Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

2014 ◽  
pp. 120-140 ◽  
Author(s):  
Sílvio César Cazella ◽  
Jorge Luiz Victória Barbosa ◽  
Eliseo Berni Reategui ◽  
Patricia Alejandra Behar ◽  
Otavio Costa Acosta
Keyword(s):  
Learning Environment ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Recommender System ◽  
Information Filtering ◽  
Likert Scale ◽  
Learning Objects ◽  
Mobile Environments ◽  
Proposed Model

Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.

Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

2015 ◽  
pp. 2159-2178
Author(s):  
Sílvio César Cazella ◽  
Jorge Luiz Victória Barbosa ◽  
Eliseo Berni Reategui ◽  
Patricia Alejandra Behar ◽  
Otavio Costa Acosta
Keyword(s):  
Learning Environment ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Recommender System ◽  
Information Filtering ◽  
Likert Scale ◽  
Learning Objects ◽  
Mobile Environments ◽  
Proposed Model

Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.

Work-Based Mobile Learning Environments

2012 ◽  
pp. 229-238 ◽  
Author(s):  
Graham Attwell
Keyword(s):  
Learning Environment ◽  
Informal Learning ◽  
Learning Environments ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Cultural Practices ◽  
Cultural Ecology ◽  
Work Process ◽  
Learning At Work ◽  
Mobile Learning Environment

This paper examines the idea of a Work Oriented Mobile Learning Environment (WOMBLE) and considers the potential affordances of mobile devices for supporting developmental and informal learning in the workplace. The authors look at the nature and pedagogy of work-based learning and how technologies are being used in the workplace for informal learning. The paper examines the nature of Work Process Knowledge and how individuals are shaping or appropriating technologies, often developed or designed for different purposes, for social learning at work. The paper goes on to describe three different use cases for a Work Oriented Mobile Learning Environment. The final section of the paper considers how the idea of the WOMBLE can contribute to a socio-cultural ecology for learning, and the interplay of agency, cultural practices, and structures within mobile work-based learning.

Context-Aware Recommender System for Location-Based Advertising

2011 ◽  
Vol 467-469 ◽  
pp. 2091-2096 ◽  
Author(s):  
Hyun Chul Ahn ◽  
Kyoung Jae Kim
Keyword(s):  
Mobile Devices ◽  
Recommender System ◽  
Service Industries ◽  
Classification Rule ◽  
Mobile Service ◽  
Context Aware ◽  
Good Market ◽  
Proposed Model ◽  
Market Opportunities ◽  
Marketing Tool

Demand for context-aware systems continues to grow due to the diffusion of mobile devices. This trend may represent good market opportunities for mobile service industries. Thus, context-aware or location-based advertising (LBA) has been an interesting marketing tool for many companies. However, some studies reported that the performance of context-aware marketing or advertising has been quite disappointing. In this study, we propose a novel context-aware recommender system for LBA. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user’s needs type. In particular, we employ a classification rule to understand user’s needs type using a decision tree algorithm. We empirically validated the effectiveness of the proposed model by using a real-world dataset. Experimental results show that our model makes more accurate and satisfactory advertisements than comparative systems.

scholarly journals A Mobile Learning Model to Improve the Learning Environment in Iraqi Secondary Schools

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 972-981
Author(s):  
Mohammed Hasan Aldulaimi ◽  
Thair A Kadhim ◽  
Israa S Kamil ◽  
Musaddak M. Abdul Zahra
Keyword(s):  
High School Students ◽  
Secondary Schools ◽  
Learning Environment ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Learning Model ◽  
Educational Setting ◽  
School Students ◽  
Positive Learning Environment ◽  
Technology In Schools

Nowadays the use of mobile devices has increased dramatically as they have been integrated into different learning facilities. In this paper, the opinions of high school students and their teachers will be evaluated in order to get a better understanding of how mobile devices are used in the learning environment. A qualitative and quantitative method was used in this study. Multiple cases for the purpose of understanding the level of students' use of these devices in schools. Through the results of this study, it can be determined whether spending on textbooks and supplies is necessary compared to replacing it with technology. This model can be divided into five categories. (MLIS) mobile phone by developing a mobile learning model in Iraqi secondary schools (MLIS). This model can be divided into five categories, including mobile learning, drivers, process, community, and influencing factors. Each of the categories is related to each other, as well as related to planning and goals. However, both students and teachers believe that using mobile devices in an educational setting can help increase overall achievement, improve student motivation, and create a positive learning environment in schools. This study also helps enrich the existing literature on mobile technology in schools, where knowledge is lacking in the Iraqi educational system.

scholarly journals Towards MANET-based Recommender Systems for Open Facilities

2021 ◽  
Author(s):  
Jürgen Dunkel ◽  
Ramón Hermoso
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Mobile Devices ◽  
Recommender System ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Metrics ◽  
Sensor Data ◽  
Mobile Ad Hoc

AbstractNowadays, most recommender systems are based on a centralized architecture, which can cause crucial issues in terms of trust, privacy, dependability, and costs. In this paper, we propose a decentralized and distributed MANET-based (Mobile Ad-hoc NETwork) recommender system for open facilities. The system is based on mobile devices that collect sensor data about users locations to derive implicit ratings that are used for collaborative filtering recommendations. The mechanisms of deriving ratings and propagating them in a MANET network are discussed in detail. Finally, extensive experiments demonstrate the suitability of the approach in terms of different performance metrics.

Enhancing e-Learning Environment with Embedded Recommender Systems

2013 ◽  
pp. 234-253
Author(s):  
Mohamed S. El Sayed ◽  
Mona Nasr ◽  
Torky I. Sultan
Keyword(s):  
Learning Environment ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Learning Objects ◽  
E Learning ◽  
Content Based Filtering

Recommended learning objects are obtained by using a range of recommendation strategies based mainly on content based filtering and collaborative filtering approaches, each applied separately or in combination.

Exploring the Role of Mobile Learning in Global Education

2021 ◽  
pp. 1303-1315
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Collaborative Learning ◽  
Learning Environment ◽  
Blended Learning ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Global Education ◽  
Mobile Technologies ◽  
Easy Access ◽  
Current Trends

This chapter describes the current trends of mobile devices in education, the applications of mobile technologies in learning, the overview of Mobile Learning (m-learning), and the importance of m-learning in global education. M-learning encourages both blended learning and collaborative learning, thus allowing the learners at different locations to get in touch with their peers or others teams to discuss and learn. The m-learning environment is about access to content, peers, experts, portfolio artifacts, credible sources, and previous thinking on relevant topics. Given the convenience of m-learning, there is less time spent getting trained, and the overall costs are lowered as a results. With m-learning, learners are able to learn in their own style at their own pace. M-learning provides easy access to the learning at any place and any time, which is more convenient to the learners.

scholarly journals For Sustainable Application of Mobile Learning: An Extended UTAUT Model to Examine the Effect of Technical Factors on the Usage of Mobile Devices as a Learning Tool

2021 ◽  
Vol 13 (4) ◽  
pp. 1856 ◽  
Author(s):  
Saud S. Alghazi ◽  
Amirrudin Kamsin ◽  
Mohammed Amin Almaiah ◽  
Seng Yue Wong ◽  
Liyana Shuib
Keyword(s):  
Mobile Devices ◽  
Mobile Learning ◽  
Structural Equation ◽  
Memory Device ◽  
Positive Influence ◽  
Decision Makers ◽  
Intention To Use ◽  
Use Of Technology ◽  
Proposed Model ◽  
Network Speed

Mobile devices have become an important tool in higher education. Although mobile devices have several benefits for students, the use of these devices is still very limited among students. This low percentage of usage could be attributed to several reasons, both technical and non-technical. Therefore, there is a need to conduct more research in order to understand the necessary factors that would lead to enhanced student usage, thus achieving sustainable mobile learning. In order to achieve that, our study proposes a model by employing the unified theory of acceptance and use of technology (UTAUT) to investigate the necessary factors that influence intention to use mobile learning among university students. To evaluate the proposed model, structural equation modelling (SEM) was employed to analyze data collected from 612 students. The results indicated that factors, such as device connectivity, device compatibility, device memory, device performance, network coverage, and network speed have a significant and positive influence on students’ intention to use mobile learning. This research provides important recommendations for university decision makers and developers on understanding the necessary factors for adopting mobile learning and reflect the students’ requirements.

scholarly journals The effect of visualisation on user experience in recommender systems

2021 ◽  
Vol 26 (3) ◽  
Author(s):  
Wen-Yau Liang ◽  
Chun-Che Huang ◽  
Tzu-Liang Tseng ◽  
Zih-Yan Wang ◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
User Satisfaction ◽  
Business Environment ◽  
Perceived Usefulness ◽  
Continuance Intention ◽  
Systems Research ◽  
Proposed Model

Introduction. Measuring user experience, though natural in a business environment, is often challenging for recommender systems research. How recommender systems can substantially improve consumers’ decision making is well understood; but the influence of specific design attributes of the recommender system interface on decision making and other outcome measures is far less understood. Method. This study provides the first empirical test of post-acceptance model adaption for information system continuance in the context of recommender systems. Based on the proposed model, two presentation types (with or without using tag cloud) are compared. An experimental design is used and a questionnaire is developed to analyse the data. Analysis. Data were analysed using SPSS and SmartPLS (partial least squares path modeling method). Statistical methods used for the questionnaire on user satisfaction were a reliability analysis, a validity analysis and T-tests. Results. The results demonstrate that the proposed model is supported and that the visual recommender system can indeed significantly enhance user satisfaction and continuance intention. Conclusions. In order to improve the satisfaction or continuance intention of users, it is required to improve the perceived usefulness, effectiveness and visual attractiveness of a recommender system.

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