Training children safe behavior in traffic situations is both important and challenging. One of the problems is children’s limited perceptual-motor abilities and associated difficulties with important cognitive skills required to be safe pedestrians. Existing traffic education programs focus more on theoretical knowledge, while training practical skills in the real world is dangerous, expensive and hard to organize. This paper presents a promising alternative – an intelligent virtual reality training environment that allows children to practice their pedestrian skills. It describes the interface and architecture of the system, as well as the skill model of the pedestrian safety domain. The results of the conducted pilot study show that children of the target age group rarely have problems with applying (and acquiring) “basic” pedestrian skills in the developed virtual environment. However, when applying and learning “advanced” skills, they require additional support.

The last years have seen an increased usage of apps, applications that are downloaded from a centralized repository and run in a particular environment. However, smaller app environments face the major challenge of attracting sufficient awareness and usage by third-party developers and users to become sustainable. This is a typical chicken-and-egg problem. Without apps, few people will be using the environment. Few users lead to limited incentive for app developers. We analyze work done in a very similar domain, namely learning object repositories (LORs), to learn about factors that inhibit participation in app environments. We also present an approach to overcome these problems, which aims at lowering the barriers of participation for app production as much as possible. The framework was partly implemented and is being applied at a higher education institution, where teachers and teaching assistants created over a hundred of apps.

A large amount of educational content is available as lecture videos, which record teachers as they proceed through a course. Students watch these videos in different ways. They rewind, skip forward, watch some scenes repeatedly. This work investigates what can be learned by analyzing such viewing patterns. We show how to use machine learning techniques to analyze such data, and present the outcomes of an analysis of data collected from the interactions of 2992 students in 253 courses. The viewing pattern were put into relation to seven different variables, such as the final score of the student and the rating teachers received from students Our analysis shows that some variables, such as the teacher rating, were indeed predictable from the viewing patterns.

This paper presents an approach to quickly transform online resources into widgets. This approach has several advantages over previous solutions. Two small-scale evaluations indicate that this solution empowers the average teachers to become a widget author, without any programming knowledge. The teachers showed their enthusiasm to use this tool in the near future and believe that this authoring tool will help them both during the authoring and teaching process. There are still many features required to be added to improve our tool. For example, the automatic generation of metadata might improve usability and ease of use even more, especially when adding category information to the metadata to ease later reuse for existing widgets.

Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, theresulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to capture the local dependencies among hidden units. This regularization not only encourages hidden units of many groups to be inactive given observed data but also makes hidden units within a group compete with each other for modeling observed data. Thus, the l1/l2 regularization on RBMs yields sparsity at both the group and the hidden unit levels. We call RBMs trained with the regularizer sparse group RBMs (SGRBMs). The proposed SGRBMs are appliedto model patches of natural images, handwritten digits and OCR English letters. Then to emphasize that SGRBMs can learn more discriminative features we applied SGRBMs to pretrain deep networks for classification tasks. Furthermore, we illustrate the regularizer can also be applied to deep Boltzmann machines, which lead to sparse group deep Boltzmann machines. When adapted to the MNIST data set, a two-layer sparse group Boltzmann machine achieves an error rate of 0.84%, which is, to our knowledge, the best published result on the permutation-invariant version of the MNIST task.

This paper summarises the analyses of participant interaction within the Twitter microblogging environment. The study employs longitudinal probabilistic social network analysis (SNA) techniques to identify the patterns and trends of network dynamics. It explores the associations of student achievement records with the observed network. The results indicate tendencies towards: [i] reciprocal interaction, and [ii] adoption of a selective approach in communication over time, implying that students tend to communicate with fewer peers over time. The evaluations that examine achievement score attributes indicate [iii] network homogeneity and popularity effects associated to achievement scores - suggesting greater interaction among students of similar levels and more attention to higher achieving students.

In China, the number of online learners who attend formal education has quadrupled in the last 5 years to 8.2 millions until the end of 2008. How can online teachers build and update web-courses for such a large numbers of online students - courses that ideally respect the different requirements of the students? In this paper we describe an automatic course generation System (ACGS) developed at the Shanghai Jiao Tong University (SJTU). In this ACGS, teachers can build web-based courses according to their own instructional plan and publish the web-based courses without requiring technological support. Teachers can also update the web-courses whenever the instructional objective changes. The web-course learning environment generated from this system takes into account pedagogical scenarios. The ease of use of our system is illustrated by its quickly increasing number of users. About 50 teachers in the SJTU School of Continuing Education (SOCE-SJTU) have developed 45 web-courses in only two months. These 45 web-courses have been provided to about 5800 online learners in the last 2009 autumn semester alone.

The term is used to describe applications that distinguish themselves from previous generations of software by a number of principles. Existing work shows that Web 2.0 applications can be successfully exploited for technology-enhance learning. However, in-depth analyses of the relationship between Web 2.0 technology on the one hand and teaching and learning on the other hand are still rare. In this article, we will analyze the technological principles of the Web 2.0 and describe their pedagogical implications on learning. We will furthermore show that Web 2.0 is not only well suited for learning but also for research on learning: the wealth of services that is available and their openness regarding API and data allow to assemble prototypes of technology-supported learning applications in amazingly small amount of time. These prototypes can be used to evaluate research hypotheses quickly. We will present two example prototypes and discuss the lessons we learned from building and using these prototypes.

1We propose a mediator architecture that allows a learning system to retrieve learning objects (LOs) from heterogeneous repositories of LOs and not exclusively from its proprietary repository. A mediating component accepts queries formulated in a uniform query language, translates them into repository specific queries and passes them to each connected repository. For the translation of queries, an ontology-based queryrewriting method has been developed which uses the specified knowledge representation of the repository and an ontology mapping to compute the rewriting steps for translating. For distributing queries we realized a mediating architecture, which passes the translated queries to the repositories. This architecture has been integrated in the Web-based, user-adaptive and interactive e-learning environment Active Math.

Means of achieving personalization in the Web are adaptive hypermedia techniques and the flexible composition of learning content from individual learning objects. If the objects are represented in a format different from the presentation format (e.g. as XML as opposed to HTML), a potentially expensive transformation process is required. Furthermore, caching becomes impractical depending on the degree of personalization, as the dynamic information is available at presentation time only and varies for each user. In this paper we present a flexible and efficient presentation pipeline based on a model-view-controller architecture, which overcomes performance problems and couples caching and personalization. It transforms single learning objects and uses a view-layer for adaptive presentation and navigation support that provides additional advantages such as incremental rendering and the easy adaption to different layout styles. Both theoretical and simulation results prove the efficiency of this architecture.