This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data. The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.

This book constitutes the proceedings of the 14th International Conference on Intelligent Tutoring Systems, IST 2018, held in Montreal, Canada, in June 2018.The 26 full papers and 22 short papers presented in this volume were carefully reviewed and selected from 120 submissions. In the back matter of the volume 20 poster papers and 6 doctoral consortium papers are included. They deal with the use of advanced computer technologies and interdisciplinary research for enabling, supporting and enhancing human learning.

May the Forcing Functions be with You: The Stimulating World of AIED and ITS Research It is my pleasure to write the foreword for Advances in Intelligent Tutoring S- tems. This collection, with contributions from leading researchers in the field of artificial intelligence in education (AIED), constitutes an overview of the many challenging research problems that must be solved in order to build a truly intel- gent tutoring system (ITS). The book not only describes some of the approaches and techniques that have been explored to meet these challenges, but also some of the systems that have actually been built and deployed in this effort. As discussed in the Introduction (Chapter 1), the terms "AIED" and "ITS" are often used int- changeably, and there is a large overlap in the researchers devoted to exploring this common field. In this foreword, I will use the term "AIED" to refer to the - search area, and the term "ITS" to refer to the particular kind of system that AIED researchers build. It has often been said that AIED is "AI-complete" in that to produce a tutoring system as sophisticated and effective as a human tutor requires solving the entire gamut of artificial intelligence research (AI) problems.

This book constitutes the refereed proceedings of the 20 th International Conference on User Modeling, Adaptation, and Personalization, held in Montreal, Canada, in July 2012. The 22 long and 7 short papers of the Research Paper Track presented were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on user engagement; trust; user motivation, attention, and effort; recommender systems (including topics such as matrix factorization, critiquing, noise and spam in recommender systems); user centered design and evaluation; educational data mining; modeling learners; user models in microblogging; and visualization. The Industry Paper Track covered innovative commercial implementations or applications of UMAP technologies, and experience in applying recent research advances in practice. 2 long and 1 short papers were accepted of 5 submissions.

May the Forcing Functions be with You: The Stimulating World of AIED and ITS Research It is my pleasure to write the foreword for Advances in Intelligent Tutoring S- tems. This collection, with contributions from leading researchers in the field of artificial intelligence in education (AIED), constitutes an overview of the many challenging research problems that must be solved in order to build a truly intel- gent tutoring system (ITS). The book not only describes some of the approaches and techniques that have been explored to meet these challenges, but also some of the systems that have actually been built and deployed in this effort. As discussed in the Introduction (Chapter 1), the terms "AIED" and "ITS" are often used int- changeably, and there is a large overlap in the researchers devoted to exploring this common field. In this foreword, I will use the term "AIED" to refer to the - search area, and the term "ITS" to refer to the particular kind of system that AIED researchers build. It has often been said that AIED is "AI-complete" in that to produce a tutoring system as sophisticated and effective as a human tutor requires solving the entire gamut of artificial intelligence research (AI) problems.

The9thInternationalConferenceonIntelligentTutoringSystems(ITS2008)was heldJune 23-27,2008inMontreal. Thisyearwecelebratedthe 20thanniversary ofthe conferencefounded in 1988in Montreal. We havehadbiennial conferences for most of the past 10 years around the world, including in Brazil, Taiwan, France, Canada, and the USA. These ITS conferences provide a forum for the interchange of ideas in all areas of computer science and human learning, a unique environment to exchange ideas and support new developments relevant for the future. The 2008 conference was a symbolic milestone that enabled us to look back at what has been achieved and what is currently being done, in order to face the challenges of tomorrow. Much has changed in the last 20 years in terms of hardware, software, p- grammers, and education stakeholders. Technology is now networked, pervasive, and availableanyplace and anytime. The potential exists to provide customized, ubiquitous guidance andinstruction. However, much has remainedthe same and the need is just as great to model the learner, teaching strategies and domain knowledge. This year we saw an increase in research into student a?ect (mo- vation, boredom, and frustration), speci?cally attempts to detect student a?ect, while feedback studies consideredwhich responses to provide given both student cognition and a?ect. Studies also looked at the impact on learning of positive feedbackandpolitenessinfeedback. Newresearchwasseenindataminingbased on larger studies that use data from real students to diagnose e?ective learning and teaching. So much interest has been generated in this area that the ?rst International Conference on Educational Data Mining was co-located with ITS 200