Educo - A Collaborative Learning Environment Based on Social Navigation

Lecture Notes in Computer Science

2012

Abstract.A large number of educational resources is now available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produces at least two problems: how to help students find the most appropriate resources, and how to engage them into using these resources and benefiting from them. Personalized and social learning have been suggested as potential methods for addressing these problems. Our work presented in this paper attempts to combine the ideas of personalized and social learning. We introduce Progressor + , an innovative Webbased interface that helps students find the most relevant resources in a large collection of self-assessment questions and programming examples. We also present the results of a classroom study of the Progressor + in an undergraduate class. The data revealed the motivational impact of the personalized social guidance provided by the system in the target context. The interface encouraged students to explore more educational resources and motivated them to do some work ahead of the course schedule. The increase in diversity of explored content resulted in improving students' problem solving success. A deeper analysis of the social guidance mechanism revealed that it is based on the leading behavior of the strong students, who discovered the most relevant resources and created trails for weaker students to follow. The study results also demonstrate that students were more engaged with the system: they spent more time in working with self-assessment questions and annotated examples, attempted more questions, and achieved higher success rates in answering them.

Section: Social Navigation and Visualization For E-learningmentioning

confidence: 99%

Motivational Social Visualizations for Personalized E-Learning

Hsiao

Lecture Notes in Computer Science

2012

“…Most relevant to our project are CoFIND [6] and Educo [7]. CoFIND is a self-organized learning environment that organizes online resources through the counting of votes cast by learners.…”

Section: Previous Workmentioning

confidence: 99%

Social Navigation Support Through Annotation-Based Group Modeling

Farzan

2005

User Modeling 2005

Abstract.Closed corpus AH systems demonstrate what is possible to achieve with adaptive hypermedia technologies. However, they are impractical for dealing with the large volume of open corpus resources. Our Knowledge Sea project explores social navigation support, an approach for providing open corpus personalized guidance that is based on past learners' interaction with the system. The most recent stage of our project focuses on using annotations for social navigation support. We present here Knowledge Sea II, which implements annotation-based social navigation support, and report the results of several classroom studies, which have evaluated this technology.

“…Social navigation assists users browsing through Web resources by applying "community wisdom" distilled from the actions of earlier users. This navigation support most frequently comes in the form of visual cues indicating, for example, which of the available links were picked by the majority of similar users [22], or which pages were being explored by other users at the moment [14].…”

Section: Introductionmentioning

confidence: 99%

Social Navigation Support for Information Seeking: If You Build It, Will They Come?

Farzan

Lecture Notes in Computer Science

2009

Abstract. Navigating through the ever-changing information space is becoming increasingly difficult. Social navigation support is a technique for guiding users to interesting and relevant information by leveraging the browsing behavior of past users. Effect of social navigation support on users' information seeking behavior has been studied mostly from conceptual basis or under natural experiments. In the current work, we have designed and conducted a controlled experiment to investigate the effect of social navigation support through a multifaceted method. This paper reports on the design of the study and the result of log data, subjective evaluation, and eye movement data analysis.