Getting the Information You Need, When You Need It: A Context-aware Q&amp;A System for Collaborative Learning

Diaconița, Irina; Rensing, Christoph; Tittel, Stephan

doi:10.1007/978-3-319-11200-8_34

Cited by 3 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding to Diaconita et al [155], they focus on how professionals keeping up with the rapid development in their fields, particularly when the new information is meant to be used for problem solving. Thus, they assert collaborative approaches (e.g., forums and question and answering) only partly mitigate this problem in cases where time constraints and user mobility are of no crucial concern.…”

Section: Toolmentioning

confidence: 99%

A Revision of the Literature Concerned with Mobile, Ubiquitous, and Pervasive Learning: A Survey

Peña-Ayala

Cárdenas

2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

This chapter tailors a perspective of the work fulfilled in three learning research lines, which besides holding many common attributes also tend to converge to shape mobile, ubiquitous, and pervasive sceneries. Such a junction pursues to spread the traditional classroom and distance settings to open environments, as well as use the surrounding physical and digital objects as learning content that is available to learners at anytime, anywhere, and in any way. In sum, a complete learning environment is recreated to provide formal and informal learning to support academic studies, professional training, and lifelong learning. Thus, in this chapter a description of the mobile, ubiquitous, and pervasive learning (MUP-Learning) arena is presented through the selection of a sample of recent and transcendent works that offer from a conceptual contribution, such as models and frameworks, even empirical approaches oriented to specific domains of study. The sample of works is characterized according to a proposed pattern, as well as organized according to a suggested taxonomy. A profile to describe each work is also stated and a series of statistics are presented, as well as an analysis of the arena is provided to understand the potential and challenges related to the MUP-Learning field.

show abstract

Section: Toolmentioning

confidence: 99%

A Revision of the Literature Concerned with Mobile, Ubiquitous, and Pervasive Learning: A Survey

Peña-Ayala

Cárdenas

2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

“…For example, by predicting user purchasing behavior, businesses can develop targeted marketing strategies for different customer segments, boosting sales and profits. Moreover, AI technologies can help merchants optimize customer service and feedback, such as automated Q&A systems [3] and sentiment analysis [4], enhancing customer satisfaction and service quality. Therefore, the application of AI in e-commerce and the study of user behavior are of great significance, providing more accurate and efficient services and recommendations for ecommerce businesses while also promoting the rapid development of the e-commerce industry.…”

Section: Introductionmentioning

confidence: 99%

Analysis of promotional online shopping behavior based on machine learning

Huang

2023

HSET

View full text Add to dashboard Cite

Artificial intelligence's widespread use in e-commerce enables precise, personalized services and recommendations through deep data analysis, enhancing user experience and loyalty. Studying user behavior is essential for AI applications, providing businesses with valuable insights into customers' needs and behaviors, ultimately boosting sales efficiency and market competitiveness. This paper aims to investigate the application of artificial intelligence in the e-commerce domain, focusing on user behavior analysis and purchase behavior prediction. Using a user behavior dataset provided by the Tianchi platform, machine learning models, particularly random forest and logistic regression models, are constructed to predict user behavior in purchasing specific product categories. The results demonstrate that the random forest-based machine learning model can predict user purchase behavior effectively. Furthermore, the paper provides statistical data on user behavior and introduces related techniques such as feature engineering, performance metrics, and algorithms. The study highlights that artificial intelligence can offer personalized recommendations and services by analyzing users' historical behavior, interests, and preferences, optimizing merchants' product and service strategies, and ultimately enhancing sales efficiency and user satisfaction.

show abstract

A Context Aware Learning Application for Communities of Service Technicians

Rensing

2016

2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)

View full text Add to dashboard Cite

Getting the Information You Need, When You Need It: A Context-aware Q&A System for Collaborative Learning

Cited by 3 publications

References 7 publications

A Revision of the Literature Concerned with Mobile, Ubiquitous, and Pervasive Learning: A Survey

A Revision of the Literature Concerned with Mobile, Ubiquitous, and Pervasive Learning: A Survey

Analysis of promotional online shopping behavior based on machine learning

A Context Aware Learning Application for Communities of Service Technicians

Contact Info

Product

Resources

About