Using thematic ontologies for user- and group- based adaptive personalization in web searching

This paper presents Prospector, an adaptive meta-search layer, which performs personalized re-ordering of search results. Prospector combines elements from two approaches to adaptive search support: (a) collaborative web searching; and, (b) personalized searching using semantic metadata. The paper focuses on the way semantic metadata and the users’ search behavior are utilized for user- and group- modeling, as well as on how these models are used to re-rank results returned for individual queries. The paper also outlines past evaluation activities related to Prospector, and discusses potential applications of the approach for the adaptive retrieval of multimedia documents

Query independent measures of annotation and annotator impact

The modern-day web-user plays a far more active role in the creation of content for the web as a whole. In this paper we present Annoby, a free-text annotation system built to give users a more interactive experience of the events of the Rugby World Cup 2007. Annotations can be used for query-independent ranking of both the annotations and the original recorded video footage (or documents) which has been annotated, based on the social interactions of a community of users. We present two algorithms, AuthorRank and MessageRank, designed to take advantage of these interactions so as to provide a means of ranking documents by their social impact

Architecture of Engagement: Autonomy-Supportive Leadership for Instructional Improvement

This multiple paper dissertation addresses the importance of improving student success in online higher education programs by providing support for instructors. The autonomy-supportive structures to improve instructional practice are explained through three main domains, including instructional development, instructional design, and instructional practice. The first paper addresses instructional leadership with the theoretical foundations and practical considerations necessary for instructional leaders. Recommendations are made to use microcredentials or digital badges to scaffold programming using self-determination theory. The second paper addresses the importance of instructional design in improving instructional practice including the intentionality involved in implementing a gamification strategy to improve online student motivation. The third paper addresses instructional practice with a mixed-method sequential explanatory case study. Using the community of inquiry framework, this paper explains intentional course design, course facilitation, and student perceptions of the digital powerups strategy. The conclusion considers implications for practice and the need for instructional leaders to scaffold an architecture of engagement to support instructors and improve student success

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Group recommendation with automatic detection and classification of groups

This PhD thesis presents ART (Automatic Recommendation Technologies), a set of group recommendation algorithms that detect groups of users with similar preferences. With respect to classic group recommendation, the first step that such systems have to compute is the detection of groups of people with similar preferences, in order to respect the constraint on the number of recommendations that can be produced and maximize users’ satisfaction

Group recommendation with automatic detection and classification of groups

This PhD thesis presents ART (Automatic Recommendation Technologies), a set of group recommendation algorithms that detect groups of users with similar preferences. With respect to classic group recommendation, the first step that such systems have to compute is the detection of groups of people with similar preferences, in order to respect the constraint on the number of recommendations that can be produced and maximize users’ satisfaction