THE STUDENTS’ PERCEPTION TOWARDS USING MOBILE APPLICATION FOR BUSINESS ENGLISH LEARNING

This paper reviews about The Students’ Perception towards Using Mobile Application for Business English Learning that the writer consider, may contribute to the overall effectiveness of the learning process of Business English Learning.  The focus of the study is to know the implementation of Using Mobile Application for Business English Learning. The problem of this research is about “What is student’s perception mobile application for Business English Learning?  In line with focus of research is to know student’s perception toward the mobile application for Business English Learning. It is necessary to describe the weaknesses and strengths in Using Mobile Application for Business English Learning into the mobile application: WhatsApp and Google Classroom.  In this study, the writer used qualitative method, the writer provided questionnaires. The questionnaire was given by the writer to 39 students. They were told by the writer to answer the questions to find out the students perception toward English business learning using Mobile Application.  Based on the findings and discussion, using the mobile application (Google classroom & WhatsApp) in Business English learning online classroom is the best choice due to pandemic COVID-19. In term of students perceptions toward using mobile application the writer concluded that it clearly shows the students have perceptions in English learning during the pandemic COVID-19 while using mobile application (Google classroom & WhatsApp); such as learning English using a mobile application makes it easier to learn and they understand learning using a mobile application, but sometimes students understand better if they learn face-to-face.  In term of the students perceptions toward strengths and weaknesses in using mobile application for English learning, the writer conclude that it clearly shows that the strengths and weaknesses of using mobile applications for learning English, most of the answers of students in the strengths of a mobile app is to simplify the learning process due to the covid-19 pandemic. The weaknesses is that learning will be difficult when the internet connection has problems and slow

Efficiently learning metric and topological maps with autonomous service robots

Models of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., corridors or rooms. This enables the robot to construct annotated metric as well as topological maps of the environment. All techniques have been implemented and thoroughly tested using real mobile robot in a variety of environments

Mobile web and app QoE monitoring for ISPs - from encrypted traffic to speed index through machine learning

International audienceWeb browsing is one of the key applications of the Internet. In this paper, we address the problem of mobile Web and App QoE monitoring from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Our study targets the analysis of Web and App QoE in mobile devices, including mobile browsing in smartphones and tablets, as well as mobile apps. As a proxy to Web QoE, we focus on the analysis of the well-known Speed Index (SI) metric. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI of individual web page and app loading sessions, using as input only packet level data. Empirical evaluations on a large, multi mobile-device corpus of Web and App QoE measurements for top popular websites and selected apps demonstrate that the proposed solution can properly infer the SI from in-network, encrypted-traffic measurements, relying on learning-based models. Our study also reveals relevant network and web page content characteristics impacting Web QoE in mobile devices, providing a complete overview on the mobile Web and App QoE assessment problem

Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase

Mobile Devices for Arabic Learning in Junior High Schools: The Teachers’ Perspective

The present study aims to identify teachers' perceptions of mobile devices use in Arabic learning at junior high schools. Employed a qualitative method with a case study, 24 Arabic language teachers from various junior high schools in the South Tangerang City involved in this study selected by using a random sampling method. Based on the teacher perspectives, the results of the study reveal that the use of mobile devices facilitates students in understanding Arabic lessons and increase their motivation for learning. Time limitations and large classroom sizes were obstacles that decrease teachers’ interest to integrate mobile devices into classroom activities. Reading is by far the main language skill taught by teachers and there is a less focus on productive skills such as speaking and writing. The students have less attention and motivation for learning Arabic while the teachers have attempted to deal with this problem by several measures.The characteristic of Arabic language differs from each context. However, whether for inside or outside classroom activities, active methods are of particular importance for Arabic learning. Arabic teaching and learning practices facilitated by mobile devices require careful planning for a successful implementation lead to more active and effective learning of Arabic

Use of Mobile Learning Strategies and Devices for E-Portfolio Content Creation in an Engineering Thermodynamics and Fluid Mechanics Classes: Student Perceptions

Use of mobile learning strategies and devices for e-portfolio content creation in an engineering Thermodynamics class: Student perceptions Mobile devices can be useful for creating educational content and to help students to learn better (Benedict & Pence, 2012; Tabor & Minch, 2013; Pereira, Echeazarra, Sanz-Santamaria, & Gutierrez, 2014). The purpose of this study is to determine student perceptions on the efficacy of using mobile learning strategies and devices to create electronic content for inclusion in an engineering individual e-portfolio. Students enrolled in an undergraduate 300-level engineering Thermodynamics class, created multimedia videos and produced content demonstrating course content summaries, problem solving techniques, and written work on concept question solutions. A post course completion anonymous survey and focus group meeting was conducted at the end of the semester, to document student perceptions on the efficacy of using mobile learning strategies and devices to create electronic content for inclusion in an engineering individual e-increased interaction with the instructor. Students reported that the act of creating videos portfolio. Results indicated that most students found using mobile devices to create content for verbalizing and demonstrating each step of a problem solution helped review course their e-portfolios increased engagement with content though it did not necessarily lead to materials, to think deeply and to retain information for future use. The e-portfolio was not used as an interaction venue between the students and the instructor and most students found it easier to contact the instructor face-to-face. Several advantages and disadvantages of using mobile devices were noted. Students expressed interest in using mobile devices for learning in future, and suggested changes for ways in which mobile devices can be used in future engineering classes

Mobile game to teach children problem solving and programming logic using game-based learning / Nur Aziemah Tenuri

The important aspect of computer science is computational processes and critical thinking which is essential in our daily life. The purpose of this project is to motivate and attract the attention of children 7 years old and above based on problem solving and logical thinking. Through Game-based learning (GBL) is an activity of game that involves problem solving and challenges which provide learners or players a sense of achievement since it is the way that can attract children by playing a game. This mobile game will use Android as a platform. There are a few techniques or theory had been used to design the mobile game in order to increases children focus in learning programming such as Logical Principles, Computational Thinking Strategy, Game Elements and guidelines of design development. ADDIE model was chosen as a framework in methodology because it based on pedagogical principles of instructional development. The conclusion from finding the usability testing shows that participant can accept the game learning that was implied and satisfaction to the mobile game. In the future, more features such as add English Language option, and develop in computer-based which can be added to make it more attractive and more interesting

Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement

The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.Comment: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, 2013, Pages 793-80