@article{CaspariSadeghi2022, author = {Caspari-Sadeghi, Sima}, title = {Learning assessment in the age of big data: Learning analytics in higher education}, series = {Cogent Education}, volume = {2023}, journal = {Cogent Education}, number = {Volume 110, issue 1}, publisher = {Taylor \& Francis}, doi = {0.1080/2331186X.2022.2162697}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-12236}, pages = {11 Seiten}, year = {2022}, abstract = {Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that educa-tion lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper con-cludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.}, language = {en} } @article{CaspariSadeghi2022, author = {Caspari-Sadeghi, Sima}, title = {Artificial Intelligence in Technology-Enhanced Assessment: A Survey of Machine Learning}, series = {Journal of Educational Technology Systems}, volume = {51}, journal = {Journal of Educational Technology Systems}, number = {3}, publisher = {SAGE Publications}, address = {Sage CA: Los Angeles, CA}, issn = {0047-2395}, doi = {10.1177/00472395221138791}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-11818}, pages = {372 -- 386}, year = {2022}, abstract = {Intelligent assessment, the core of any AI-based educational technology, is defined as embedded, stealth and ubiquitous assessment which uses intelligent techniques to diagnose the current cognitive level, monitor dynamic progress, predict success and update students' profiling continuously. It also uses various technologies, such as learning analytics, educational data mining, intelligent sensors, wearables and machine learning. This can be the key to Precision Education (PE): adaptive, tailored, individualized instruction and learning. This paper explores (a) the applications of Machine Learning (ML) in intelligent assessment, and (b) the use of deep learning models in 'knowledge tracing and student modeling'. The paper concludes by discussing barriers involved in using state-of-the-art ML methods and some suggestions to unleash the power of data and ML to improve educational decision-making.}, language = {en} }