I scratch and sense but can I program? An investigation of learning with a block based programming language

This paper reports an investigation into undergraduate student experiences and views of a visual or ‘blocks’ based programming language and its environment. An additional and central aspect of this enquiry is to substantiate the perceived degree of transferability of programming skills learnt within the visual environment to a typical mainstream textual language. Undergraduate students were given programming activities and examples covering four basic programming concepts based on the Sense programming language which is intended to simplify programming. Sense programming statements are represented by blocks which only fit together in ways that produce a meaningful syntactic outcome, which may lower the cognitive barrier to learning. Students were also presented with concepts represented using an equivalent textual construct and asked to consider their understanding of these based on the graphical cases. They were finally asked to complete a short online survey. This paper presents the programming activities, the survey and an analysis of the results

Using device detection techniques in M-learning scenarios

Recent studies of mobile Web trends show the continued explosion of mobile-friend content. However, the wide number and heterogeneity of mobile devices poses several challenges for Web programmers, who want automatic delivery of context and adaptation of the content to mobile devices. Hence, the device detection phase assumes an important role in this process. In this chapter, the authors compare the most used approaches for mobile device detection. Based on this study, they present an architecture for detecting and delivering uniform m-Learning content to students in a Higher School. The authors focus mainly on the XML device capabilities repository and on the REST API Web Service for dealing with device data. In the former, the authors detail the respective capabilities schema and present a new caching approach. In the latter, they present an extension of the current API for dealing with it. Finally, the authors validate their approach by presenting the overall data and statistics collected through the Google Analytics service, in order to better understand the adherence to the mobile Web interface, its evolution over time, and the main weaknesses

A mobile context-aware framework for managing learning schedules : data analysis from an interview study

Mobile learning applications can be categorized into four generations: non-adaptive, learning-preferences based adaptive, learning-contexts-based adaptive and learning-contexts-aware adaptive. The research on our learning schedule framework is motivated by some of the challenges within the context-aware mobile learning field. These include being able to create and enhance students’ learning opportunities in different locations by considering different learning contexts and using them as the basis for selecting appropriate learning materials. We have adopted a pedagogical approach for evaluating this framework, an exploratory interview study with potential users consisting of 37 university students. The observed interview feedback gives us insights into the use of a pedagogical m-learning suggestion framework deploying a learning schedule subject to the five proposed learning contexts. Our data analysis is described and interpreted leading to a personalized suggestion mechanism for each learner and each scenario and a proposed taxonomy for describing mobile learner preferences