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@inproceedings{1750939, author = {Pelánek, Radek}, address = {New York, NY, United States}, booktitle = {Proceedings of the Tenth International Conference on Learning Analytics & Knowledge}, doi = {http://dx.doi.org/10.1145/3375462.3375463}, editor = {Christoph Rensing, Hendrik Drachsler}, keywords = {trade-offs; scalability; methodology}, howpublished = {elektronická verze "online"}, language = {eng}, location = {New York, NY, United States}, isbn = {978-1-4503-7712-6}, pages = {554-558}, publisher = {Association for Computing Machinery}, title = {Learning analytics challenges: trade-offs, methodology, scalability}, url = {https://dl.acm.org/doi/10.1145/3375462.3375463}, year = {2020} }
TY - JOUR ID - 1750939 AU - Pelánek, Radek PY - 2020 TI - Learning analytics challenges: trade-offs, methodology, scalability PB - Association for Computing Machinery CY - New York, NY, United States SN - 9781450377126 KW - trade-offs KW - scalability KW - methodology UR - https://dl.acm.org/doi/10.1145/3375462.3375463 N2 - Ryan Baker presented in a LAK 2019 keynote a list of six grand challenges for learning analytics research. The challenges are specified as problems with clearly defined success criteria. Education is, however, a domain full of ill-defined problems. I argue that learning analytics research should reflect this nature of the education domain and focus on less clearly defined, but practically essential issues. As an illustration, I discuss three important challenges of this type: addressing inherent trade-offs in learning environments, the clarification of methodological issues, and the scalability of system development. ER -
PELÁNEK, Radek. Learning analytics challenges: trade-offs, methodology, scalability. Online. In Christoph Rensing, Hendrik Drachsler. \textit{Proceedings of the Tenth International Conference on Learning Analytics \&{} Knowledge}. New York, NY, United States: Association for Computing Machinery, 2020, s.~554-558. ISBN~978-1-4503-7712-6. Dostupné z: https://dx.doi.org/10.1145/3375462.3375463.
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