An Assessment of the Singerian Inquiring Organizational Model: Cases from Academia and the Utility Industry

The Singerian model of inquiring organizations is reviewed (Proceedings of the Americas Conference on Information Systems, Phoenix, Arizona Aug. 16–18,1996:443–445; Australian Journal of Information Systems, 1998;6(1):3–15, reprinted in Foundations of Information Systems: Towards a Philosophy of Informative Technology, http://www.cba.uh.edu/~parks/fis/fisart.htm.) and two cases of organizations representing the Singerian approach are analyzed. The Singerian organization, based on Churchman's (The Design of Inquiring Systems: Basic Concepts of Systems and Organizations, New York, NY: Basic Books Inc., 1971.) Singerian inquiring system, has ethical purposes, treats employees, customers and other stakeholders with trust and respect, and pursues socially responsible goals, including a respect for the environment. Decision making in Singerian organizations is holistic and systemic, and includes diverse stakeholders, thus requiring an open, cooperative culture, wherein debate is encouraged. Authority is diffuse. Measurement is fundamental. A system of measures is continuously monitored, refined, compared and revised to assess progress. Cooperation and communication are supported with various types of groupware, including e-mail, electronic meeting systems, group support systems, negotiation support systems, discussion forums, knowledge bases, knowledge management systems and the like. The Internet and World Wide Web may serve as resource and dissemination agents for exoteric knowledge created by the organization. Two organizations, an Energy Service Company and an academic Center for the Management of Information Systems, are analyzed for their conformance to and success with the Singerian approach. The organizations tended to prosper when the model was followed, but ran into difficulties when it was not.     

Bridging the educational divide

The sharpest visible divide in Internet utilisation, which has deepened in recent years, is an educational one. Especially with regard to the learning disabled, the educational digital divide requires the improvement of inclusive didactical measures to promote media competence. A major prerequisite, which as a basic architectural principle determines systems design, in this respect demands support of evolutionary learning by tutorial learning systems designed as guidance systems which accord closely with the individual pupils evolutionary process.     

Learning management systems and cloud file hosting services: A study on students’ acceptance

The aim of this paper is to investigate the motivations that lead higher education students to replace several Learning Management Systems (LMS) services with cloud file hosting services for information sharing and collaboration among them. The research approach is based on the Technology Acceptance Model (TAM). More specifically, the model is devoted to identifying barriers and enablers to the acceptance of these technologies. A questionnaire comprising three factors (Attitude toward using technology, Perceived ease of use and Perceived usefulness) was applied to a sample consisting of 121 higher education students. Results show that the perceived ease of use of cloud file hosting services is above that of LMS tools and services and that cloud file hosting services presented higher levels of perceived usefulness than standard learning management tools. In addition, attitude toward using cloud file hosting services is well above that of using LMS tools.     

Adaptive and intelligent web based education system: Towards an integral architecture and framework

In this paper it is presented our contribution for carrying out adaptive and intelligent Web-based Education Systems (WBES) that take into account the individual student learning requirements, by means of a holistic architecture and Framework for developing WBES. In addition, three basic modules of the proposed WBES are outlined: an Authoring tool, a Semantic Web-based Evaluation, and a Cognitive Maps-based Student Model. As well, it is stated a Service Oriented Architecture (SOA) oriented to deploy reusable, accessible, durable and interoperable services. The approach enhances the Learning Technology Standard Architecture, proposed by IEEE-LTSA (Learning Technology System Architecture) [IEEE 1484.1/D9 LTSA (2001). Draft standard for learning technology – learning technology systems architecture (LTSA). New York, USA. URL: http://ieee.ltsc.org/wg1], and the Sharable Content Object Reusable Model (SCORM), claimed by Advanced Distributed Learning (ADL) [Advanced Distributed Learning Initiative (2004). URL: http://www.adlnet.org].     

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.     

Intrusion detection with evolutionary learning classifier systems

Evolutionary Learning Classifier Systems (LCSs) combine reinforcement learning or supervised learning with effective genetics-based search techniques. Together these two mechanisms enable LCSs to evolve solutions to decision problems in the form of easy to interpret rules called classifiers. Although LCSs have shown excellent performance on some data mining tasks, many enhancements are still needed to tackle features like high dimensionality, huge data sizes, non-uniform distribution of classes, etc. Intrusion detection is a real world problem where such challenges exist and to which LCSs have not previously been applied. An intrusion detection problem is characterised by huge network traffic volumes, difficult to realize decision boundaries between attacks and normal activities and highly imbalanced attack class distribution. Moreover, it demands high accuracy, fast processing times and adaptability to a changing environment. We present the results and analysis of two classifier systems (XCS and UCS) on a subset of a publicly available benchmark intrusion detection dataset which features serious class imbalances and two very rare classes. We introduce a better approach for handling the situation when no rules match an input on the test set and recommend this be adopted as a standard part of XCS and UCS. We detect little sign of overfitting in XCS but somewhat more in UCS. However, both systems tend to reach near-best performance in very few passes over the training data. We improve the accuracy of these systems with several modifications and point out aspects that can further enhance their performance. We also compare their performance with other machine learning algorithms and conclude that LCSs are a competitive approach to intrusion detection.

Enhancing the performance of an agent-based manufacturing system through learning and forecasting

Agent-based technology has been identified as an important approach for developing next generation manufacturing systems. One of the key techniques needed for implementing such advanced systems will be learning. This paper first discusses learning issues in agent-based manufacturing systems and reviews related approaches, then describes how to enhance the performance of an agent-based manufacturing system through learning from history (based on distributed case-based learning and reasoning) and learning from the future (through system forecasting simulation). Learning from history is used to enhance coordination capabilities by minimizing communication and processing overheads. Learning from the future is used to adjust promissory schedules through forecasting simulation, by taking into account the shop floor interactions, production and transportation time. Detailed learning and reasoning mechanisms are described and partial experimental results are presented.     

Semi-automatic assembly of learning resources

Technology Enhanced Learning is a research field that has matured considerably over the last decade. Many technical solutions to support design, authoring and use of learning activities and resources have been developed. The first datasets that reflect the tracking of actual use of these tools in real-life settings are beginning to become available. In this article, we present an exploratory study that relies on these datasets to support semi-automatic assembly of learning activities and resources for specific contexts. Starting from learning designs and other online sources that describe well designed learning experiences as they were used in practice, we derive sequencing patterns that capture re-occurring patterns of activities. A semi-automatic assembly framework uses these patterns to support teachers in the design and authoring of course activities. We present a case study that integrates recommendation support for sequencing activities as well as associated resources in the LAMS learning activity environment. Results indicate that the perceived usefulness is high: both teachers with expertise in the use of learning design tools as well as teachers with no background knowledge in the area indicate that the recommendations helped them in the authoring process. In addition, they feel more confident using learning design tools when support is provided that is driven by best practice knowledge.     

A new expert system for learning management systems evaluation based on neutrosophic sets

There has been a sudden increase in the usage of Learning Management Systems applications to support learner's learning process in higher education. Many studies in learning management system evaluation are implemented under complete information, while the real environment has uncertainty aspects. As these systems were described by development organizations with uncertainty terms such as vague, imprecise, ambiguity and inconsistent, that is why traditional evaluation methods may not be effective. This paper suggests neutrosophic logic as a better option to simulate human thinking than fuzzy logic because unlike fuzzy logic, it is able to handle indeterminacy of information which expresses the percentage of unknown parameters. As previous studies suggested neutrosophic decision maker and neutrosophic expert systems as future work in ecommerce and e‐learning applications, this paper presents neutrosphic expert system for learning management systems evaluation. Information for building and validating the neutrosophic expert system is collected from five experts using surveys, and then analysis is done by using Fuzzytech 5.54d software. Finally, the comparison between fuzzy expert system and neutrosophic expert system results show that the neutrosophic logic is capable of representing uncertainty in human thinking for evaluating Learning Management Systems.     

A multi-layered learning model

The growth of technology is leading mankind to an increased awareness of the need for more intelligent systems. However, one of the bottlenecks in building intelligent systems is the difficulty of acquisition, testing and refinement of domain specialists' knowledge. Learning capability offers a way through this bottleneck.In this paper, we describe a general-purpose learning model for use in an unstructured environment. The proposed model exploits different learning techniques to improve the coordination, to increase task and resource allocation efficiency and to refine problem-solving skills of system elements. The utility of such system is most evident in complex domains such as grasping unknown objects by a dextrous hand. An example of the proposed model is illustrated by an intelligent dextrous hand which learns to grasp unknown objects. Moreover, an expert system for grasp mode selection was implemented in a software package and an example of grasp mode generation is demonstrated.     

Towards a learning networked organisation: human capital, compatibility and usability in e-learning systems

In all parts of organisations there flourish developments of different new subsystems in areas of knowledge and learning. Over recent decades, new systems for classification of jobs have emerged both at the level of organisations and at a macro-labour market level. Recent developments in job evaluation systems make it possible to cope with the new demands for equity at work (between, for example, genders, races, physical abilities). Other systems have emerged to describe job requirements in terms of skills, knowledge and competence. Systems for learning at work and web-based learning have created a demand for new ways to classify and to understand the process of learning. Often these new systems have been taken from other areas of the organisation not directly concerned with facilitating workplace learning. All these new systems are of course closely interrelated but, in most organisations, a major problem is the severe lack of cohesion and compatibility between the different subsystems. The aim of this paper is to propose a basis for how different human resource systems can be integrated into the business development of an organisation. We discuss this problem and develop proposals alternative to integrated macro-systems. A key element in our proposition is a structure for classification of knowledge and skill to be used in all parts of the process. This structure should be used as an added dimension or an overlay on all other subsystems of the total process. This will facilitate a continued use of all existing systems within different organisations. We develop Burge's (personal communication) model for learning to show that learning is not a successive linear process, but rather an iterative process. In this way we emphasise the need for greater involvement of learners in the development of learning systems towards increased usability in a networked system. This paper is divided into two parts which are closely related. The first part gives an overview of the lack of compatibility between the different subsystems. In this first part we note two paradoxes which impact learning and for which we propose solutions. The second part deals with 'usability' aspects of these competency-related systems; in particular, usability in e-learning systems. In this second part we describe an example of a new organisational structure. We conclude by discussing four key concepts that are necessary conditions for organisations to address when developing their human capital. Establishing these conditions helps ensure compatibility and usability in e-learning systems.     

Genetic production systems for intelligent problem solving

The paper discusses an evolutionary knowledge approach to intelligent problem solving. A rule-based production system is used to model the problem and the means by which the problem space should be searched. Search heuristics are modelled as production rules. These rules are redundant as there may be more than one view on the best method for building solutions. Some rules may have complex reasoning for their actions, others have none. Deciding which rule is most appropriate is solved by a genetic algorithm and ultimately only the fitter rules will survive. The approach eliminates the necessity of designing problem specific search or variation operators, leaving the genetic algorithm to process patterns independent of the problem at hand. Learning methods and how they aid evolution is also discussed: they are Lamarckian learning and the Baldwin effect. The approach is tested on a scheduling problem.     

Master in Teacher Training: A real implementation of Active Learning

Teaching methods based on lectures often result in student passivity rather than pro-activity with the only goal of the student being to pass the final exam. Consequently, content retention is temporary and true learning is not achieved. Lack of student motivation can be solved by using Active Learning methodologies: serious games, Project-Based Learning (PBL), blended learning, etc. In addition, these methodologies enhance the development of the competences of students and provide a better evaluation of outcomes, provided adequate tools are used. However, apart from this, a more profound use of Information and Communications Technology (ICT) is needed to improve the effectiveness of learning and to create a learning system adapted to our current society. Learning Management Systems (LMS), social networks and blogs are indispensable for the efficient application of innovative learning methodologies. The Master in Teacher Training (MTT) provides an ideal case study as the most relevant features of Active Learning are present. The main contributions to this learning environment have been the incorporation of Active Learning methodologies (using real scenarios and project-based collaborative learning), an innovative method for assessing the Master and the impact of technology in learning. All of these have resulted in very positive results in relative to academic marks, surveys and entrepreneurship.     

Semantic Ontology Mapping for Interoperability of Learning Resource Systems using a rule-based reasoning approach

There is an increasing demand for sharing learning resources between existing learning resource systems to support reusability, exchangeability, and adaptability. The learning resources need to be annotated with ontologies into learning objects that use different metadata standards. These ontologies have introduced the problems of semantic and structural heterogeneity. This research proposes a Semantic Ontology Mapping for Interoperability of Learning Resource Systems. To enable semantic ontology mapping, this research proposes conflict detection and resolution techniques for both semantic and structural conflicts. The Semantic Bridge Ontology has been proposed as a core component for generating mapping rules to reconcile terms defined in local ontologies into terms defined in the target common ontology. This work defines the reasoning rules to classify related learning objects to enhance the powerful deductive reasoning capabilities of the system. As a consequence, ontology-based learning object metadata are generated and used by the semantic query engine to facilitate user queries of learning objects across heterogeneous learning resource systems.     

Learning teaching strategies in an Adaptive and Intelligent Educational System through Reinforcement Learning

One of the most important issues in Adaptive and Intelligent Educational Systems (AIES) is to define effective pedagogical policies for tutoring students according to their needs. This paper proposes to use Reinforcement Learning (RL) in the pedagogical module of an educational system so that the system learns automatically which is the best pedagogical policy for teaching students. One of the main characteristics of this approach is its ability to improve the pedagogical policy based only on acquired experience with other students with similar learning characteristics. In this paper we study the learning performance of the educational system through three important issues. Firstly, the learning convergence towards accurate pedagogical policies. Secondly, the role of exploration/exploitation strategies in the application of RL to AIES. Finally, a method for reducing the training phase of the AIES.

Evaluating learning, design, and engagement in web-based learning tools (WBLTs): The WBLT Evaluation Scale

Web-based learning tools (WBLTs), also known as learning objects, are online, interactive tools that support the learning of specific concepts by enhancing, amplifying, and/or guiding the cognitive processes of learners. Research examining the effectiveness of WBLTs is somewhat limited because sound, reliable, valid evaluation metrics are sparse, particularly in the K-12 environment. The purpose of the following study was to re-examine the Learning Object Evaluation Scale for students (LOES-S), originally developed by Kay and Knaack (2009), to assess three key constructs: learning, design, and engagement. Over 800 middle and secondary schools students participated in high quality, pre-designed lessons intended to accentuate the use of WBLTs. Data collected from the new WBLT Evaluation Scale demonstrated good internal reliability, construct validity, convergent validity and predictive validity.     

Saving time or innovating practice: Investigating perceptions and uses of Learning Management Systems

Learning Management Systems (LMS) are web-based systems that allow instructors and/or students to share materials, submit and return assignments, and communicate online. In this study, we explored the uses and perceived benefits of using a LMS to support traditional classroom teaching as reported by instructors and students at a large American Midwestern university. We examined two years of survey data focusing on specific uses of the LMS that emphasized either efficient communication or interactive teaching and learning practices. We matched aggregate user log data with corresponding survey items to see if system use was consistent with patterns seen in the survey results. Findings suggest that instructors and students value tools and activities for efficient communication more than interactive tools for innovating existing practices. However, survey item analysis reveals that instructors and students also highly value the teaching and learning tools within the LMS.     

CAL delivery selection criteria and authoring systems

Abstract  In the past, authoring systems have been used to generate computer-assisted learning materials that have often followed the rather restrictive programmed learning format. A number of innovative ways of using recently developed systems to develop CAL packages that include a wider range of learning activities (access to data bases, simulations, interactive video, etc.) are described. Analogous to the concept of a media selection model, a computer delivery mode selection model is presented as an aid to decision making for designers. This increase in flexibility should broaden the applicability of computerassisted learning (CAL) to a wider range of educational objectives and raise the level of the cognitive emphasis in learning packages, as well as facilitate the implementation of visually appealing materials. The paper also implies a standard of acceptability for authoring systems.     

A web-based e-learning system for increasing study efficiency by stimulating learner’s motivation

Due to the opportunities provided by the Internet, more and more people are taking advantage of distance learning courses and during the last few years enormous research efforts have been dedicated to the development of distance learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is about 30%. One of the reasons is the low study desire when the learner studies the learning materials. In this research, we propose an interactive Web-based e-learning system. The purpose of our system is to increase the e-learning completion rate by stimulating learner’s motivation. The proposed system has three subsystems: the learning subsystem, learner support subsystem, and teacher support subsystem. The learning subsystem improves the learner’s study desire. The learner support subsystem supports the learner during the study, and the teacher support subsystem supports the teacher to get the learner’s study state. To evaluate the proposed system, we developed several experiments and surveys. By using new features such as: display of learner’s study history, change of interface color, encourage function, ranking function, self-determination of the study materials, and grouping of learners, the proposed system can increase the learning efficiency.

数据丢失下多智能体系统迭代学习跟踪控制

考虑数据丢失下非线性多智能体系统的一致性跟踪问题。假设多智能体系统使用固定网络通信拓扑结构,由于通信网络自身限制导致多智能体系统中存在数据丢失现象。将数据丢失现象描述为取值0/1的随机伯努利序列,设计分布式一致性跟踪误差,提出该系统在数据丢失下的P型迭代学习控制算法。采用压缩映射的方法给出收敛性条件,并在理论上分析了跟踪误差的收敛性。仿真结果表明,提出的算法可以实现该系统在有限时间区间上对期望轨迹的完全跟踪,验证了算法的有效性。