Recent advancements in mobile devices and the quick proliferation of information and communication technologies (ICT) have resulted in the generation of increasingly large volumes and varieties of data at an unprecedented pace. According to the Cisco Annual Internet Report, over 70 percent of the global population will have mobile connectivity by 2023. The total number of global mobile subscribers will grow from 5.1 billion (66 percent of the population) in 2018 to 5.7 billion (71 percent of the population) by 2023. Mobile analytics captures data from mobile apps, websites, and web app visitors to identify unique users, track their journeys, record their behavior, and report on the app’s performance. Similar to traditional web analytics, mobile analytics are used to improve conversions, and are the key to crafting world-class mobile experiences. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data.

Mobile big data analytics is currently a high-focus topic aimed at extracting meaningful information and patterns from raw mobile data. Extracting insights or useful knowledge from the big data can be used to build data-driven intelligent systems or models for smart and automated decision-making, where machine learning/deep learning technology is key. Big data have started to demonstrate significant values in higher education. Scalable data processing and analysis enables the development of new insights and valuable information from educational data and has shown promise in higher education to benefit academics, students, and the whole education ecosystem. Machine learning and deep learning approaches solve a broad set of complex problems in mobile big data analytics. They are emerging as solutions for managing large amounts of data, especially for making predictions and providing suggestions based on large data sets.

This Special Issue aims to collect recent developments in methods of mobile big data analytics using machine learning and deep learning approaches. We invite authors from both industry and academia to submit original research and review articles that cover the machine learning and deep learning approaches, models, protocols, and optimization algorithms with the specific focus on mobile big data analytics in education in the topics listed below.

Potential topics include but are not limited to the following:

• Machine learning/deep learning methods for mobile big data analytics
• Machine learning/deep learning methods for educational data mining
• Hybrid intelligent systems for mobile big data analytics
• Clustering and classification methods
• Developments of data collection and algorithmic approaches that can increase the wider use of machine learning/deep learning in mobile big data analytics
• Mathematical modeling for mobile big data analytics
• Decision support systems for mobile big data analytics
• Incremental machine learning approaches for mobile big data analytics
• Recommendation agents and educational resource recommendation