Introduction

ACM UMAP is an annual conference on user modeling, adaptation and personalization. User modeling concerns the process of understanding the user’s needs, preferences, interests, knowledge and other aspects. This is achieved by reasoning about and extracting knowledge from user data, which includes both data that is explicitly provided by the user—such as profile data—and implicitly gathered usage data—such as browsing data. Adaptation and personalization techniques exploit the user models in order to better tailor a software system, such as a website, to the user needs. Recommender systems are the best known type of personalized systems, but the field is much wider and includes among others personalized search, adaptive user interfaces, personalized advice, and personalized technology-enhanced learning. This special issue contains extended versions of selected papers from UMAP 2017, the 25th edition of the conference series. The conference was hosted in Bratislava, Slovakia, from 9 to 12 July 2017. The conference consisted of five tracks that represent the variety of disciplines and application areas in which user modeling, adaptation and personalization play a role. User interface aspects, including adaptive presentation and navigation, were covered by the tracks Intelligent User Interfaces and Adaptive Hypermedia. As one of the most visible and largest application area of personalization is the Social Web, we received in the corresponding track submissions that both analyzed user behavior to function as input for personalization, as well as the effect of personalization on user behavior. Being the most prominent and most applied adaptive technique, Recommender Systems were given a dedicated track as well. Finally, we dedicated a track to the field of Technology-Enhanced Adaptive Learning, as this is an application area with important and tangible impacts on society. The papers in this special issue belong to the latter two areas. Three papers are situated in the field of Technology-Enhanced Learning. The first paper, “Analysis and Design of Mastery Learning Criteria” (Pelánek and Řihák), shows that, under the assumption of isolated skills, the decision over skill mastery, and whether a system should let the student move on to the next skill to learn, can rest on a simple exponential moving average rather than on the more sophisticated Bayesian and logistic approaches to learner modeling. They also show that the choice of an appropriate mastery threshold and of the source of information is more influential than the choice of the learner modeling technique. The second paper focuses on open learner models, which is an approach for making a student’s learner model explicit to the student, in order to enhance reflection, self-awareness and self-regulation of the learning process. In “Navigation Support in Complex Learner Models: Assessing Visual Design Alternatives” (Guerra, Schunn, Bull, BarríaPineda and Brusilovsky), six alternative prototypes were investigated in two control studies. The results provide several insights in how to balance between ease of use and complexity during the design of such open learner models, and open several lines of future research.