@inproceedings{schulz_data-driven_2020-1,
	address = {Berlin, Germany},
	title = {A data-driven platform for creating educational content in language learning},
	url = {http://ceur-ws.org/Vol-2535/paper_9.pdf},
	abstract = {In times of increasingly personalized educational content, designing
a data-driven platform which offers the opportunity to create
content for different use cases is arguably the only solution to handle the
massive amount of information. Therefore, we developed the software
"Machina Callida" (MC) in our project CALLIDUS (Computer-Aided
Language Learning: Vocabulary Acquisition in Latin using Corpus-based
Methods) which is funded by the German Research Foundation.
The main focus of this research project is to optimize the vocabulary
acquisition of Latin by using a data-driven language learning approach
for creating exercises. To achieve that goal, we were facing problems
concerning the quality of externally curated research data (e.g. annotated
text corpora) while curating educational materials ourselves (e.g.
predefined sequences of exercises). Besides, we needed to build an interface
which would be user-friendly both for teachers and students. While
teachers would like to create an exercise or test and use them (even as
printed out copies) in class, students would like to learn on the 
y and
right away.
As a result we offer a repository, a file exporter for various formats and,
above all, interactive exercises so that learners are actively engaged in
the learning process. In this paper we show the work
ow of our software
and explain the architecture focusing on the integration of Artificial Intelligence
(AI) and data curation. Ideally, we want to use AI technology
to facilitate the process and increase the quality of content creation,
dissemination and personalization for our end users.},
	language = {English},
	booktitle = {Proceedings of the {Conference} on {Digital} {Curation} {Technologies} ({Qurator} 2020)},
	author = {Schulz, Konstantin and Beyer, Andrea and Dreyer, Malte and Kipf, Stefan},
	month = jan,
	year = {2020},
	keywords = {scientific},
}

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The main focus of this research project is to optimize the vocabulary acquisition of Latin by using a data-driven language learning approach for creating exercises. To achieve that goal, we were facing problems concerning the quality of externally curated research data (e.g. annotated text corpora) while curating educational materials ourselves (e.g. predefined sequences of exercises). Besides, we needed to build an interface which would be user-friendly both for teachers and students. While teachers would like to create an exercise or test and use them (even as printed out copies) in class, students would like to learn on the y and right away. As a result we offer a repository, a file exporter for various formats and, above all, interactive exercises so that learners are actively engaged in the learning process. In this paper we show the work ow of our software and explain the architecture focusing on the integration of Artificial Intelligence (AI) and data curation. Ideally, we want to use AI technology to facilitate the process and increase the quality of content creation, dissemination and personalization for our end users.","language":"English","booktitle":"Proceedings of the Conference on Digital Curation Technologies (Qurator 2020)","author":[{"propositions":[],"lastnames":["Schulz"],"firstnames":["Konstantin"],"suffixes":[]},{"propositions":[],"lastnames":["Beyer"],"firstnames":["Andrea"],"suffixes":[]},{"propositions":[],"lastnames":["Dreyer"],"firstnames":["Malte"],"suffixes":[]},{"propositions":[],"lastnames":["Kipf"],"firstnames":["Stefan"],"suffixes":[]}],"month":"January","year":"2020","keywords":"scientific","bibtex":"@inproceedings{schulz_data-driven_2020-1,\n\taddress = {Berlin, Germany},\n\ttitle = {A data-driven platform for creating educational content in language learning},\n\turl = {http://ceur-ws.org/Vol-2535/paper_9.pdf},\n\tabstract = {In times of increasingly personalized educational content, designing\na data-driven platform which offers the opportunity to create\ncontent for different use cases is arguably the only solution to handle the\nmassive amount of information. Therefore, we developed the software\n\"Machina Callida\" (MC) in our project CALLIDUS (Computer-Aided\nLanguage Learning: Vocabulary Acquisition in Latin using Corpus-based\nMethods) which is funded by the German Research Foundation.\nThe main focus of this research project is to optimize the vocabulary\nacquisition of Latin by using a data-driven language learning approach\nfor creating exercises. To achieve that goal, we were facing problems\nconcerning the quality of externally curated research data (e.g. annotated\ntext corpora) while curating educational materials ourselves (e.g.\npredefined sequences of exercises). Besides, we needed to build an interface\nwhich would be user-friendly both for teachers and students. While\nteachers would like to create an exercise or test and use them (even as\nprinted out copies) in class, students would like to learn on the \ny and\nright away.\nAs a result we offer a repository, a file exporter for various formats and,\nabove all, interactive exercises so that learners are actively engaged in\nthe learning process. In this paper we show the work\now of our software\nand explain the architecture focusing on the integration of Artificial Intelligence\n(AI) and data curation. Ideally, we want to use AI technology\nto facilitate the process and increase the quality of content creation,\ndissemination and personalization for our end users.},\n\tlanguage = {English},\n\tbooktitle = {Proceedings of the {Conference} on {Digital} {Curation} {Technologies} ({Qurator} 2020)},\n\tauthor = {Schulz, Konstantin and Beyer, Andrea and Dreyer, Malte and Kipf, Stefan},\n\tmonth = jan,\n\tyear = {2020},\n\tkeywords = {scientific},\n}\n\n","author_short":["Schulz, K.","Beyer, A.","Dreyer, M.","Kipf, S."],"key":"schulz_data-driven_2020-1","id":"schulz_data-driven_2020-1","bibbaseid":"schulz-beyer-dreyer-kipf-adatadrivenplatformforcreatingeducationalcontentinlanguagelearning-2020","role":"author","urls":{"Paper":"http://ceur-ws.org/Vol-2535/paper_9.pdf"},"keyword":["scientific"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://api.zotero.org/users/912485/collections/VL2FMYT9/items?key=tJHJRX0dfdnwe3iuxbtyH4ht&format=bibtex&limit=100","dataSources":["nsAxma8Gv9c6fai5n"],"keywords":["scientific"],"search_terms":["data","driven","platform","creating","educational","content","language","learning","schulz","beyer","dreyer","kipf"],"title":"A data-driven platform for creating educational content in language learning","year":2020}