Shuangyan Liu

Students' Perceptions of the Factors Leading to Unsuccessful Group Collaboration

A nationwide sample of university students completed a survey that asked questions about their perceptions of the factors that can cause problems which exist with online or general group work. Data were obtained from 173 students at more than 18 different universities in the United Kingdom. Three main problems that exist in group collaboration are identified through an extensive review of literature and addressed as problem scenarios in the survey. These include: poor motivation, lack of individual accountability and negative interdependence. Findings from the survey include that on average more than five factors are acknowledged by the students for each subcategory of problem, and for each scenario the factors that affect the group work are ranked by importance level. Furthermore, we find no statistically significant association between the students’ backgrounds and their perceptions of the factors identified.

iGLS: Intelligent Grouping for Online Collaborative Learning

One of the factors that affect successful collabora-tive learning is the composition of collaborative groups. Due to the lack of intelligent grouping according to learners’ pedagogic needs in current online collaborative learning environments, developing intelligent grouping according to individual learners’ cognitive characteristics is highly desired. In this paper, we propose a new approach to supporting intelligent grouping based on learners’ learning styles. Our approach achieves the balance of different levels of learning styles in group composition. We demonstrate how it can fit into current activity-based collaborative learning environments and how it could be applied in a real world application.

Measuring Accuracy of Triples in Knowledge Graphs

An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples.