TY - JOUR A1 - Caspari-Sadeghi, Sima T1 - Applying Learning Analytics in Online Environments: Measuring Learners’ Engagement Unobtrusively JF - Frontiers in Education N2 - Prior to the emergence of Big Data and technologies such as Learning Analytics (LA), classroom research focused mainly on measuring learning outcomes of a small sample through tests. Research on online environments shows that learners’ engagement is a critical precondition for successful learning and lack of engagement is associated with failure and dropout. LA helps instructors to track, measure and visualize students’ online behavior and use such digital traces to improve instruction and provide individualized support, i.e., feedback. This paper examines 1) metrics or indicators of learners’ engagement as extracted and displayed by LA, 2) their relationship with academic achievement and performance, and 3) some freely available LA tools for instructors and their usability. The paper concludes with making recommendations for practice and further research by considering challenges associated with using LA in classrooms. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-10440 SP - 1 EP - 6 ER - TY - JOUR A1 - Caspari-Sadeghi, Sima T1 - Learning assessment in the age of big data: Learning analytics in higher education JF - Cogent Education N2 - 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. KW - Big data KW - learning analytics KW - technology-enhanced assessment Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-12236 VL - 2023 IS - Volume 110, issue 1 PB - Taylor & Francis ER - TY - JOUR A1 - Caspari-Sadeghi, Sima T1 - Artificial Intelligence in Technology-Enhanced Assessment: A Survey of Machine Learning JF - Journal of Educational Technology Systems N2 - 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. KW - artificial intelligence KW - knowledge tracing KW - machine learning KW - technology-enhanced assessment (TEA) Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-11818 SN - 0047-2395 SN - 1541-3810 VL - 51 IS - 3 SP - 372 EP - 386 PB - SAGE Publications CY - Sage CA: Los Angeles, CA ER -