Learning styles and formative assessment strategy: enhancing student achievement in Web-based learning

Abstract The purpose of this research was to investigate the effects of formative assessment and learning style on student achievement in a Web-based learning environment. A quasi-experimental research design was used. Participants were 455 seventh grade students from 12 classes of six junior high schools. A Web-based course, named BioCAL, combining three different formative assessment strategies was developed. The formative assessment strategies included Formative Assessment Module of the Web-Based Assessment and Test Analysis system (FAM-WATA) (with six Web-based formative assessment strategies), Normal Module of Web-Based Assessment and Test Analysis system (N-WATA) (only with partial Web-based formative assessment strategy) and Paper and Pencil Test (PPT) (without Web-based formative assessment strategy). Subjects were tested using Kolb's Learning Style Inventory, and assigned randomly by class into three groups. Each group took Web-based courses using one of the formative assessment strategies. Pre- and post-achievement testing was carried out. A one-way ANCOVA analysis showed that both learning style and formative assessment strategy are significant factors affecting student achievement in a Web-based learning environment. However, there is no interaction between these two factors. A post hoc comparison showed that performances of the FAM-WATA group are higher than the N-WATA and PPT groups. Learners with a 'Diverger' learning style performed best followed by, 'Assimilator', 'Accommodator', and 'Converger', respectively. Finally, FAM-WATA group students are satisfied with six strategies of the FAM-WATA.     

Cooperative situation assessment in a maritime scenario

In large‐scale, complex domains such as space defense and security systems, situation assessment and decision making are evolving from centralized models to high‐level, net‐centric models. In this context, collaboration among the many actors involved in the situation assessment process is critical to achieve a prompt reaction as needed in the operational scenario. In this paper, we propose a multiagent‐based approach to situation assessment, where agents cooperate by sharing local information to reach a common and coherent assessment of situations. Specifically, we characterize situation assessment as a classification process based on OWL ontology reasoning, and we provide a protocol for cooperative multiagent situation assessment, which allows the agents to achieve coherent high‐level conclusions. We validate our approach in a real maritime surveillance scenario, where our prototype system effectively supports the user in detecting and classifying potential threats; moreover, our distributed solution performs comparably to a centralized method, while preserving independence of decision makers and dramatically reducing the amount of communication required. © 2012 Wiley Periodicals, Inc.     

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.     

Enhancing Employee Job satisfaction via E-learning: The Mediating Role of an Organizational Learning Culture

Although e-learning systems have been widely employed to develop employee learning at a workplace, discerning what influences the interaction between employee acceptance of an e-learning system within an organizational learning culture (OLC) and the resultant impact on job attitudes remains under-researched and requires further investigation. This study examined the relationship between employee perceptions of an e-learning system, the existing OLC, and job satisfaction. Structural equation modeling was applied to analyze data collected from 297 employees of a telecommunications company. The results suggest that employee acceptance of e-learning is a positive predictor of the OLC and employee job satisfaction. OLC mediates the relationship between the use and acceptance of e-learning by employees and their job satisfaction. Recommendations and implications are subsequently provided in terms of the future for human resource development researches and practices.     

Learning in linear neural networks: a survey

Networks of linear units are the simplest kind of networks, where the basic questions related to learning, generalization, and self-organization can sometimes be answered analytically. We survey most of the known results on linear networks, including: 1) backpropagation learning and the structure of the error function landscape, 2) the temporal evolution of generalization, and 3) unsupervised learning algorithms and their properties. The connections to classical statistical ideas, such as principal component analysis (PCA), are emphasized as well as several simple but challenging open questions. A few new results are also spread across the paper, including an analysis of the effect of noise on backpropagation networks and a unified view of all unsupervised algorithms.     

E-learning adoption of academicians: a proposal for an extended model

From the beginning of the e-learning technology era, many concerns arose regarding the use of e-learning in everyday academic processes. Thus, in recent years, a number of studies were conducted on e-learning acceptance, mostly with the use of Unified Theory of Acceptance and Use of Technology (UTAUT) Model. Current study investigates the determinants of acceptance of academic e-learning technologies with the use of proposed modified UTAUT model. The basic UTAUT model was supplemented by the examined new determinants: system interactivity and the area of scientific expertise. Therefore, the paper contributes to UTAUT theory, applied in e-learning, by utilising empirical research with a modified UTAUT model including these two new variables. This study confirmed the hypotheses related to the effects of performance expectancy, effort expectancy, and system interactivity on behavioural intention to use e-learning technologies, and also stated that the area of scientific expertise does not have a direct impact on the intention to use e-learning technologies. The research model has been tested in an academic environment in Turkey.     

Learning patterns in ambient intelligence environments: a survey

It is essential for environments that aim at helping people in their daily life that they have some sort of Ambient Intelligence. Learning the preferences and habits of users then becomes an important step in allowing a system to provide such personalized services. Thus far, the exploration of these issues by the scientific community has not been extensive, but interest in the area is growing. Ambient Intelligence environments have special characteristics that have to be taken into account during the learning process. We identify these characteristics and use them to highlight the strengths and weaknesses of developments so far, providing direction to encourage further development in this specific area of Ambient Intelligence.     

Learning from positive and unlabeled data: a survey

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.

     

Explanation-Based Learning: A survey

The paper provides an introductory survey of Explanation-Based Learning (EBL). It attempts to define EBL's position in AI by exploring its relationship to other AI techniques, including other sub-fields of machine learning. Further issues discussed include the form of learning exhibited by EBL and potential applications of the method.     

智能代理和学习对象的集成—— 一种应用于在线教学的设计思想

讨论了一种很有发展前景的E教学开发设计思想,集智能代理和学习对象为一体来开发更智能化,更高交互性和高扩展性的在线教学系统。在该系统中,每个对象都被附加上一个智能代理,从而在在线学习中像一个代理程序一样,从学习环境中获得各项实时参数,然后根据这些参数采取相应的操作。此外,与内置智能代理的对象一样,用户模块被设计用来记录和保存一切与用户有关的信息,包括习惯的学习方式以及所有在线学习课程当中的活动事件。由于用户模块可以更好地了解每个用户,系统就会以一种更具有针对性的方式工作,就像老师在课堂上可以对不同的学生采取     

Learning to write in an online writing center: The effect of learning styles on the writing process

One of the main advantages of online learning materials is that they can be adapted for students with different learning styles. This article presents a study and a methodology to investigate whether students with different learning styles make use of the potential flexibility of online learning materials, i.c. in the context of an online writing center. The study aims to investigate the effect of learning styles on (a) the students' approach to the writing task (process), and (b) on the letters they write (product). Twenty students each completed a module on writing ‘bad news’ letters designed for Business Communication courses. Their reading and writing processes were recorded. The letters were also graded to determine their quality. An effect of learning style was found: Active and Reflective writers approached the task differently, but only in the beginning of the process. In this early stage Reflective learners were more likely to focus on the theory section than Active learners. This suggests that writers with different learning styles tackle the learning materials in different ways, often in line with the preferences that characterize their learning styles. However, no effect of learning style on text quality was found.     

De sitter relativity: a natural scenario for an evolving Λ

In de Sitter special relativity, spacetime translations are replaced by a combination of translations and proper conformal transformations. As a consequence, the energy-momentum current is replaced by a combination of ordinary energy-momentum and proper conformal currents. Whereas the ordinary energy-momentumtensor remains to be a dynamic source of the spacetime curvature, the proper conformal current appears as a kinematic source of ??. The de Sitter special relativity, therefore, allows for a new interpretation of dark energy as an entity encoded in the kinematic group of spacetime. Furthermore, since ordinary energy is allowed to transform into dark energy and vice versa, it provides a natural scenario for an evolving cosmological term. A qualitative discussion on how a ??-evolving universe would behave is presented.     

Truck driver fatigue risk assessment and management: a multinational survey

As part of an organizational review of safety, interviews and questionnaire surveys were performed on over 700 commercial goods drivers and their managers within a series of related companies operating across 17 countries. The results examine the reported incidence of fatigue-related problems in drivers and their associations with near miss and accident experience as well as work and organizational factors. Experience of fatigue problems while driving was linked to time of day and rotation of shifts, though most associations were small. There were significant associations found between fatigue experiences and driver and management systems of break taking and route scheduling. The quantitative combined with qualitative information suggested that, where feasible, more flexible approaches to managing the scheduling and sequencing of deliveries assisted drivers in managing their own fatigue problems through appropriate break-taking. The results are interpreted within the overarching principles of risk assessment and risk control.     

Truck driver fatigue risk assessment and management: a multinational survey

As part of an organizational review of safety, interviews and questionnaire surveys were performed on over 700 commercial goods drivers and their managers within a series of related companies operating across 17 countries. The results examine the reported incidence of fatigue-related problems in drivers and their associations with near miss and accident experience as well as work and organizational factors. Experience of fatigue problems while driving was linked to time of day and rotation of shifts, though most associations were small. There were significant associations found between fatigue experiences and driver and management systems of break taking and route scheduling. The quantitative combined with qualitative information suggested that, where feasible, more flexible approaches to managing the scheduling and sequencing of deliveries assisted drivers in managing their own fatigue problems through appropriate break-taking. The results are interpreted within the overarching principles of risk assessment and risk control.     

How a Pareto frontier complements scenario projections in land use change impact assessment

To evaluate the sustainability of potential agricultural land developments, scenario projections with land use change models are often combined with environmental impact assessments. Although this allows inter-scenario comparison of impacts, it does not permit interpretation of scenarios in the light of theoretically optimal impacts. A Pareto frontier provides this information. We demonstrate this for ethanol production in Goiás, Brazil, in 2030. For a Business-as-Usual scenario projection, the spatial configuration, production costs, and GHG emissions of the production chain are compared with those obtained from spatial optimization and summarized by the Pareto frontier. Projected production costs are 729 $/m³ ethanol, with GHG emissions of 40 kg CO₂-eq/m³ ethanol. The Pareto frontier indicates an improvement potential of ∼50 $/m³ ethanol when keeping emissions fixed, or ∼250 kg CO₂-eq/m³ ethanol when keeping costs fixed. Robust locations having low costs and emissions show where and how improvements are reached, offering instruments for policy (re)design.     

态势估计中的想定模型和仿真技术

通过仿真模拟获得战场数据及态势估计中算法训练、评价所需的数据类型与数量。将本体引入群体组织建模技术中,并结合作战想定生成的特点和作战仿真的需求,实现了一个基于本体的群体组织想定模型。研究表明可以通过仿真训练或者测试态势来估计分类器。     

Learning programming via worked-examples: Relation of learning styles to cognitive load

This paper describes an experiment that compared learners with contrasting learning styles, Active vs. Reflective, using three different strategies for learning programming via worked-examples: Paired-method, Structure-emphasising, and Completion. The quality of the learners’ acquired cognitive schemata was assessed in terms of their post-test performance. The experiment investigated variations in learners’ cognitive load, taking both the learning strategies and the learners’ learning styles into account. Overall, the results of the experiment were inconsistent. In comparing the effects of the strategies during the learning phase, the study found significant differences in cognitive load. Unexpectedly, no differences were then detected either in cognitive load or in performance during the post-test (post-test). In comparing the effects of the learning styles during the learning phase and the transfer phase, medium effect sizes suggested that learning style may have had an effect on cognitive load. However, no significant difference was observed in performance during the post-test.