Context in Mobile learning: the point of view of the learners

Context-awareness is becoming a crucial component in the mobile learning systems due to the dynamic changing of the Mobile learning environment, a Context-aware mobile learning system senses mobile environment and reacts to changing context during the learning process. Some efforts have been made in the area of Context-aware Mobile learning systems in order to propose a user model, most of them are focusing on the technological context such as the network performances, mobile devices capabilities, others are focusing on the learners’ style and preferences, and no one tried to understand the learners’ needs. However, no one tried to study the Learning context from the point of view of the learners. For this purpose, we created a questionnaire, in which we tried to understand which learning contexts are important to the learners in the learning process, and we use it to understand their needs and preferences, to inform the design of a new Context aware Mobile Learning Approach

Challenges in context-aware mobile language learning: the MASELTOV approach

Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment

A Broad Learning Approach for Context-Aware Mobile Application Recommendation

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for \textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor \textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate user's preference, app category information and multi-view features to facilitate the performance of app rating prediction. The multidimensional structure is employed to capture the hidden relationships between multiple app categories with multi-view features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions within multiple categories and features. Furthermore, we employ a group $\ell_{1}-$norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world mobile app datasets demonstrate the effectiveness of the proposed method

Understanding Angle and Angle Measure: A Design-Based Research Study Using Context Aware Ubiquitous Learning

Mobile technologies are quickly becoming tools found in the educational environment. The researchers in this study use a form of mobile learning to support students in learning about angle concepts. Design-based research is used in this study to develop an empirically-substantiated local instruction theory about students\u27 develop of angle and angle measure. This local instruction theory involves real-world connections and mobile technologies through a sub category of mobile learning called context-aware ubiquitous learning. Through a process of anticipation, enactment, evaluation, and revision, the local instruction theory was developed to include a theoretical contribution of how students come to understand angle and angle measure using context-aware ubiquitous. A set of instructional activities was also developed as an embodiment of that theory. The findings from clinical interviews indicate that context-aware ubiquitous learning is a valuable mathematical context for introducing students to angle and angle measure

Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.Comment: 18 pages, 10 figure

Integrating Mobile Web 2.0 within tertiary education

Based on three years of innovative pedagogical development and guided by a participatory action research methodology, this paper outlines an approach to integrating mobile web 2.0 within a tertiary education course, based on a social constructivist pedagogy. The goal is to facilitate a student-centred, collaborative, flexible, context-bridging learning environment that empowers students as content producers and learning context generators, guided by lecturers who effectively model the use of the technology. We illustrate how the introduction of mobile web 2.0 has disrupted the underlying pedagogy of the course from a traditional Attelier model (face-to-face apprenticeship model), and has been successfully transformed into a context independent social constructivist model. Two mobile web 2.0 learning scenarios are outlined, including; a sustainable house design project (involving the collaboration of four departments in three faculties and three diverse groups of students), and the implementation of a weekly ‘nomadic studio session'. Students and lecturers use the latest generation of smartphones to collaborate, communicate, capture and share critical and reflective learning events. Students and lecturers use mobile friendly web 2.0 tools to create this environment, including: blogs, social networks, location aware (geotagged) image and video sharing, instant messaging, microblogging etc… Feedback from students and lecturers has been extremely positive