%0 Generic %1 sanandaji2020developing %A Sanandaji, Anahita %A Grimm, Cindy %A West, Ruth %A Sanchez, Christopher %D 2020 %K 2020 3D arxiv shape %T Developing and Validating an Interactive Training Tool for Inferring 2D Cross-Sections of Complex 3D Structures %U http://arxiv.org/abs/2001.06737 %X Understanding 2D cross-sections of 3D structures is a crucial skill in many disciplines, from geology to medical imaging. Cross-section inference in the context of 3D structures requires a complex set of spatial/visualization skills including mental rotation, spatial structure understanding, and viewpoint projection. Prior studies show that experts differ from novices in these, and other, skill dimensions. Building on a previously developed model that hierarchically characterizes the specific spatial sub-skills needed for this task, we have developed the first domain-agnostic, computer-based training tool for cross-section understanding of complex 3D structures. We demonstrate, in an evaluation with 60 participants, that this interactive tool is effective for increasing cross-section inference skills for a variety of structures, from simple primitive ones to more complex biological structures.

@misc{sanandaji2020developing, abstract = {Understanding 2D cross-sections of 3D structures is a crucial skill in many disciplines, from geology to medical imaging. Cross-section inference in the context of 3D structures requires a complex set of spatial/visualization skills including mental rotation, spatial structure understanding, and viewpoint projection. Prior studies show that experts differ from novices in these, and other, skill dimensions. Building on a previously developed model that hierarchically characterizes the specific spatial sub-skills needed for this task, we have developed the first domain-agnostic, computer-based training tool for cross-section understanding of complex 3D structures. We demonstrate, in an evaluation with 60 participants, that this interactive tool is effective for increasing cross-section inference skills for a variety of structures, from simple primitive ones to more complex biological structures.}, added-at = {2020-01-28T12:07:14.000+0100}, author = {Sanandaji, Anahita and Grimm, Cindy and West, Ruth and Sanchez, Christopher}, biburl = {https://www.bibsonomy.org/bibtex/2d43caec2702444b69130bc954a6bf8e9/analyst}, description = {[2001.06737] Developing and Validating an Interactive Training Tool for Inferring 2D Cross-Sections of Complex 3D Structures}, interhash = {5a392c402480edbf2fc7bb9346afa9d7}, intrahash = {d43caec2702444b69130bc954a6bf8e9}, keywords = {2020 3D arxiv shape}, note = {cite arxiv:2001.06737Comment: 17 pages, 9 figures, 3 tables}, timestamp = {2020-01-28T12:07:14.000+0100}, title = {Developing and Validating an Interactive Training Tool for Inferring 2D Cross-Sections of Complex 3D Structures}, url = {http://arxiv.org/abs/2001.06737}, year = 2020 }