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.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Learning from openness : the dynamics of breadth in external innovation linkages

We explore how openness in terms of external linkages generates learning effects, which enable firms to generate more innovation outputs from any given breadth of external linkages. Openness to external knowledge sources, whether through search activity or linkages to external partners in new product development, involves a process of interaction and information processing. Such activities are likely to be subject to a learning process, as firms learn which knowledge sources and collaborative linkages are most useful to their particular needs, and which partnerships are most effective in delivering innovation performance. Using panel data from Irish manufacturing plants, we find evidence of such learning effects: establishments with substantial experience of external collaborations in previous periods derive more innovation output from openness in the current period. © 2013 The Authors. Strategic Management Journal published by John Wiley & Sons Ltd.

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.

A study of learners' perceived value and satisfaction for implied effectiveness of online learning systems

Online learning systems (OLS) have become center stage for corporations and educational institutions as a competitive tool in the knowledge economy. The satisfaction construct has received extensive coverage in information systems literature as an indicator of effectiveness but has been criticized for lack of validity; yet, the value construct has been largely ignored, although it has a long history in psychology, sociology, and behavioral science. The purpose of this dissertation is to investigate the value and satisfaction constructs in the context of OLS, and their perceived by learners relationship for implied effectiveness of OLS. ^ First, a qualitative phase is employed to gather OLS values from learners' focus groups, followed by a pilot phase to refine a proposed instrument, and a main phase to validate the survey. Responses were received from 75 students in four focus groups, 141 in the pilot, and 207 the main survey. Extensive data cleaning and exploratory factor analysis were done to identify factors of learners' perceived value and satisfaction of OLS. Then, Value-Satisfaction grids and the Learners' Value Index of Satisfaction (LeVIS) were developed as benchmarking tools of OLS. Moreover, Multicriteria Decision Analysis (MCDA) techniques were employed to impute value from satisfaction scores in order to reduce survey response time. ^ The results provided four satisfaction and four value factors with high reliability (Cronbach's α). Moreover, value and satisfaction were found to have low linear and nonlinear correlations, indicating that they are two distinct uncorrelated constructs. This is consistent with the literature. Value-Satisfaction grids and the LeVIS index indicated relatively high effectiveness for technology and support characteristics, relatively low effectiveness for professor's characteristics, while course and learner characteristics indicated average effectiveness. ^ The main contributions of this study include identifying, defining, and articulating the relationship between value and satisfaction constructs as assessment of users' implied IS effectiveness, as well as assessing the accuracy of MCDA procedures to predict value scores, thus reducing by half the survey questionnaire size. ^

Group presence in virtual worlds: Supporting collaborative e-learning

Even though e-learning endeavors have significantly proliferated in recent years, current e-learning technologies provide poor support for group-oriented learning. The now popular virtual world's technologies offer a possible solution. Virtual worlds provide the users with a 3D - computer generated shared space in which they can meet and interact through their virtual representations. Virtual worlds are very successful in developing high levels of engagement, presence and group presence in the users. These elements are also desired in educational settings since they are expected to enhance performance. The goal of this research is to test the hypothesis that a virtual world learning environment provides better support for group-oriented collaborative e-learning than other learning environments, because it facilitates the emergence of group presence. To achieve this, a quasi-experimental study was conducted and data was gathered through the use of various survey instruments and a set of collaborative tasks assigned to the participants. Data was gathered on the dependent variables: Engagement, Group Presence, Individual Presence, Perceived Individual Presence, Perceived Group Presence and Performance. The data was analyzed using the statistical procedures of Factor Analysis, Path Analysis, Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA). The study provides support for the hypothesis. The results also show that virtual world learning environments are better than other learning environments in supporting the development of all the dependent variables. It also shows that while only Individual Presence has a significant direct effect on Performance; it is highly correlated with both Engagement and Group Presence. This suggests that these are also important in regards to performance. Developers of e-learning endeavors and educators should incorporate virtual world technologies in their efforts in order to take advantage of the benefit they provide for e-learning group collaboration.

“Half Bricks and Half Clicks”: Is Blended Onsite and Online Teaching and Learning the Best of Both Worlds?

Blended learning, or combined onsite and online learning, is increasingly popular in higher education. This literature review investigated its effectiveness compared to traditional teaching and learning, concluding that with retention and achievement, blended learning is similar or slightly better; with interaction and satisfaction, blended teaching and learning are more effective.

Adults Learning Math Online: A Surprising Harmony

Adults returning to school face challenges including overcoming math anxiety. Many choose online courses as they balance life and work schedules. Online math courses therefore can be restructured to prevent math anxiety by catering to individual learning styles, providing tools that aid concept attainment, and using problem-based learning strategies.