%0 Book Section %1 Effenberger2021 %A Effenberger, Tomás %A Pelánek, Radek %B Visualizations and Dashboards for Learning Analytics %C Cham %D 2021 %E Sahin, Muhittin %E Ifenthaler, Dirk %I Springer International Publishing %K dashboard learning-analytics %P 439--456 %R 10.1007/978-3-030-81222-5_20 %T Visualization of Student-Item Interaction Matrix %U https://doi.org/10.1007/978-3-030-81222-5_20 %X One type of visualization of data from digital learning environments focuses on students' interaction with the educational content. Students may, for example, answer questions, read texts, or solve problems. We can represent these interactions as a matrix, where rows correspond to students, columns to educational items, and values to some aspect of student activity (e.g., the correctness of answers, response times, the order of actions). Visualizing this matrix is useful for several purposes. For teachers, it can provide an understanding of the skill and behavior of their students. For system developers, it can provide insight into the behavior of both students and adaptive algorithms, and it can also help detect suspicious activity. For researchers, it can provide an understanding of the properties of datasets used in experiments and valuable warnings about biases that are present in data. However, suitable visualization of the student-item interactions is nontrivial. To facilitate the design of the visualization, we provide a systematic discussion of approaches to student-item matrix visualization. Using data from an introductory programming exercise, we also provide specific illustrations of different visualization designs. %@ 978-3-030-81222-5

@inbook{Effenberger2021, abstract = {One type of visualization of data from digital learning environments focuses on students' interaction with the educational content. Students may, for example, answer questions, read texts, or solve problems. We can represent these interactions as a matrix, where rows correspond to students, columns to educational items, and values to some aspect of student activity (e.g., the correctness of answers, response times, the order of actions). Visualizing this matrix is useful for several purposes. For teachers, it can provide an understanding of the skill and behavior of their students. For system developers, it can provide insight into the behavior of both students and adaptive algorithms, and it can also help detect suspicious activity. For researchers, it can provide an understanding of the properties of datasets used in experiments and valuable warnings about biases that are present in data. However, suitable visualization of the student-item interactions is nontrivial. To facilitate the design of the visualization, we provide a systematic discussion of approaches to student-item matrix visualization. Using data from an introductory programming exercise, we also provide specific illustrations of different visualization designs.}, added-at = {2021-12-21T18:39:31.000+0100}, address = {Cham}, author = {Effenberger, Tom{\'a}{\v{s}} and Pel{\'a}nek, Radek}, biburl = {https://www.bibsonomy.org/bibtex/21cf0a999d98a37569283fa7b3ed210cd/brusilovsky}, booktitle = {Visualizations and Dashboards for Learning Analytics}, description = {Visualization of Student-Item Interaction Matrix | SpringerLink}, doi = {10.1007/978-3-030-81222-5_20}, editor = {Sahin, Muhittin and Ifenthaler, Dirk}, interhash = {173becf49ac2ea29d74999df18e272da}, intrahash = {1cf0a999d98a37569283fa7b3ed210cd}, isbn = {978-3-030-81222-5}, keywords = {dashboard learning-analytics}, pages = {439--456}, publisher = {Springer International Publishing}, timestamp = {2021-12-21T18:39:31.000+0100}, title = {Visualization of Student-Item Interaction Matrix}, url = {https://doi.org/10.1007/978-3-030-81222-5_20}, year = 2021 }