Linking employees’ e-learning system use to their overall job outcomes: An empirical study based on the IS success model

e-Learning systems are increasingly being adopted by modern enterprises, because of their cost-down effect. However, the extant literature provides little insight into their beneficial consequences. It is expected that a transfer of acquired knowledge, skills and/or work attitudes to jobs will occur through e-learning systems use. This makes e-learning systems a useful tool for improving job outcomes, although this is not well documented. The IS success model, which captures both the human and technological elements of information systems, provides a theoretical basis for linking system use to system task. The current study then examines the relation between e-learning systems use and overall job outcomes based on the model. The data collected from one hundred and ninety-three e-learning system users were analyzed with partial least square (PLS). The results indicate that e-learning systems are perceived as useful and satisfying by employees, and employees’ e-learning systems use is significantly associated with overall job outcomes. In practice, these results provide a basis for establishing a link between an organization’s investment in e-learning and human capital management. They also lend empirical support to the IS success model.     

Change detection in desktop virtual environments: An eye-tracking study

It is common knowledge that attention is important for learning. We need to utilize attention in order to learn something efficiently and effectively. Similarly, we may also need to acquire familiarity with (i.e., learn) our surroundings in order to utilize our attention. In this study, learning is defined as a product of one’s exposure to natural visual stimuli. Using a virtual model of a natural scene, we investigate both attention and its relationship to learning, according to this definition. Specifically, our focus is the effect of environment familiarity on gaze direction. Our findings reveal that the factor of familiarity with one’s surroundings in virtual reality environments exerts a significant influence on peoples’ ability to detect a variety of specific changes that occur within scenes under their observation.     

谈智能科学多媒体辅助教学设计

在多媒体辅助教学中,教学设计直接影响教学效果。认知心理学的发展,尤其是R.E.Mayer提出的多媒体教学理论给我们提供了一个研究教学设计规则的依据。描述同多媒体教学相关的认知心理学的几个基本理论,在此基础上提出一些关于提高多媒体辅助教学效果的教学设计方法。     

略论平面广告的互动设计形式

数字式信息传播在现代人类生活中发挥着越来越大的作用,网络、手机、互动电视等新兴媒体成为人们信息获取的重要来源,这些结合了视、听等多感官刺激的媒体不仅满足了人们在获取信息时的感官多样性、多层次需要,更预示着现代人类对互动的思维方式的渴望。这种渴求也使得是否互动成为当代广告成功的关键。如何将互动这个"动"的概念融合到"静"的平面广告中,成为平面广告研究的重要课题。     

Investigating factors that influence social presence and learning outcomes in distance higher education

There are many factors that influence distance learning especially in higher education where collaborative and communicative discourse is necessary for pursuing knowledge. Social presence, among other factors, is an important concept to be facilitated, developed and sustained in distance higher education as it promotes and supports discourse based learning. This study examines the relationship among demographic and other variables, social presence and learning satisfaction. Results showed demographic variables, such as gender, online learning experience and work status were not significant factors in terms of influencing on either social presence or learning satisfaction. While media integration and instructor’s quality teaching were significant predictors of both social presence and learning satisfaction, interactivity among participants was a predictor of social presence but not of learning satisfaction. Along with the study findings, some implications were discussed for online learning practitioners in higher education setting.     

Effect of high-level content organizers on hypertext learning

This study investigates the cognitive abilities involved in hypertext learning and design approaches that can help users. We examined the effects of two types of high-level content organizers - a graphic spatial map and an alphabetical list - on readers’ memory for hypertext structure. In the control condition, a simple “home” page with no navigational aid was offered. Subjects were asked to read the hypertext with the purpose of learning the content, but in the post test phase they also had to recall the layout of nodes and links. Memory for links and page places varied as a function of condition. When a spatial map was available participants reconstructed more accurate formal structure then in the two other conditions. Participants’ memory about page places was the least accurate in the list condition. Results also indicate that participants use the content organizer when it is available in order to orientate during learning from hypertext documents.Our results prove that a content organizer showing the formal structure can facilitate the spatial mapping process. However, an organizer exposing a different structure than the real one would generate a conflict.     

Stochastic approximation learning for mixtures of multivariate elliptical distributions

Most of the current approaches to mixture modeling consider mixture components from a few families of probability distributions, in particular from the Gaussian family. The reasons of these preferences can be traced to their training algorithms, typically versions of the Expectation-Maximization (EM) method. The re-estimation equations needed by this method become very complex as the mixture components depart from the simplest cases. Here we propose to use a stochastic approximation method for probabilistic mixture learning. Under this method it is straightforward to train mixtures composed by a wide range of mixture components from different families. Hence, it is a flexible alternative for mixture learning. Experimental results are presented to show the probability density and missing value estimation capabilities of our proposal.     

On calibration error of randomized forecasting algorithms

It has been recently shown that calibration with an error less than Δ>0$\Delta >0$ is almost surely guaranteed with a randomized forecasting algorithm, where forecasts are obtained by random rounding the deterministic forecasts up to Δ$\Delta$. We show that this error cannot be improved for a vast majority of sequences: we prove that, using a probabilistic algorithm, we can effectively generate with probability close to one a sequence “resistant” to any randomized rounding forecasting with an error much smaller than Δ$\Delta$. We also reformulate this result by means of a probabilistic game.     

Combining feature spaces for classification

In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix.