A systematic literature review on the development and use of mobile learning (web) apps by early adopters

Surveys in mobile learning developed so far have analysed in a global way the effects on the usage of mobile devices by means of general apps or apps already developed. However, more and more teachers are developing their own apps to address issues not covered by existing m-learning apps. In this article, by means of a systematic literature review that covers 62 publications placed in the hype of teacher-created m-learning apps (between 2012 and 2017, the early adopters) and the usage of 71 apps, we have analysed the use of specific m-learning apps. Our results show that apps have been used both out of the classroom to develop autonomous learning or field trips, and in the classroom, mainly, for collaborative activities. The experiences analysed only develop low level outcomes and the results obtained are positive improving learning, learning performance, and attitude. As a conclusion of this study is that the results obtained with specific developed apps are quite similar to previous general surveys and that the development of long-term experiences are required to determine the real effect of instructional designs based on mobile devices. These designs should also be oriented to evaluate high level skills and take advantage of mobile features of mobile devices to develop learning activities that be made anytime at anyplace and taking into account context and realistic situations. Furthermore, it is considered relevant the study of the role of educational mobile development frameworks in facilitating teachers the development of m-learning apps

Database Optimization Aspects for Information Retrieval

There is a growing need for systems that can process queries, combining both structured data and text. One way to provide such functionality is to integrate information retrieval (IR) techniques in a database management system (DBMS). However, both IR and database research have been separate research fields for decades, resulting in different - even conflicting - approaches to data management. Each DBMS has a component called a "query optimizer", which plays a crucial role in the efficiency and flexibility of the system. So, for successful integration the IR techniques and data structures, as well as the DBMS query optimizer, should be adapted to enable mutual cooperation. The author concentrates on top-N queries - a common class of IR queries. An IR top-N query asks for the N best documents given a set of keywords. The author proposes processing the data in batches as a compromise between IR and DBMS query processing. Experiments with this technique show that porting IR optimization techniques is (still) not a promising option due to the additional administrative overhead. Two new mathematical models are introduced to eliminate this overhead: a model that predicts selectivity, which is a crucial factor in the execution costs, and a model that predicts the quality of the top-N