%0 Conference Paper %1 saar2018personalized %A Saar, Merike %A Prieto, Luis P. %A Rodríguez-Triana, María Jesús %A Kusmin, Marge %B 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT) %D 2018 %K analytics classroom design learning personalised principles teaching %P 58-62 %T Personalized, teacher-driven in-action data collection: technology design principles %U https://ieeexplore.ieee.org/abstract/document/8433453 %X The collection of evidence from authentic classroom practice is important both for teacher professional development (TPD) and educational research. However, most current learning analytics (LA) fails to capture the physical occurrences of the classroom, and do not always address individual teachers' needs. More customizable forms of classroom data collection (e.g., through video-recordings or by human observers) are time-consuming and expensive to implement. Navigating this tradeoff between the personalization of data collection and the strict time/effort constraints of classroom practice is still an unsolved challenge for designers of LA and teaching analytics (TA) systems to be used in face-to-face classrooms. In this paper, we extract lessons learnt from a design-based research process that explores this tradeoff, towards the development of teacher-driven, personalizable data collection tools. Through a survey of 15 expert teachers and paper and software prototype testing with a total of 14 teachers in simulated and authentic settings, we gathered information about teachers' preferences for teacher-driven data collection in classrooms, and derived design insights (e.g., a cold-start problem and the need for multiple layers in the personalization), which can be useful for the design of TA/LA tools that collect personalized data from everyday classrooms.

@inproceedings{saar2018personalized, abstract = {The collection of evidence from authentic classroom practice is important both for teacher professional development (TPD) and educational research. However, most current learning analytics (LA) fails to capture the physical occurrences of the classroom, and do not always address individual teachers' needs. More customizable forms of classroom data collection (e.g., through video-recordings or by human observers) are time-consuming and expensive to implement. Navigating this tradeoff between the personalization of data collection and the strict time/effort constraints of classroom practice is still an unsolved challenge for designers of LA and teaching analytics (TA) systems to be used in face-to-face classrooms. In this paper, we extract lessons learnt from a design-based research process that explores this tradeoff, towards the development of teacher-driven, personalizable data collection tools. Through a survey of 15 expert teachers and paper and software prototype testing with a total of 14 teachers in simulated and authentic settings, we gathered information about teachers' preferences for teacher-driven data collection in classrooms, and derived design insights (e.g., a cold-start problem and the need for multiple layers in the personalization), which can be useful for the design of TA/LA tools that collect personalized data from everyday classrooms.}, added-at = {2018-09-27T11:26:53.000+0200}, author = {Saar, Merike and Prieto, Luis P. and Rodríguez-Triana, María Jesús and Kusmin, Marge}, biburl = {https://www.bibsonomy.org/bibtex/23b0f68a421e1eed068fbbe8d40d4ba95/yish}, booktitle = {2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT)}, interhash = {9a054670b9dd1fa6999c916f795dc739}, intrahash = {3b0f68a421e1eed068fbbe8d40d4ba95}, keywords = {analytics classroom design learning personalised principles teaching}, organization = {IEEE}, pages = {58-62}, timestamp = {2018-09-27T11:27:53.000+0200}, title = {Personalized, teacher-driven in-action data collection: technology design principles}, url = {https://ieeexplore.ieee.org/abstract/document/8433453}, year = 2018 }