Effects of training peer tutors in content knowledge versus tutoring skills on giving feedback to help tutees’ complex tasks

This study aims to investigate the effectiveness of training tutors in content knowledge of a particular domain versus training them in tutoring skills of pedagogical knowledge when tutoring on a complex tutee task. Forty-seven tutor-tutee pairs of fourth year secondary school students were created and assigned to one of two treatments. Twenty-two tutors received training in content knowledge and the other twenty-five tutors in tutoring skills. Tutors formulated written feedback immediately after the training. Tutees first interpreted the tutor feedback and then used it to revise their research questions. The results showed that tutors trained in tutoring skills formulated more effective feedback than tutors trained in content knowledge. In addition, tutees helped by tutoring-skills tutors found the feedback more motivating than those helped by content- knowledge tutors. However, no differences were found in tutee performance on revision. The findings are discussed in terms of the set-up of this study and implications for improving the effectiveness of peer tutoring.

Towards Cloud-Based Knowledge Capturing Based on Natural Language Processing

Paper presented at the Cloud Forward Conference 2015, October 6th-8th, Pisa

Peer Support to Facilitate Knowledge Sharing on Complex Tasks

This thesis explores how to design a peer support system to facilitate self-organized knowledge sharing in non-formal learning environments, in particular when learners work on complex tasks. The peer support system aims to replace two teacher-led didactic arrangements: selecting a tutor at the initial stage, and guidance during the interaction process (Dillenbourg, 1999; Topping, 1996). Such a system has previously been developed by Van Rosmalen (2008) and De Bakker (2010) and has been tentatively used to facilitate knowledge sharing on content-related questions. In this thesis, we would like to find out how to further improve the design of this peer support system, especially to facilitate knowledge sharing on complex tasks. Since little pedagogical theory is available to inform the design of our peer support system, this thesis attempts to apply cognitive load theory (Sweller, Van Merriënboer, & Paas, 1998; Van Merriënboer & Sweller, 2005) that informs instructional designs in classroom settings to the design of our peer support system in Learning Networks.

Combining Taxonomies using Word2vec

Taxonomies have gained a broad usage in a variety of fields due to their extensibility, as well as their use for classification and knowledge organization. Of particular interest is the digital document management domain in which their hierarchical structure can be effectively employed in order to organize documents into content-specific categories. Common or standard taxonomies (e.g., the ACM Computing Classification System) contain concepts that are too general for conceptualizing specific knowledge domains. In this paper we introduce a novel automated approach that combines sub-trees from general taxonomies with specialized seed taxonomies by using specific Natural Language Processing techniques. We provide an extensible and generalizable model for combining taxonomies in the practical context of two very large European research projects. Because the manual combination of taxonomies by domain experts is a highly time consuming task, our model measures the semantic relatedness between concept labels in CBOW or skip-gram Word2vec vector spaces. A preliminary quantitative evaluation of the resulting taxonomies is performed after applying a greedy algorithm with incremental thresholds used for matching and combining topic labels.