Prof. Philippe Fournier-Viger

Shenzhen University, China

Brief: Philippe Fournier-Viger (Ph.D) is distinguished professor at Shenzhen University (China). Five years after completing his Ph.D., he came to China in 2015 and became full professor after receiving an important talent title from the National Science Foundation of China. He has published more than 400 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent systems and applications, which have received more than 15,000 citations (H-Index 63 - Google Scholar). He is the founder of the popular SPMF data mining library, offering more than 250 algorithms to find patterns in data, cited in more than 1,000 research papers. He is former associate editor-in-chief of the Applied Intelligence journal and has been keynote speaker for over 30 international conferences and co-edited four books for Springer. He appears in the top 2% of researchers for scientific influence in the Stanford list, and is a Elsevier «Highly Cited Chinese Researcher» (2022). Website: http://www.philippe-fournier-viger.com. 

Title: Algorithms for the discovery of interesting patterns in data: recent work and appliations to education

Abstract: Intelligent systems and tools can play an important role in various domains such as for factory automation, e-business, transportation, and e-learning. The foundation of these advancements lies in the availability of high-quality data, encompassing diverse forms such as activity logs, multimedia content, and sensor-derived metrics from interactive educational environments. Managing data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems and in particular educational systems.

The talk will first briefly review early study on designing algorithms for identifying frequent patterns. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data such as graphs and sequences. Topics that will be discussed include high utility patterns, locally interesting patterns, periodic patterns and spatial patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with conventional artificial intelligence techniques to enhance the efficacy of educational intelligent systems.

Prof. Wenbing Zhao

Cleveland State University, USA

Brief: Dr. Zhao is a Professor at the Department of Electrical and Computer Engineering, Cleveland State University. He got his BS and MS degrees from the Physics Department in Peking University. He earned his Ph.D. at University of California, Santa Barbara in 2002. He has over 250 peer-reviewed publications and the author of the research monograph titled “From Traditional Fault Tolerance to Blockchain.” Dr. Zhao’s research spans from dependable distributed systems, human centered smart systems, and engineering education. His research has been funded by the US NSF, US Department of Energy, US Department of Education, US Department of Transportation, Ohio Bureau of Workers’ Compensation, Ohio Department of Higher Education, the Ohio Development Services Agency, and Woodruff Foundation. He has delivered more than 10 keynotes, tutorials, public talks and demonstrations in various conferences, industry and academic venues. Dr. Zhao is an associate editor for IEEE Access, Human-Centric Computing and Information Sciences, MDPI Computers, and PeerJ Computer Science, and a member of the editorial board of several international journals, including Sensors, Applied System Innovation, Internal Journal of Parallel, Emergent and Distributed Systems. He is currently an IEEE Senior Member and serves as the Treasurer of the IEEE Cleveland Section.

Title: Sabermetrics and Beyond: Data Science in Professional Baseball

Abstract: In this talk, I will describe a personalized learning environment facilitated by a next-generation TEL platform, NxTEL, which is a full-stack interactive application platform that runs on mobile phones and tablets as iOS or Android native apps, and on computers via Web browsers. NxTEL incorporates artificial intelligence (AI) algorithms to guide an individual student for self- paced learning and assessment, as well as to facilitate computer- supported collaborative learning (CSCL) between a group of students. Furthermore, NxTEL encourages students to create curriculum content to the platform. This will not only enrich the content of the platform, but also more importantly, cultivate stronger student ownership of the knowledge. Finally, NxTEL incorporates gamification elements and social networking elements to further engage students. As a case study, we elaborate how to use the proposed NxTEL platform in a computer networks course. More specifically, first, we develop a multi-dimensional knowledge map for the Computer Network curriculum. Second, based on the knowledge map, we develop AI-driven modules for self-paced learning and assessment. Third, we develop AI-driven modules for computer-supported collaborative learning. Fourth, we develop a theoretical framework towards the conceptual understanding of motivation and self-efficacy for NxTEL-powered education in computer science. 

Assoc. Fonny Dameaty Hutagalung

Department of Educational Psychology, Faculty of Education, Universiti Malaya, Malaysia

Brief:Dr. Fonny Dameaty Hutagalung's research interest is related to early childhood education such as digital literacy among preschooler children, digital media and technology in ECE, 21st Century Education in ECE, etc. She is currently a program coordinator in the Master of Early Childhood Education. She was an Editor Guest, at Advanced Science Letter Journal and an editor Guest, at Education and Information Technology (Springer). Her name is very well-known in CRC Balkema Taylor & Francis Publisher as Editor Books of Economics, Social Sciences and Informational Management (2015), The Role of Service in Tourism & Hospitality Industry (2015), Social Sciences and Interdisciplinary Behaviour (2016) and Knowledge, Service, Tourism & Hospitality (2016), Managing Service, Education, and Knowledge Management in the Knowledge Economic Era (2017), Social Interactions and Networking in Cyber Society (Springer) (2017), Trends and Issues in Interdisciplinary Behaviour and Social Sciences (2017, 2018), Early Childhood Education in 21st century (2019), and The Social Science Empowered (2020). More than 100 articles published in ISI Journal, Scopus index, Proceeding, and reputation journals.

Title: AI and Psychology: Perspective among Psychologists 

Abstract:  In psychology practice, artificial intelligence (Al) chatbots can make therapymore accessible and less expensive. Al tools can also improve interventionsautomate administrative tasks, and aid in training new clinicians. On theresearch side, synthetic intelligence is offering new ways to understandhuman intelligence, while machine learning allows researchers to gleaninsights from massive quantities of data. Meanwhile, educators are exploringways to leverage ChatGPT in the classroom. As algorithms and chatbotsflood the system, a few crucial questions have emerged. ls Al safe to use?Is it ethical? What protections could help ensure privacy, transparency, andequity as these tools are increasingly used across society? Manyperspective among psychologist about Al as a tools to help clients.