Fedor Bakalov

Progressor: social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students.

An approach to controlling user models and personalization effects in recommender systems

Personalization nowadays is a commodity in a broad spectrum of computer systems. Examples range from online shops recommending products identified based on the users previous purchases to web search engines sorting search hits based on the user browsing history. The aim of such adaptive behavior is to help users to find relevant content easier and faster. However, there are a number of negative aspects of this behavior. Adaptive systems have been criticized for violating the usability principles of direct manipulation systems, namely controllability, predictability, transparency, and unobtrusiveness. In this paper, we propose an approach to controlling adaptive behavior in recommender systems. It allows users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. We present this approach using an example of a personalized portal for biochemical literature, whose users are biochemists, biologists and genomicists. Also, we report on a user study evaluating the impacts of controllable personalization on the usefulness, usability, user satisfaction, transparency, and trustworthiness of personalized systems.

Open social student modeling: visualizing student models with parallel introspectiveviews

This paper explores a social extension of open student modeling that we call open social student modeling. We present a specific implementation of this approach that uses parallel IntrospectiveViews to visualize models representing student progress with QuizJET parameterized self-assessment questions for Java programming. The interface allows visualizing not only the students own model, but also displaying parallel views on the models of their peers and the cumulative model of the entire class or group. The system was evaluated in a semester-long classroom study. While the use of the system was non-mandatory, the parallel IntrospectiveViews interface caused an increase in all of the usage parameters in comparison to a regular portal-based access, which allowed the student to achieve a higher success rate in answering the questions. The collected data offer some evidence that a combination of traditional personalized guidance with social guidance was more effective than personalized guidance alone.

IntrospectiveViews: an interface for scrutinizing semantic user models

User models are a key component for user-adaptive systems They represent information about users such as interests, expertise, goals, traits, etc This information is used to achieve various adaptation effects, e.g., recommending relevant documents or products To ensure acceptance by users, these models need to be scrutable, i.e., users must be able to view and alter them to understand and if necessary correct the assumptions the system makes about the user However, in most existing systems, this goal is not met In this paper, we introduce IntrospectiveViews, an interface that enables the user to view and edit her user model Furthermore, we present the results of a formative evaluation that show the importance users give in general to different aspects of scrutable user models and also substantiate our claim that IntrospectiveViews is an appropriate realization of an interface to such models.

Ontology-Based Multidimensional Personalization Modeling for the Automatic Generation of Mashups in Next-Generation Portals

Recently, the combination of portal and mashup technology has gained some attention. Portals were originally designed as single points of access to the information and applications distributed across the enterprise. However, due to the increasing number of resources available through portals, they have gained a new challenging goal: To provide users with the information tailored to their individual needs and geared to the situation they are working in. Mashups, the tools that dynamically integrate independent applications, seem to be a good technique to achieve this goal. What is needed, however, are means to automatically create personalized mashups that optimally fit a users information needs in a given situation. In this paper, we describe our approach to this automatic mashup generation. At the core of our approach are different ontology-based models that describe the user, the domain, possible information needs in this domain, and personalization rules determining which information to present to which user in which situation.

Natural Language Processing for Semantic Assistance in Web Portals

Web portals are a major class of web-based content management systems. They can provide users with a single point of access to a multitude of content sources and applications. However, further analysis of content brokered through a portal is not supported by current portal systems, leaving it to their users to deal with information overload. We present the first work examining the integration of natural language processing into web portals to provide users with semantic assistance in analyzing and interpreting content. This integration is based on the portal standard JSR286 and open source NLP frameworks. Two application scenarios, news analysis and biocuration, highlight the feasibility and usefulness of our approach.

An Evolutionary Algorithm for Automatic Composition of Information-gathering Web Services in Mashups

The idea behind mashups is to provide a mechanism that allows for more or less spontaneous combination of existing web applications. Users shall thus be enabled to combine data and services according to their needs.However, existing mashup frameworks require some programming knowledge, hence are not suitable for non-expert users. In this paper, we present a system that builds on existing Semantic Web research to achieve an automatic,ad-hoc generation of mashups thus eliminating the need for programmer involvement. At the core of our approach, there is an evolutionary algorithm that automatically composes different information web services based on semantic service descriptions. The information that has been retrieved from the invoked web services is automatically transformed into a semantic representation and presented as a mashup to the users of the system.

Personalized recommendation of related content based on automatic metadata extraction

In order to efficiently use information, users often need access to additional background information. This additional information might be stored at various places, such as news websites, company directories, geographic information systems, etc. Oftentimes, in order to access these different pieces of information, the user has to launch new browser windows and direct them to appropriate resources. In our todays Web 2.0, the problem of accessing background information becomes even more prominent: Due to the large number of different users contributing, Web 2.0 sites grow quickly and, most often, in a more uncoordinated way regarding, e.g., structure and vocabulary used, than centrally controlled sites. In such an environment, finding relevant information can become a tedious task. In this paper, we propose a framework allowing for automated, user-specific annotation of content in order to enable provisioning of related information. Making use of unstructured data analysis services like UIMA or Calais, we are able to identify certain types of entities like locations, persons, etc. These entities are wrapped into semantic tags that contain machine-readable information about the entity type. The entity types are associated with applications able to provide background information or related content. A location, e.g., could be associated with Google Maps, whereas a person could be associated with the companys employee directory. However, it strongly depends on the individual users interests and experience which additional information he deems relevant. We therefore tailor the information provided based on the User Model, which reflects the users interests and expertise. This allows providing the user with in-place, in-context background information on those entities he is likely to be interested in as well as with recommendations to related content for those entities. It also relieves users from the tedious task of manually collecting relevant additional information. Our main concepts have been prototypically embedded within IBMs WebSphere Portal.

Automating Mashups for Next-Generation Enterprise Portals

The increasing number of resources available through portals establish a need to tailor information to individual needs and situations. Mashups are tools for dynamically integrating independent applications. For portals, what is needed are means to automatically create personalized mashups that optimally fit a users information needs in a given situation. At the core of our approach are different ontology-based models that describe the user, the domain, possible information needs in this domain, and personalization rules determining which information to present to which user in which situation.

Personalized semantic assistance for the curation of biochemical literature

The number of scientific publications available in multiple repositories is huge and rapidly growing. Accessing this information is of critical importance to conducting research and designing experiments. However, retrieving data of particular interest for a specific research field in such a large volume of publications is often like looking for a needle in a haystack. We present a web platform that supports researchers in navigating and curating biochemical literature. Our platform provides a single-point of access to abstracts of publications harvested from multiple databases and supports further analysis of these abstracts. It also allows users to obtain a personalized view of the literature and its semantic analysis results.