Evaluating e-learning systems: an empirical investigation on students' perception in higher education area

In search of better, traditional learning universities have expanded their ways to deliver knowledge and integrate cost effective e-learning systems. Universities’ use of information and communication technologies has grown tremendously over the last decade. To ensure efficient use of the e-learning system, the Arab Open University (AOU) in Bahrain was the first to use e-learning system there, aimed to evaluate the good and bad practices, detect errors and determine areas for further improvements in usage. This study critically evaluated the students’ perception of the elearning system in Bahrain and recommended changes to improve students’ e-learning usage. Results of the study indicated that, in general, students have favourable perceptions toward using the e-learning system. This study has shown that technology acceptance is the most variable, factor that contributes to students’ perception and satisfaction of the e-learning system.

e-learning in project management using simulation models: a case study based on the replication of an experiment

Current e-learning systems are increasing their importance in higher education. However, the state of the art of e-learning applications, besides the state of the practice, does not achieve the level of interactivity that current learning theories advocate. In this paper, the possibility of enhancing e-learning systems to achieve deep learning has been studied by replicating an experiment in which students had to learn basic software engineering principles. One group learned these principles using a static approach, while the other group learned the same principles using a system-dynamics-based approach, which provided interactivity and feedback. The results show that, quantitatively, the latter group achieved a better understanding of the principles; furthermore, qualitatively, they enjoyed the learning experience

Results of a DSP based adaptive cancellation filter system for the chirp, pulsar and localiser sirens

Sirens’ used by police, fire and paramedic vehicles generate noise that propagates inside the vehicle cab that subsequently corrupts intelligibility of voice communications from the emergency vehicle to the control room. It is even common for the siren to be turned off to enable the control room to hear what is being said. Both fixed filter and adaptive filter systems have previously been developed to help cancel the transmission of the siren noise over the radio. Previous cancellation systems have only concentrated on the traditional 2-tone, wail and yelp sirens. This paper discusses an improvement to a previous adaptive filter system and presents the cancellation results to three new types of sirens; being chirp pulsar and localiser. A siren noise filter system has the capability to improve the response time for an emergency vehicle and thus help save lives. To date, this system has been tested using live recordings taken from a nonemergency situation with good results.

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.

Towards cost-sensitive adaptation: when is it worth updating your predictive model?

Our digital universe is rapidly expanding,more and more daily activities are digitally recorded, data arrives in streams, it needs to be analyzed in real time and may evolve over time. In the last decade many adaptive learning algorithms and prediction systems, which can automatically update themselves with the new incoming data, have been developed. The majority of those algorithms focus on improving the predictive performance and assume that model update is always desired as soon as possible and as frequently as possible. In this study we consider potential model update as an investment decision, which, as in the financial markets, should be taken only if a certain return on investment is expected. We introduce and motivate a new research problem for data streams ? cost-sensitive adaptation. We propose a reference framework for analyzing adaptation strategies in terms of costs and benefits. Our framework allows to characterize and decompose the costs of model updates, and to asses and interpret the gains in performance due to model adaptation for a given learning algorithm on a given prediction task. Our proof-of-concept experiment demonstrates how the framework can aid in analyzing and managing adaptation decisions in the chemical industry.

The relationship between multi-species catch and effort: among fishery comparisons

The control of fishing mortality via fishing effort remains fundamental to most fisheries management strategies even at the local community or co-management level. Decisions to support such strategies require knowledge of the underlying response of the catch to changes in effort. Even under adaptive management strategies, imprecise knowledge of the response is likely to help accelerate the adaptive learning process. Data and institutional capacity requirements to employ multi-species biomass dynamics and age-structured models invariably render their use impractical particularly in less developed regions of the world. Surplus production models fitted to catch and effort data aggregated across all species offer viable alternatives. The current paper seeks models of this type that best describe the multi-species catch–effort responses in floodplain-rivers, lakes and reservoirs and reef-based fisheries based upon among fishery comparisons, building on earlier work. Three alternative surplus production models were fitted to estimates of catch per unit area (CPUA) and fisher density for 258 fisheries in Africa, Asia and South America. In all cases examined, the best or equal best fitting model was the Fox type, explaining up to 90% of the variation in CPUA. For lake and reservoir fisheries in Africa and Asia, the Schaefer and an asymptotic model fitted equally well. The Fox model estimates of fisher density (fishers km−2) at maximum yield (iMY) for floodplain-rivers, African lakes and reservoirs and reef-based fisheries are 13.7 (95% CI [11.8, 16.4]); 27.8 (95% CI [17.5, 66.7]) and 643 (95% CI [459,1075]), respectively and compare well with earlier estimates. Corresponding estimates of maximum yield are also given. The significantly higher value of iMY for reef-based fisheries compared to estimates for rivers and lakes reflects the use of a different measure of fisher density based upon human population size estimates. The models predict that maximum yield is achieved at a higher fishing intensity in Asian lakes compared to those in Africa. This may reflect the common practice in Asia of stocking lakes to augment natural recruitment. Because of the equilibrium assumptions underlying the models, all the estimates of maximum yield and corresponding levels of effort should be treated with caution.

Design, synthesis and computational modelling of aromatic tweezer-molecules as models for chain-folding polymer blends

Novel 'tweezer-type' complexes that exploit the interactions between pi-electron-rich pyrenyl groups and pi-electron deficient diimide units have been designed and synthesised. The component molecules leading to complex formation were accessed readily from commercially available starting materials through short and efficient syntheses. Analysis of the resulting complexes, using the visible charge-transfer band, revealed association constants that increased sequentially from 130 to 11,000 M-1 as increasing numbers of pi-pi-stacking interactions were introduced into the systems. Computational modelling was used to analyse the structures of these complexes, revealing low-energy chain-folded conformations for both components, which readily allow close, multiple pi-pi-stacking and hydrogen bonding to be achieved. In this paper, we give details of our initial studies of these complexes and outline how their behaviour could provide a basis for designing self-healing polymer blends for use in adaptive coating systems. (C) 2008 Elsevier Ltd. All rights reserved.

PASSCLAIM - gut health and immunity

Background The gut and immune system form a complex integrated structure that has evolved to provide effective digestion and defence against ingested toxins and pathogenic bacteria. However, great variation exists in what is considered normal healthy gut and immune function. Thus, whilst it is possible to measure many aspects of digestion and immunity, it is more difficult to interpret the benefits to individuals of variation within what is considered to be a normal range. Nevertheless, it is important to set standards for optimal function for use both by the consumer, industry and those concerned with the public health. The digestive tract is most frequently the object of functional and health claims and a large market already exists for gut-functional foods worldwide. Aim To define normal function of the gut and immune system and describe available methods of measuring it. Results We have defined normal bowel habit and transit time, identified their role as risk factors for disease and how they may be measured. Similarly, we have tried to define what is a healthy gut flora in terms of the dominant genera and their metabolism and listed the many, varied and novel methods for determining these parameters. It has proved less easy to provide boundaries for what constitutes optimal or improved gastric emptying, gut motility, nutrient and water absorption and the function of organs such as the liver, gallbladder and pancreas. The many tests of these functions are described. We have discussed gastrointestinal well being. Sensations arising from the gut can be both pleasant and unpleasant. However, the characteristics of well being are ill defined and merge imperceptibly from acceptable to unacceptable, a state that is subjective. Nevertheless, we feel this is an important area for future work and method development. The immune system is even more difficult to make quantitative judgements about. When it is defective, then clinical problems ensure, but this is an uncommon state. The innate and adaptive immune systems work synergistically together and comprise many cellular and humoral factors. The adaptive system is extremely sophisticated and between the two arms of immunity there is great redundancy, which provides robust defences. New aspects of immune function are discovered regularly. It is not clear whether immune function can be "improved". Measuring aspects of immune function is possible but there is no one test that will define either the status or functional capacity of the immune system. Human studies are often limited by the ability to sample only blood or secretions such as saliva but it should be remembered that only 2% of lymphocytes circulate at any given time, which limits interpretation of data. We recommend assessing the functional capacity of the immune system by: measuring specific cell functions ex vivo, measuring in vivo responses to challenge, e. g. change in antibody in blood or response to antigens, determining the incidence and severity of infection in target populations during naturally occurring episodes or in response to attenuated pathogens.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Sparse density estimation on the multinomial manifold

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators.

Zero attracting recursive least squares algorithms

The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the l1-norm of the parameter vector is approximated by an adaptively weighted l2-norm, in which the weighting factors are set as the inversion of the associated l1-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.