%0 Journal Article %1 ilkou2023collabgraph %A Ilkou, Eleni %A Tolmachova, Tetiana %A Fisichella, Marco %A Taibi, Davide %D 2023 %I IEEE %J IEEE Transactions on Learning Technologies %K %P 1-18 %T CollabGraph: A graph-based collaborative search summary visualisation %U https://ieeexplore.ieee.org/abstract/document/10038326 %X Currently, the search history in search engines is presented in a list view of some combination of enumerated results by title, url or search query. However, this classical list view is not ideal in collaborative search environments as it does not always assist users in understanding collaborators' search history results and the project's status. We present CollabGraph, a system for graph-based summary visualisation in collaborative search learning environments. Our system differentiates from existing solutions by visualising the summary of the collaboration results in a graph and having its core Personal Knowledge Graphs (PKGs) for each user. Our research questions concentrate around the CollabGraph's usefulness, preference, and enhancement of participation of student's and teacher's feedback compared to the list view of search history results. We evaluate our approach with an online questionnaire in 6 different project-based Search as Learning (SaL) scenarios. The evaluation of users' experience indicates that the CollabGraph is useful, highly likeable and could benefit users' participation and teacher's feedback by providing more precise insights into the project status. Our approach helps users better perceive about everyone's work, and it is a highly preferable feature alongside the list view. Also, the results demonstrate that graph summary visualisations, such as the CollabGraph, are more suitable for closed-end scenarios and collaborative projects with many participants.

@article{ilkou2023collabgraph, abstract = {Currently, the search history in search engines is presented in a list view of some combination of enumerated results by title, url or search query. However, this classical list view is not ideal in collaborative search environments as it does not always assist users in understanding collaborators' search history results and the project's status. We present CollabGraph, a system for graph-based summary visualisation in collaborative search learning environments. Our system differentiates from existing solutions by visualising the summary of the collaboration results in a graph and having its core Personal Knowledge Graphs (PKGs) for each user. Our research questions concentrate around the CollabGraph's usefulness, preference, and enhancement of participation of student's and teacher's feedback compared to the list view of search history results. We evaluate our approach with an online questionnaire in 6 different project-based Search as Learning (SaL) scenarios. The evaluation of users' experience indicates that the CollabGraph is useful, highly likeable and could benefit users' participation and teacher's feedback by providing more precise insights into the project status. Our approach helps users better perceive about everyone's work, and it is a highly preferable feature alongside the list view. Also, the results demonstrate that graph summary visualisations, such as the CollabGraph, are more suitable for closed-end scenarios and collaborative projects with many participants.}, added-at = {2024-02-07T10:03:38.000+0100}, author = {Ilkou, Eleni and Tolmachova, Tetiana and Fisichella, Marco and Taibi, Davide}, biburl = {https://www.bibsonomy.org/bibtex/2a4b2b4e5956e4cd3390d1f683083ffbd/mfisichella}, interhash = {fa7ca3f7ca3c8fac67161e7ab6a707ee}, intrahash = {a4b2b4e5956e4cd3390d1f683083ffbd}, journal = {IEEE Transactions on Learning Technologies}, keywords = {}, pages = {1-18}, publisher = {IEEE}, timestamp = {2024-02-07T10:03:38.000+0100}, title = {CollabGraph: A graph-based collaborative search summary visualisation}, url = {https://ieeexplore.ieee.org/abstract/document/10038326}, year = 2023 }