Artificial Intelligence in Online Higher Educational Data Mining

Dear Colleagues,

We are inviting submissions to the Special Issue on “Artificial Intelligence in Online Higher Educational Data Mining”.

Online higher educational data have become increasingly important since the COVID-19 began, affecting the many systems in our daily life, and education systems in particular. Educational data mining using artificial intelligence methods has become an increasingly important topic of interest in the research community. Moreover, the amount of higher educational data generated and recorded has been rapidly growing, and extracting meaningful patterns and information from large educational data has become critical to improve the quality of online education. Artificial intelligence frameworks play a key role in resolving complex problems for online higher education. Artificial intelligence methods employ one or more techniques such as neural networks, probabilistic learning methods, deep learning, and evolutionary algorithms. In addition, a hybrid model which is an integration of multiple learning techniques can be developed and employed to resolve a problem. This Special Issue offers an opportunity for researchers to contribute on both theoretical and application aspects of artificial intelligence in online educational data mining.

The purpose of this Special Issue is to investigate and examine theory and application of artificial intelligence methods for online educational data mining applications. We invite researchers to contribute their original research and review papers that will motivate and support ongoing research on the application of artificial intelligence frameworks to solve online higher educational data mining problems. Topics of interest include but are not limited to:

• Identification of student behavioral patterns
• Early identification of at-risk students
• Intelligent online learning systems
• Prediction of student learning outcomes
• Cognitive models of learning
• Student support and online learning recommendations
• Modelling and developing students’ online education systems
• Self-adaptive learning
• Hybrid learning systems
• Deep learning frameworks and applications in online education systems
• Evolutionary methods and applications in online education systems
• Computer vision-based intelligent systems for online learning systems
• Automatic assessment
• Smart classes
• Interpretable and explainable smart educational systems

Dr. Hüseyin Kusetogullari
Prof. Dr. Chih-Hsiung Tu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.