Abstract
This Doctoral Consortium paper focuses on Extreme Adaptivity, a set of top level requirements for adaptive hypertext systems, which has resulted from one year of examining the adaptive hypertext landscape. The complete specification of a system, KnowledgeAtoms, is also given, mainly as an example of Extreme Adaptivity. Additional methodological elements are discussed.

Abstract
In this paper we describe a platform that enables Web site automation and monitoring. The platform automatically gathers high quality site activity data, both from the server and client sides. Web adapters, such as rec-ommender systems, can be easily plugged into the platform, and take advantage of the up-to-date activity data. The platform also includes a module to support the editor of the site to monitor and assess the effects of automation. We illustrate the features of the platform on a case study, where we show how it can be used to gather information not only to model the behavior of users but also the impact of the personalization mechanism. Copyright © 2008, Association for the Advancement of Artificial Intelligence.

Abstract
Over the years, several systematic literature reviews have been published reporting advances in tools and techniques for automated assessment in Computer Science. However, there is not yet a major bibliometric study that examines the relationships and influence of publications, authors, and journals to make these research trends visible. This paper presents a bibliometric study of automated assessment of programming exercises, including a descriptive analysis using various bibliometric measures and data visualizations. The data was collected from the Web of Science Core Collection. The obtained results allow us to identify the most influential authors and their affiliations, monitor the evolution of publications and citations, establish relationships between emerging themes in publications, discover research trends, and more. This paper provides a deeper knowledge of the literature and facilitates future researchers to start in this field.

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Abstract
Learning to program requires diligent practice and creates room for discovery, trial and error, debugging, and concept mapping. Learners must walk this long road themselves, supported by appropriate and timely feedback. Providing such feedback in programming exercises is not a humanly feasible task. Therefore, the early and steadily growing interest of computer science educators in the automated assessment of programming exercises is not surprising. The automated assessment of programming assignments has been an active area of research for over a century, and interest in it continues to grow as it adapts to new developments in computer science and the resulting changes in educational requirements. It is therefore of paramount importance to understand the work that has been performed, who has performed it, its evolution over time, the relationships between publications, its hot topics, and open problems, among others. This paper presents a bibliometric study of the field, with a particular focus on the issue of automatic feedback generation, using literature data from the Web of Science Core Collection. It includes a descriptive analysis using various bibliometric measures and data visualizations on authors, affiliations, citations, and topics. In addition, we performed a complementary analysis focusing only on the subset of publications on the specific topic of automatic feedback generation. The results are highlighted and discussed.