Among the general population, students are especially sensitive to social media and smartphones because of their pervasiveness. Several studies have shown that there is a negative correlation between social media and academic performance since they can lead to behaviors that hurt students' careers, e.g., addictedness. However, these studies either focus on smartphones and social media addictedness or rely on surveys, which only provide approximate estimates. We propose to bridge this gap by i) parametrizing social media usage and academic performance, and ii) combining smartphones and time diaries to keep track of users' activities and their smartphone interaction. We apply our solution on the 72 students participating in the SmartUnitn project, which investigates students' time management and their academic performance. By analyzing the logs of social media apps on students' smartphones and by comparing them to students' credits and grades, we can provide a quantitative and qualitative estimate of negative and positive correlations. Our results show the negative impact of social media usage, distinguishing different influence patterns of social media on academic activities and also underline the need to control the smartphone usage in academic settings.
The usage of mobile devices for mobile learning is becoming increasingly popular. There is a new brand of students in the universities now-a-days who are easily connected to technology and innovative mobile devices. We attempt to do an analysis on a survey done with university students on mobile device usage for mobile learning purposes. This is to find the learning trends within the student community so that some of these popular practices could be encouraged to enhance learning among the student community. Both the quantitative and qualitative approaches are adopted in the analysis. The resu…
The use of the internet, and in particular web browsing, offers many potential advantages for educational institutions as students have access to a wide range of information previously not available. However, there are potential negative effects due to factors such as time-wasting and asocial behaviour. In this study, we conducted an empirical investigation of the academic performance and the web-usage pattern of 2153 undergraduate students. Data from university proxy logs allows us to examine usage patterns and we compared this data to the students' academic performance. The results show that…
The aim of this study is to demostrate that mobile phone usage data can be used to make predictions and find the best classification method for credit scoring even if the dataset is small (2,503 customers). We use different classification algorithms to split customers into paying and non-paying ones using mobile data, and then compare the predicted results with actual results. There are several related works publicly accessible in which mobile data has been used for credit scoring, but they are all based on a large dataset. Small companies are unable to use datasets as large as those used by t…
This study analyzes mobile phone data derived from 10 million daily active users across the United States to better understand the spatio-temporal activity patterns of users in Central Park, New York. The aim of this initial investigation is to create quantifiable measures for understanding public space usage in regions of the city that have no natural data source for measuring activity. We analyze the trip behaviors of users across time and different regions in the park to find patterns of co-location and shared time and, thus, potential social interaction. We find that regions with establish…
Mobile devices have evolved from just communication devices into an indispensable part of people's lives in form of smartphones, tablets and smart watches. Devices are now more personal than ever and carry more information about a person than any other. Extracting user behaviour is rather difficult and time-consuming as most of the work previously has been manual or requires feature extraction. In this paper, a novel approach of user behavior detection is proposed with Deep Learning Network (DNN). Initial approach was to use recurrent neural network (RNN) along with LSTM for completely unsuper…
We demonstrate a novel mobile application called GangaWatch that makes water pollution data usable and accessible focusing on one of the most polluted river basins in the world. It is intended to engage common public who want to see water condition and safe limits, and their relevance based on different purposes. The data is a combination of old data determined from lab tests on physical samples and new data from real time sensors collected from Ganga basin. The platform is open for contribution from others, the data is also available for reuse via public APIs, and it has already been used to …
We report a user study of over four months on the non-voice usage of mobile phones by teens from an underserved urban community in the USA where a community-wide, open-access Wi-Fi network exists. We instrumented the phones to record quantitative information regarding their usage and location in a privacy-respecting manner. We conducted focus group meetings and interviewed participants regularly for qualitative data. We present our findings on what applications our participants used and how their usage changed over time. The findings highlight the challenges to evaluating the usability of mobi…
Facebook is only an electronic communication between human but unfortunately it has become an addiction for all. This paper examines the usage of Facebook among university students and its influence in their academic performance. The impact of Facebook can either be good or bad on university students and in their academic activities. Even though a closer look on the real impact of Facebook reveals that it leads to several problems in university students academic performances. Today Facebook is somehow destroying the future and academic carrier of university students. At the same time also inte…
We describe the results of an industrial-academic collaboration among the Graduate Program in Electrical Engineering (PPGEE), the Electronics and Information Research Centre (CETELI), and Samsung Eletrônica da Amazônia Ltda. (Samsung), which aims at training human resources for Samsung's research and development (R&D) areas. Inspired by co-operative education systems, this collaboration offers an academic experience by means of a complementary training programme (CTP), in order to train undergraduates and graduate students in electrical and computer engineering, with especial emphasis on digit…
Since the beginning of the Saudi Arabian academic year 1435 (Sept 2014), the web-based learning management system Blackboard has been introduced and made available to all instructors and students for all courses at King Abdulaziz University (KAU). The current study takes place to assess the current usages of the Blackboard usages at KAU. The data collected from the 923 students of the foundation year which represent about one-third of the total number of the male students for the academic 2016-2017. Based on statistical evidence gained from the students responses to the survey questions, 78% o…
Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structur…
Mobile applications, mobile apps for short, have proven their usefulness in enhancing service provisioning across a multitude of domains that range from smart healthcare, to mobile commerce, and areas of context sensitive computing. In recent years, a number of empirically grounded, survey-based studies have been conducted to investigate secure development and usage of mHealth apps. However, such studies rely on self reported behaviors documented via interviews or survey questions that lack a practical, i.e. action based approach to monitor and synthesise users actions and behaviors in securit…
Mobile Application Traffic Reveals Multifunctional Use Patterns in Parisian Parks
Telefónica Innovación Digital（Telefónica创新数字）；King’s College London（伦敦国王学院）；The Uehiro Oxford Institute, University of Oxford（牛津大学Uehiro研究所）；University of Nottingham（诺丁汉大学）；Orange Research（Orange研究）
Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user's personality. As filling out personality questionnaires is tedious, we propose the prediction of the user's personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a large…
The aim of this study is to investigate the style of Facebook usage between undergraduate students and the impact on their academics performance. Also, this paper was evaluated in the view of student the using of Facebook. A questioner was design for collecting data from a sample of 480 undergraduate students in Irbid National University. The survey revealed that 77% of the students have an account on Facebook. One of the main findings is that there was a significant relationship between gender and Facebook usage. Moreover, the survey revealed that whenever the less time spent on Facebook, the…
The Web 2.0 has permeated academic life. The use of online information services in post-secondary education has led to dramatic changes in faculty teaching methods as well as in the learning and study behavior of students. At the same time, traditional information media, such as textbooks and printed handouts, still form the basic pillars of teaching and learning. This paper reports the results of a survey about media usage in teaching and learning conducted with Western University students and instructors, highlighting trends in the usage of new and traditional media in higher education by in…
The present study provides the first evidence that illiteracy can be reliably predicted from standard mobile phone logs. By deriving a broad set of mobile phone indicators reflecting users financial, social and mobility patterns we show how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further we show how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. Geo…
Understanding human behavior is an important task and has applications in many domains such as targeted advertisement, health analytics, security, and entertainment, etc. For this purpose, designing a system for activity recognition (AR) is important. However, since every human can have different behaviors, understanding and analyzing common patterns become a challenging task. Since smartphones are easily available to every human being in the modern world, using them to track the human activities becomes possible. In this paper, we extracted different human activities using accelerometer, magn…
Trends in media usage by students can affect the way they learn. Students demand the use of technology, thus institutions and instructors should meet students requests. This paper describes the results of a survey where drivers in the use of media show continuously increasing or decreasing values from the first to the fourth year of study experience at the Western University, Canada, highlighting trends in the usage of new and traditional media in higher education by students. The survey was used to gather data on students media usage habits and user satisfaction from first to fourth year of s…
Exeat is a generic term commonly used to describe a period of absence from a centre of learning either for entire day, or parts of a day for appointments, interviews, open days and other fixtures in privately owned academic environment. The current method of monitoring students movement is inefficient and brings difficulty to the University Halls management checking students exit or entry into the halls of residence as well as impersonation. By using nexus combination of Ubiquitous Mobile Computing Technology through Mobile Short Message Service and biometric fingerprint approach exeat managem…
During the transition from high school to on-campus college life, a student leaves home and starts facing enormous life changes, including meeting new people, more responsibilities, being away from family, and academic challenges. These recent changes lead to an elevation of stress and anxiety, affecting a student's health and wellbeing. With the help of smartphones and their rich collection of sensors, we can continuously monitor various factors that affect students' behavioral patterns, such as communication behaviors associated with their health, wellbeing, and academic success. In this wor…
LICIT-ECO7 UMR_T9401, University of Lyon（里昂大学LICIT-ECO7 UMR_T9401）；ENTPE（法国国家公共工程学院）；University Gustave Eiffel（古斯塔夫·埃菲尔大学）
Socio-economic indicators provide context for assessing a country's overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is critical for social research and government policing. Most data sources available today, such as censuses, have sparse population coverage or are updated infrequently. Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators. This wor…
The relevance of mobile working is steadily increasing. Based on new mobile devices (e.g. smartphones) and their innovative functionalities, an increasing amount of data is being made available ubiquitously. As a result, the growing diffusion of smartphones offers new potential for enterprises. Current mobile devices and related mobile networks have reached a high level of maturity. Thus, the organizational aspects of mobile work have become a focal point of interest for enterprises as well as for academics. This research article addresses the question: How does context influence the choice of…
Online app search optimization (ASO) platforms that provide bulk installs and fake reviews for paying app developers in order to fraudulently boost their search rank in app stores, were shown to employ diverse and complex strategies that successfully evade state-of-the-art detection methods. In this paper we introduce RacketStore, a platform to collect data from Android devices of participating ASO providers and regular users, on their interactions with apps which they install from the Google Play Store. We present measurements from a study of 943 installs of RacketStore on 803 unique devices …
Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good night's sleep is important for job performance. However, both real-world sleep behavior and job performance are hard to measure at scale. In this work, we show that people's everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. We present an observational study in which we objectively tracked the sleep behavior and job performance of salespeople (N = 15) and athletes (N = 19) for 18 mont…
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）；Columbia University（哥伦比亚大学）；University of Oregon（俄勒冈大学）
Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications towards the early detection and intervention of mental health disorders. One promising data source to help monitor human behavior is from daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected attributes (e.g., race, gender). In this paper, we stu…
Use of AI Tools: Guidelines to Maintain Academic Integrity in Computing Colleges
Faculty of computer and information System, Islamic University of Madina, Saudi Arabia（伊斯兰麦地那大学计算机与信息系统学院）；Faculty of Artificial Intelligence Multimedia University, Malaysia（马来西亚多媒体大学人工智能学院）
This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London and Hong Kong. By comparing whole cities typical patterns, we provide insights on how cultural, technological and economical factors shape human dynamics. At a more local scale, we use …
