Data Science and Artificial Intelligence in Education and Educational Research

Guest Editors
Prof. Tzu-Hua Wang, Ph.D.
Department of Education and Learning Technology,
National Tsing Hua University, TAIWAN (R.O.C.)
E-mail: tzuhuawang@mx.nthu.edu.tw
Web: https://www.researchgate.net/profile/Tzu-Hua_Wang

Assoc. Prof. Marian Simko, Ph.D.
Faculty of Informatics and Information Technologies,
Slovak University of Technology in Bratislava, Slovakia
Web:http://www2.fiit.stuba.sk/~simko/
E-mail: marian.simko@stuba.sk

Assoc. Prof. Ahcene Bounceur, Ph.D.
Computer Science and Operations Research,
University of Brest, France
Web: http://pagesperso.univ-brest.fr/~bounceur/
E-mail: Ahcene.Bounceur@univ-brest.fr

Call for Papers
With the development of information technology, it becomes feasible to widely and thoroughly apply information technology to education, whether for educational researches or practices. For example, information technology can be applied in the fields including curriculum, instruction and assessment and education management and leadership. That in turn makes it possible to store much digital data in various kinds of educational digital systems. The amount and coverage of the digital data is even wider and deeper than traditional paper-based educational data. In the trend of digital learning, Massive Open Online Courses (MOOCs) and Small Private Online Courses (SPOCs) become new ways of learning. MOOCs and SPOCs are able to record much digital data of learner’s learning behaviors in digital learning environment. In recent years, flipped classroom receives increasing attention in all learning stages. It emphasizes that digital learning should be part of instructional activities and expects that students can learn through the video-based online materials and take digital assessment at home before they start learning in the classroom. Based on student’s performance in digital learning, teachers arrange high-level learning activities in the classroom. This kind of blended learning makes it possible to collect the digital data of student’s learning behaviors outside the classroom. This means that instruction and educational research coverage can be largely expanded to student’s daily life outside the classroom. Student’s learning data collected will be richer than before. In recent years, more and more emphasis is put on institutional research (IR). It becomes a new research field after various school materials, including data about professor’s academic researches and educational practice development and student’s growth and learning data, are digitalized into database. Hopefully, by analyzing the information, positive and effective strategies can be developed in enhancing college recruitment mechanism, academic researches, social influences and student’s learning effectiveness. Moreover, with the prevalence of using smart mobile devices for mobile learning, digital data of learning behaviors is more quickly accumulated. In the future, it is highly possible that most of student’s learning data is this kind of digital learning behavior data, which means that educators and education researchers should analyze the digital data and develop proper models of data analysis and application.

Data science is a research topic receiving increasing attention in recent years. It is an interdisciplinary science, including statistics, mathematics, programming, problem solving, data collection and handling, and etc. It covers any issues about data cleansing, preparation, and analysis in unstructured and structured data. In short, data science includes all kinds of technologies which can be used to explore new knowledge and information in various data. Technologies of big data and data mining and their application belong to data science. In addition, along with the development of machine learning and deep learning technologies, application and development of artificial intelligence in various fields is facilitated. Artificial intelligence mainly focuses on completely automating the process of predicting problems and providing solutions. Therefore, a large amount of data is required to perform machine learning and deep learning. This process also covers technologies related to big data and data mining.

Considering that data science is seldom applied in education and there is a lot of educational data which can be thoroughly analyzed to find out valuable information for educational researches and educational practices, which is beneficial to educational management and enhancing education quality, this special issue calls for studies related to data science application in education. It covers application of big data, data mining and artificial intelligence. Researchers can focus on specific education-related objectives and specific types of education data to develop data analysis technologies. They can also explain findings of specific education big data analysis or develop prediction models for specific education-related objectives and develop artificial intelligence application in education. Based on the above, this call for papers emphasizes application of data science and artificial intelligence in education and educational researches. Potential topics include, but are not limited to:
1. Application of data science in institutional research (IR)
2. Application of data science in Massive Open Online Courses(MOOCs) and Small Private Online Courses (SPOCs)
3. Application of data science in digital learning
4. Application of data science in predicting and evaluating student’s learning effectiveness and learning behavior
5. Innovative application of data science in educational researches
6. Future trend of data science application in education
7. Application of artificial intelligence in teaching and learning
8. Application of artificial intelligence in solving educational problems
9. Innovative application of artificial intelligence in educational researches
10. Future trend of artificial intelligence application in education
11. Application of Internet of Things (IoT) in Education
12. Application of artificial intelligence algorithms in IoT applications

Manuscript Due November 30, 2018
First Round of Reviews February 28, 2019
Publication Date May 19, 2019

Authors should submit their manuscripts using Editorial System at https://www.editorialsystem.com/ejmste and please kindly select the manuscript type: SI - Data Science and Artificial Intelligence in Education and Educational Research