Factors affecting student attitudes toward flexible online learning in management education

In response to recent technological advances and the trend toward flexible learning in education, the authors examined the factors affecting student satisfaction with flexible online learning. The authors identified 2 key student attributes of student satisfaction: (a) positive perceptions of technology in terms of ease of access and use of online flexible learning material and (b) autonomous and innovative learning styles. The authors derived measures of perceptions of technology from research on the Technology Acceptance Model and used locus of control and innovative attitude as indicators of an autonomous and innovative learning mode. First-year students undertaking an introductory management course completed surveys at the beginning (n = 248) and at the end (n = 256) of course work. The authors analyzed the data by using structural equation modeling. Results suggest that student satisfaction is influenced by positive perceptions toward technology and an autonomous learning mode.

Building learning communities to support beginning teachers use of technology

This paper reports on the early stages of a three year study that is investigating the impact of a technology-enriched teacher education program on beginning teachers' integration of computers, graphics calculators, and the internet into secondary school mathematics classrooms. Whereas much of the existing research on the role of technology in mathematics learning has been concerned with effects on curriculum content or student learning, less attention has been given to the relationship between technology use and issues of pedagogy, in particular the impact on teachers' professional learning in the context of specific classroom and school environments. Our research applies sociocultural theories of learning to consider how beginning teachers are initiated into a collaborative professional community featuring both web-based and face to face interaction, and how participation in such a community shapes their pedagogical beliefs and practices. The aim of this paper is to analyse processes through which the emerging community was established and sustained during the first year of the study. We examine features of this community in terms of identity formation, shifts in values and beliefs, and interaction patterns revealed in bulletin board discussion between students and lecturers.

Noise, regularizers, and unrealizable scenarios in online learning from restricted training sets

We study the dynamics of on-line learning in multilayer neural networks where training examples are sampled with repetition and where the number of examples scales with the number of network weights. The analysis is carried out using the dynamical replica method aimed at obtaining a closed set of coupled equations for a set of macroscopic variables from which both training and generalization errors can be calculated. We focus on scenarios whereby training examples are corrupted by additive Gaussian output noise and regularizers are introduced to improve the network performance. The dependence of the dynamics on the noise level, with and without regularizers, is examined, as well as that of the asymptotic values obtained for both training and generalization errors. We also demonstrate the ability of the method to approximate the learning dynamics in structurally unrealizable scenarios. The theoretical results show good agreement with those obtained by computer simulations.

Automatic identification of best answers in online enquiry communities

Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.

Personal knowledge development in online learning:a model for measuring externalisation, combination and internalisation

This paper presents a model for measuring personal knowledge development in online learning environments. It is based on Nonaka‘s SECI model of organisational knowledge creation. It is argued that Socialisation is not a relevant mode in the context of online learning and was therefore not covered in the measurement instrument. Therefore, the remaining three of SECI‘s knowledge conversion modes, namely Externalisation, Combination, and Internalisation were used and a measurement instrument was created which also examines the interrelationships between the three modes. Data was collected using an online survey, in which online learners report on their experiences of personal knowledge development in online learning environments. In other words, the instrument measures the magnitude of online learners‘ Externalisation and combination activities as well as their level of internalisation, which is the outcome of their personal knowledge development in online learning.

Prototyping Adaptive Online Learning Courses

This article describes the process of prototyping adaptive online learning using the authoring tool for developers, which is based on ontologies. The article also gives a brief overview of contemporary situation and describes modern trends of evolution e-learning courses and present standards in this area. It also describes architecture of system VITA II.

Online learning in discrete hidden Markov models

We present and analyze three different online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare their performance with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of the generalization error we draw learning curves in simplified situations and compare the results. The performance for learning drifting concepts of one of the presented algorithms is analyzed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented. © 2006 American Institute of Physics.

The Cascade Neo-Fuzzy Architecture and its Online Learning Algorithm

In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.