Rotational perspective and learning procedural tasks from dynamic media

There have been conflicting accounts regarding the effectiveness of animations for learning. Procedural motor learning represents one of the few areas in which animations have consistently shown to be facilitative. Some have suggested that this benefit is related to activation of the mirror neuron system (MNS), with higher activation leading to better performance. This study examines this explanation, and observed the effects of instructional media (animation vs. static), as a function of viewing perspective (face-to-face vs. over-the-shoulder) on understanding a procedural motor task (knot tying). Results indicate that performance was significantly improved with animations over static images, however this appeared to be most pronounced in situations which matched the learners' own perspective (i.e., over-the-shoulder). These findings have implications for the design of instructional media for procedural motor tasks and provide confirmation of the assertion that appropriate activation of the perceptual system can be leveraged to increase performance.     

Scaffolding game-based learning: Impact on learning achievements,perceived learning,and game experiences

One of the central challenges of integrating game-based learning in school settings is helping learners make the connections between the knowledge learned in the game and the knowledge learned at school, while maintaining a high level of engagement with game narrative and gameplay. The current study evaluated the effect of supplementing a business simulation game with an external conceptual scaffold, which introduces formal knowledge representations, on learners' ability to solve financial-mathematical word problems following the game, and on learners' perceptions regarding learning, flow, and enjoyment in the game. Participants (M_age = 10.10 years) were randomly assigned to three experimental conditions: a “study and play” condition that presented the scaffold first and then the game, a “play and study” condition, and a “play only” condition. Although no significant gains in problem-solving were found following the intervention, learners who studied with the external scaffold before the game performed significantly better in the post-game problem-solving assessment. Adding the external scaffold before the game reduced learners' perceived learning. However, the scaffold did not have a negative impact on reported flow and enjoyment. Flow was found to significantly predict perceived learning and enjoyment. Yet, perceived learning and enjoyment did not predict problem-solving and flow directly predicted problem solving only in the “play and study” condition. We suggest that presenting the scaffold may have “problematized” learners' understandings of the game by connecting them to disciplinary knowledge. Implications for the design of scaffolds for game-based learning are discussed.     

On iterative learning from different tracking tasks in the presence of time-varying uncertainties.

In this paper, we introduce a new iterative learning control (ILC) method, which enables learning from different tracking control tasks. The proposed method overcomes the imitation of traditional ILC in that, the target trajectories of any two consecutive iterations can be completely different. For non-linear systems with time-varying and time-invariant parametric uncertainties, the new learning method works effectively to nullify the tracking error. To facilitate the learning control system design and analysis, in the paper we use a composite energy function (CEF) index, which consists of a positive scalar function and L2 norm of the function approximation error.     

Effects of user age and target-expansion methods on target-acquisition tasks using a mouse

Target expansion, i.e., the increase of target size according to cursor movement, can be a practical scheme to improve the usability of target-selection tasks using a mouse. This study examined the effects of different user age groups and target-expansion methods on target-acquisition tasks with grouped icons. Twenty-eight subjects performed acquisition tasks under eight experimental conditions: combinations of four expansion areas (no, one-icon, fish-eye, and group expansion) and two expansion techniques (occlusion and push). Older users took longer to acquire targets than younger users; however, they showed no significant difference in accuracy. Target expansion did not substantially improve performance speed compared to the static condition. However, the error rate was lowest when group area was expanded with the push technique, and both age groups were most satisfied with one-icon area expansion with the occlusion technique. We suggest alternative guidelines in designing target-expansion schemes.     

Autonomous learning of sequential tasks: experiments and analyses

Presents a learning model CLARION, which is a hybrid model based on the two-level approach proposed by Sun. The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that neural to symbolic representations). The model utilizes procedural and declarative knowledge (in neural and symbolic representations, respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and analyses of various ways are reported that shed light on the advantages of the model.     

Accuracy-based learning classifier systems: models, analysis and applications to classification tasks

Recently, Learning Classifier Systems (LCS) and particularly XCS have arisen as promising methods for classification tasks and data mining. This paper investigates two models of accuracy-based learning classifier systems on different types of classification problems. Departing from XCS, we analyze the evolution of a complete action map as a knowledge representation. We propose an alternative, UCS, which evolves a best action map more efficiently. We also investigate how the fitness pressure guides the search towards accurate classifiers. While XCS bases fitness on a reinforcement learning scheme, UCS defines fitness from a supervised learning scheme. We find significant differences in how the fitness pressure leads towards accuracy, and suggest the use of a supervised approach specially for multi-class problems and problems with unbalanced classes. We also investigate the complexity factors which arise in each type of accuracy-based LCS. We provide a model on the learning complexity of LCS which is based on the representative examples given to the system. The results and observations are also extended to a set of real world classification problems, where accuracy-based LCS are shown to perform competitively with respect to other learning algorithms. The work presents an extended analysis of accuracy-based LCS, gives insight into the understanding of the LCS dynamics, and suggests open issues for further improvement of LCS on classification tasks.     

Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency

The effects of individual versus group learning (in triads) on efficiency of retention and transfer test performance in the domain of biology (heredity) among 70 high-school students were investigated. Applying cognitive load theory, the limitations of the working memory capacity at the individual level were considered an important reason to assign complex learning tasks to groups rather than to individuals. It was hypothesized that groups will have more processing capacity available for relating the information elements to each other and by doing so for constructing higher quality cognitive schemata than individuals if the high cognitive load imposed by complex learning tasks could be shared among group members. In contrast, it was expected that individuals who learn from carrying out the same complex tasks would need all available processing capacity for remembering the interrelated information elements, and, consequently, would not be able to allocate resources to working with them. This interaction hypothesis was confirmed by the data on efficiency of retention and transfer test performance; there was a favorable relationship between mental effort and retention test performance for the individual learners as opposed to a favorable relationship between transfer test performance and mental effort for the students who learned in groups.     

Anticipation: learning from the past. The Russian/Soviet contributions to the science of anticipation

The focus on Russian/Soviet contributions is only an opportunity to understand the objective premises of anticipation. Since anticipation expresses a main concept characterizing human action, it is important to see whether and how it corresponds to the neuro-physiology of the human. The aim of this review is to show that anticipation is neuro-physiologically constitutive and is intertwined with all other reflective, cognitive, and coordinative functions that form an inseparable unity in the process of adaptation. The experiments described in the book draw attention to anticipation as the internal tendency of the living that cannot be ignored. The review highlights the dialectic of continuity and discontinuity in the living from the standpoint of anticipation, and the holistic conclusions of the scientific research regarding the living and the human being.     

Evaluating e-government: learning from the experiences of two UK local authorities

Abstract. Part of the remit of public sector management includes planning and reflecting on capital expenditure on new technology. With this in mind, the role that information systems play in supporting improvements in e-government service delivery to stakeholder groups continues to attract much attention. The authors of this paper seek to define the scope and role that information systems evaluation plays within the public sector. In particular, the authors assess whether public sector organizations might benefit from the use of established ex-ante evaluation techniques, when applied to analyse the impact of e-government information systems. Following a comprehensive review of the normative literature, an initial conceptual framework for public sector information systems evaluation is proposed, which is then empirically explored within two local government authorities. The conceptual framework is then revised by using the structured case approach, which is dependent on an iterative research cycle where triangulated data are elicited. This then supports the emergence of new concepts during each research cycle that leads to the view that information systems evaluation in the public sector is a process of experiential and subjective judgement, which is grounded in opinion and world views. This leads the authors to challenge the appropriateness of traditional modes of investment appraisal when applied in the public sector. The finalized framework embraces investment decisions, evaluation methods, culture and structure, as well as post hoc evaluation. It emphasizes the importance of situated, interpretive user assessments in evaluating e-government investments.     

Truth and credibility as a double ambition: reconstruction of the built past,experiences and dilemmas

The reconstruction of our historical heritage involves a few fundamental problems. We mention only two. There can be many interpretations of scientific data which can result in different reconstructions: however, the appearance of an image or animation in a digital format out of context is nearly always misleading. Therefore, the verisimilitude of the computer‐based image should be questioned in the same manner as a picture in a journal, but the digital medium seldom facilitates this questioning. Furthermore, the diffusion of visualizations raises concerns; currently there is no established ontology to moderate the process. This article is based on our own experiences and attempts to contribute to this ontology. In this, scientific ‘truth’ will go hand in hand with artistic credibility, which to many may come as a surprise. Copyright © 2003 John Wiley & Sons, Ltd.     

Static and interactive infographics in daily tasks: A value-in-use and quality of interaction user study

Infographics are a common visual means to inform users. This paper investigates how lay people of different age, gender and educational background perceive the use of infographics for information visualization in daily tasks. We chose three topics of general interest: weather, study and work and three infographics, one for each topic. We administered a questionnaire to people randomly split in two groups: the first group interacted with a static version of each infographic, i.e., a snapshot of it; the second group interacted with the fully configurable infographics. We aimed to assess information quality on different dimensions, to take into account both formal and substantial aspects; interaction quality along dimensions like usability and ease of use; and design quality on the dimensions of the Visualization Wheel by Cairo, to assess the trade-off between information complexity and aesthetics of infographics. The goal was to measure whether the quality of infographics affects the perception of information and the users' interaction. The overall results suggest that, although interactive infographics are perceived as more complex, the experience with them is better. From our observations, we derived a model to assess the overall quality of static and interactive infographics, based on information, interaction and design quality dimensions.     

Incremental motion learning through kinesthetic teachings and new motion production from learned motions by a humanoid robot

This work presents a new incremental motion learning algorithm through kinesthetic teachings and a new motion production algorithm by combining learned motions in a humanoid robot. The proposed algorithms are useful for improving the motions that a humanoid robot can produce. The learning algorithm consists of data encoding, time alignment, dimensional reduction, parameter estimation in the Gaussian mixture model (GMM) of motions, GMM refinement, and motion generation steps. The overall procedure is built to be incremental. No historic data memorization is required in any step, and model parameters are enough information to generate motions. The motion production algorithm allows a robot to extract new motions simply from learned motions without requiring teaching sessions. A series of experiments with a humanoid robot serves to validate the performance of the proposed algorithms.     

Incremental and accurate computation of machine learning models with smart data summarization

Journal of Intelligent Information Systems - Nowadays, data scientists prefer “easy” high-level languages like R and Python, which accomplish complex mathematical tasks with a few lines...     

Incremental learning of context free grammars based on bottom-up parsing and search

This paper describes approaches for machine learning of context free grammars (CFGs) from positive and negative sample strings, which are implemented in Synapse system. The grammatical inference consists of a rule generation by “inductive CYK algorithm,” mechanisms for incremental learning, and search. Inductive CYK algorithm generates minimum production rules required for parsing positive samples, when the bottom-up parsing by CYK algorithm does not succeed. The incremental learning is used not only for synthesizing grammars by giving the system positive strings in the order of their length but also for learning grammars from other similar grammars. Synapse can synthesize fundamental ambiguous and unambiguous CFGs including nontrivial grammars such as the set of strings not of the form ww with w∈{a,b}⁺.     

An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM

Multimedia Tools and Applications - Depression has become one of the most widespread mental health disorders across the globe. Depression is a state of mind which affects how we think, feel, and...