seems to be a realistic assumption for the case that the actual processing of the job is mainly machine driven, while the sum-of-processing-time-based learning model takes into account the experience the workers gain from producing the jobs. In this paper, we propose a learning model which considers both the machine and human learning effects simultaneously. We first show that the position-based learning and the sum-of-processing-time-based learning models in the literature are special cases of the proposed model. Moreover, we present the solution procedures for some single-machine and some flowshop problems.     

The learning curve: a new perspective

Despite the popularity of the "learning" curve, it should be called the "forecasting" curve. Given an industrial process, the traditional learning curve forecasts how costs are expected to decline (or other performance measures improve) in the future. Forecasting is certainly very useful. Nevertheless, the previous literature is notably mute on how the improvement or learning occurs. Without that information about how the process operates, the traditional learning curve cannot improve the rate of learning. This paper transforms the curve so that it not merely forecasts but rather actively creates the learning and knowledge. By extending work of the authors (Zangwill and Kantor, 1998), this paper constructs a new theoretical framework for learning and making improvements based upon learning cycles. This approach allows what is learned in one period to be intelligently applied to the next, improving the rate of learning right as production is occurring. Previous approaches could not do this. Because it should quickly increase the rate of learning, this approach might have important industrial applications.