University of Saskatchewan

Assistant Professor

research, teaching

University of Saskatchewan

Associate Professor

research, teaching, administration

University of Saskatchewan

Research Associate

research

University of Saskatchewan

NSERC Cameco Chair for Women in Science and Engineering (Prairies)

NSERC’s Chairs for Women in Science and Engineering Program began in 1996. Its goal is to increase the participation of women in science and engineering, and to provide role models for young women in these fields. The five Chairs are regionally based in the Atlantic, Quebec, Ontario, Prairie, and British Columbia/Yukon regions. NSERC funding must be matched by contributions from corporate sponsors.

Institute of Mathemacis, Bulgarian Academy of Sciences

Researcher

research

Federal Armed Forces University Munich

Research Associate

research

Department of Computer Science, University of Saskatchewan, Canada

Professor

graduate and undergraduate teaching,
research,
administration

Towards Social Learning Environments

IEEE Transactions on Learning Technologies, 1 (4), 199-214.

We are teaching a new generation of students, cradled in technologies, communication and abundance of information. The implications are that we need to focus the design of learning technologies to support social learning in context. Instead of designing technologies that ldquoteachrdquo the learner, the new social learning technologies will perform three main roles: 1) support the learner in finding the right content (right for the context, for the particular learner, for the specific purpose of the learner, right pedagogically); 2) support learners to connect with the right people (again right for the context, learner, purpose, educational goal etc.), and 3) motivate/incentivize people to learn. In the pursuit of such environments, new areas of sciences become relevant as a source of methods and techniques: social psychology, economic/game theory, multi-agent systems. The paper illustrates how social learning technologies can be designed using some existing and emerging technologies: ontologies vs. social tagging, exploratory search, collaborative vs. self-managed social recommendations, trust and reputation mechanisms, mechanism design and social visualization

Towards Social Learning Environments

IEEE Transactions on Learning Technologies, 1 (4), 199-214.

We are teaching a new generation of students, cradled in technologies, communication and abundance of information. The implications are that we need to focus the design of learning technologies to support social learning in context. Instead of designing technologies that ldquoteachrdquo the learner, the new social learning technologies will perform three main roles: 1) support the learner in finding the right content (right for the context, for the particular learner, for the specific purpose of the learner, right pedagogically); 2) support learners to connect with the right people (again right for the context, learner, purpose, educational goal etc.), and 3) motivate/incentivize people to learn. In the pursuit of such environments, new areas of sciences become relevant as a source of methods and techniques: social psychology, economic/game theory, multi-agent systems. The paper illustrates how social learning technologies can be designed using some existing and emerging technologies: ontologies vs. social tagging, exploratory search, collaborative vs. self-managed social recommendations, trust and reputation mechanisms, mechanism design and social visualization

SocConnect: Intelligent Social Networks Aggregator

In Proceedings of the International Conference on User Modeling, Adaptation and Personalization, 2010

Towards Social Learning Environments

IEEE Transactions on Learning Technologies, 1 (4), 199-214.

We are teaching a new generation of students, cradled in technologies, communication and abundance of information. The implications are that we need to focus the design of learning technologies to support social learning in context. Instead of designing technologies that ldquoteachrdquo the learner, the new social learning technologies will perform three main roles: 1) support the learner in finding the right content (right for the context, for the particular learner, for the specific purpose of the learner, right pedagogically); 2) support learners to connect with the right people (again right for the context, learner, purpose, educational goal etc.), and 3) motivate/incentivize people to learn. In the pursuit of such environments, new areas of sciences become relevant as a source of methods and techniques: social psychology, economic/game theory, multi-agent systems. The paper illustrates how social learning technologies can be designed using some existing and emerging technologies: ontologies vs. social tagging, exploratory search, collaborative vs. self-managed social recommendations, trust and reputation mechanisms, mechanism design and social visualization

SocConnect: Intelligent Social Networks Aggregator

In Proceedings of the International Conference on User Modeling, Adaptation and Personalization, 2010

Emphasize, don't filter! Displaying recommendations in Twitter timelines

RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

This paper describes and evaluates a method for presenting recommendations that will increase the efficiency of the social activity stream while preserving the users' accurate awareness of the activity within their own social networks. With the help of a content-based recommender system, the application displays the user's home timeline in Twitter as three visually distinct tiers by emphasizing more strongly those Tweets predicted to be more interesting. Pilot study participants reported that they were able to read the interesting Tweets while ignoring the others with relative ease and that the recommender accurately categorized their Tweets into three tiers.

Towards Social Learning Environments

IEEE Transactions on Learning Technologies, 1 (4), 199-214.

We are teaching a new generation of students, cradled in technologies, communication and abundance of information. The implications are that we need to focus the design of learning technologies to support social learning in context. Instead of designing technologies that ldquoteachrdquo the learner, the new social learning technologies will perform three main roles: 1) support the learner in finding the right content (right for the context, for the particular learner, for the specific purpose of the learner, right pedagogically); 2) support learners to connect with the right people (again right for the context, learner, purpose, educational goal etc.), and 3) motivate/incentivize people to learn. In the pursuit of such environments, new areas of sciences become relevant as a source of methods and techniques: social psychology, economic/game theory, multi-agent systems. The paper illustrates how social learning technologies can be designed using some existing and emerging technologies: ontologies vs. social tagging, exploratory search, collaborative vs. self-managed social recommendations, trust and reputation mechanisms, mechanism design and social visualization

SocConnect: Intelligent Social Networks Aggregator

In Proceedings of the International Conference on User Modeling, Adaptation and Personalization, 2010

Emphasize, don't filter! Displaying recommendations in Twitter timelines

RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

This paper describes and evaluates a method for presenting recommendations that will increase the efficiency of the social activity stream while preserving the users' accurate awareness of the activity within their own social networks. With the help of a content-based recommender system, the application displays the user's home timeline in Twitter as three visually distinct tiers by emphasizing more strongly those Tweets predicted to be more interesting. Pilot study participants reported that they were able to read the interesting Tweets while ignoring the others with relative ease and that the recommender accurately categorized their Tweets into three tiers.

Motivating Participation in Social Computing Applications: A User Modeling Perspective

Journal of User Modeling and User Adapted Interaction, Springer Verlag

The explosive growth of Web-based social applications over the last 10 years has led people to engage in online communities for various purposes: to work, learn, play, share time and mementos with friends and family and engage in public action. Social Computing Applications (SCA) allow users to discuss various topics in online forums, share their thoughts in blogs, share photos, videos, bookmarks, and connect with friends through social networks. Yet, the design of successful social applications that attract and sustain active contribution by their users still remains more of an art than a science. My research over the last 10 years has been based on the hypothesis that it is possible to incorporate mechanisms and tools in the design of the social application that can motivate users to participate, and more generally, to change their behavior in a desirable way, which is beneficial for the community. Since different people are motivated by different things, it can be expected that personalizing the incentives and the way the rewards are presented to the individual, would be beneficial. Also since communities have different needs in different phases of their existence, it is necessary to model the changing needs of communities and adapt the incentive mechanisms accordingly, to attract the kind of contributions that are beneficial. Therefore User and Group (Community) Modeling is an important area in the design of incentive mechanisms. This paper presents an overview of different approaches to motivate users to participate. These approaches are based on various theories from the area of social psychology and behavioral economics and involve rewards mechanisms, reputation, open group user modeling, and social visualization. Future trends are outlined towards convergence with the areas of persuasive systems design, adaptive/personalized systems, and intelligent social learning environments.

Towards Social Learning Environments

IEEE Transactions on Learning Technologies, 1 (4), 199-214.

We are teaching a new generation of students, cradled in technologies, communication and abundance of information. The implications are that we need to focus the design of learning technologies to support social learning in context. Instead of designing technologies that ldquoteachrdquo the learner, the new social learning technologies will perform three main roles: 1) support the learner in finding the right content (right for the context, for the particular learner, for the specific purpose of the learner, right pedagogically); 2) support learners to connect with the right people (again right for the context, learner, purpose, educational goal etc.), and 3) motivate/incentivize people to learn. In the pursuit of such environments, new areas of sciences become relevant as a source of methods and techniques: social psychology, economic/game theory, multi-agent systems. The paper illustrates how social learning technologies can be designed using some existing and emerging technologies: ontologies vs. social tagging, exploratory search, collaborative vs. self-managed social recommendations, trust and reputation mechanisms, mechanism design and social visualization

SocConnect: Intelligent Social Networks Aggregator

In Proceedings of the International Conference on User Modeling, Adaptation and Personalization, 2010

Emphasize, don't filter! Displaying recommendations in Twitter timelines

RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

This paper describes and evaluates a method for presenting recommendations that will increase the efficiency of the social activity stream while preserving the users' accurate awareness of the activity within their own social networks. With the help of a content-based recommender system, the application displays the user's home timeline in Twitter as three visually distinct tiers by emphasizing more strongly those Tweets predicted to be more interesting. Pilot study participants reported that they were able to read the interesting Tweets while ignoring the others with relative ease and that the recommender accurately categorized their Tweets into three tiers.

Motivating Participation in Social Computing Applications: A User Modeling Perspective

Journal of User Modeling and User Adapted Interaction, Springer Verlag

The explosive growth of Web-based social applications over the last 10 years has led people to engage in online communities for various purposes: to work, learn, play, share time and mementos with friends and family and engage in public action. Social Computing Applications (SCA) allow users to discuss various topics in online forums, share their thoughts in blogs, share photos, videos, bookmarks, and connect with friends through social networks. Yet, the design of successful social applications that attract and sustain active contribution by their users still remains more of an art than a science. My research over the last 10 years has been based on the hypothesis that it is possible to incorporate mechanisms and tools in the design of the social application that can motivate users to participate, and more generally, to change their behavior in a desirable way, which is beneficial for the community. Since different people are motivated by different things, it can be expected that personalizing the incentives and the way the rewards are presented to the individual, would be beneficial. Also since communities have different needs in different phases of their existence, it is necessary to model the changing needs of communities and adapt the incentive mechanisms accordingly, to attract the kind of contributions that are beneficial. Therefore User and Group (Community) Modeling is an important area in the design of incentive mechanisms. This paper presents an overview of different approaches to motivate users to participate. These approaches are based on various theories from the area of social psychology and behavioral economics and involve rewards mechanisms, reputation, open group user modeling, and social visualization. Future trends are outlined towards convergence with the areas of persuasive systems design, adaptive/personalized systems, and intelligent social learning environments.

Understanding and Controlling the Filter Bubble through Interactive Visualization: A User Study

25th ACM Conference on Hypertext and Social Media, 1– 4 Sep 2014, Santiago, Chile. Paper Accepted.

“The filter bubble” is a term popularized by Eli Pariser which refers to people getting encapsulated in streams of data such as news or social network updates that are personalized to their interests. While people need protection from information overload and maybe prefer to see content they feel familiar with and viewpoint that they agree with, there is the danger that important issues that should be of concern for everyone will get filtered away and people will live in “echo-chambers”, blissfully unaware of reality, and exposure to different views. We have proposed a design of an interactive visualization, which provides the user of a social networking site with awareness of the personalization mechanism (the semantics and the source of the content that is filtered away), and with means to control the filtering mechanism. The visualization has been implemented in a peer-to-peer social network and we present here the results of a qualitative and a quantitative evaluation. The quantitative study with 163 participants demonstrates that the visualization leads to increased users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing.