China 
Africa 
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Artificial Intelligence-Based Ubiquitous Smart Learning Educational Environments
Published in P. Kaliraj, T. Devi, Artificial Intelligence Theory, Models, and Applications, 2021
Giannakos et al. (2016) introduce smart learning analytics in video-based learning. The prospects and limitations of smart learning analytics were discussed in detail. The author also presents the research challenges in using smart learning analytics. Analytics in higher education and its benefits are presented in ECAR-Analytics Working Group (2015). The ability to accurately predict future outcomes using learning data-called predictive learning analytics–is of significant strategic value because it empowers stakeholders in the learning process with intelligence on which they can act as means to achieve more desirable outcomes.
Fifteen Years of Recommender Systems Research in Higher Education: Current Trends and Future Direction
Published in Applied Artificial Intelligence, 2023
Vusumuzi Maphosa, Mfowabo Maphosa
The green cluster highlights data mining, educational data mining and matrix factorization, which are used in RS. The blue cluster shows that RS use machine learning and predictive learning techniques to support adaptive and personalized learning. Past behavioral and preference data is mined to generate recommendation information for the target users (Bobadilla et al. 2013; Sharma, Gopalani, and Meena 2017). The red cluster shows how ontologies, semantic webs and digital libraries filter learning objects to recommend learning styles (Yang and Liu 1999). note that CF uses recommendations from other users with similar choices to recommend to the targeted user called a neighbor. Students can be asked to rank their choices (1–5) explicitly, and this feedback can be used to recommend courses and learning materials to other students (Bobadilla et al. 2013). Early warning systems are used to predict future grades and assist through various student engagement programmes to increase retention rates and overall throughput to circumvent the challenge of students failing courses and dropping out (Sweeney et al. 2016).
Simulation of University Teaching Achievement Evaluation Based on Deep Learning and Improved Vector Machine Algorithm
Published in Applied Artificial Intelligence, 2023
In today’s information age, which is also a knowledge economy age, talent should receive a lot of attention because it is one of the most active and crucial factors. Universities should pay more attention to the quality of talent training as a foundation for producing high-caliber talent, providing high-caliber talent for various construction projects in Chinese society, and realizing the high-caliber development of the Chinese economy (Janani and Vijayarani 2019). The talent battle is at the heart of the increasingly ferocious international struggle between nations. Strengthening skills development is necessary if we wish to hold a more favorable position in the international arena (Sun, Cui, and O 2019) Education is necessary for the development of skills, and the caliber of instruction has a significant impact on this process. By the first half of 2014, there were more than 2000 General Colleges and universities (excluding independent colleges) in China, including 400 private general colleges and universities; There are more than 300 adult colleges and universities countrywide, including one private adult college. Also, the number of students and the scale of enrollment in higher education in my country continue to rise. To ensure that the teaching at colleges and universities satisfies the needs of students’ growth, colleges and universities should thoroughly understand the significance of teaching quality, implement efficient techniques to enhance teaching quality, and monitor and assess it (Hansen and Kelley 1973). Most of the research on machine learning algorithms involves predictive learning of data, with the aim of estimating correlations from known data to better predict the future (Costin and Menges 1973).