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Stephanie J. Blackmon; Robert L. Moore – Journal of Computing in Higher Education, 2024
As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus' learning analytics practices…
Descriptors: Learning Analytics, Networks, Models, Ethics
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Giora Alexandron; Aviram Berg; Jose A. Ruiperez-Valiente – IEEE Transactions on Learning Technologies, 2024
This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…
Descriptors: Cheating, Identification, Online Courses, Artificial Intelligence
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Susan T. Hibbard; Jeanne McClure; Shaun Kellogg – New Directions for Teaching and Learning, 2024
This chapter introduces the learning analytics as a catalyst to transform data utilization and bolster support for the scholarship of teaching and learning.
Descriptors: Learning Analytics, Allied Health Occupations Education, Data Use, Scholarship
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Hanqiang Liu; Xiao Chen; Feng Zhao – Education and Information Technologies, 2024
Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform.…
Descriptors: MOOCs, Video Technology, Behavior Patterns, Prediction
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Kim, Yunsung; Sreechan; Piech, Chris; Thille, Candace – International Educational Data Mining Society, 2023
Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that…
Descriptors: Item Response Theory, Accuracy, Inferences, Algorithms
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Nuangchalerm, Prasart; Prachagool, Veena – Online Submission, 2023
In recent years, the integration approach of Artificial Intelligence (AI) is called for many disciplines, it also STEM education has paved the way for transformative advancements. This paper provides an example of AI-driven learning analytics within the context of STEM education. It provides a thorough analysis of the AI-driven STEM curriculum and…
Descriptors: Artificial Intelligence, Learning Analytics, STEM Education, Technology Uses in Education
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Cleophas, Catherine; Hönnige, Christoph; Meisel, Frank; Meyer, Philipp – INFORMS Transactions on Education, 2023
As the COVID-19 pandemic motivated a shift to virtual teaching, exams have increasingly moved online too. Detecting cheating through collusion is not easy when tech-savvy students take online exams at home and on their own devices. Such online at-home exams may tempt students to collude and share materials and answers. However, online exams'…
Descriptors: Computer Assisted Testing, Cheating, Identification, Essay Tests
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Vatsalan, Dinusha; Rakotoarivelo, Thierry; Bhaskar, Raghav; Tyler, Paul; Ladjal, Djazia – British Journal of Educational Technology, 2022
With Big Data revolution, the education sector is being reshaped. The current data-driven education system provides many opportunities to utilize the enormous amount of collected data about students' activities and performance for personalized education, adapting teaching methods, and decision making. On the other hand, such benefits come at a…
Descriptors: Privacy, Risk, Data, Markov Processes
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Guo, Hongfei; Yu, Xiaomei; Wang, Xinhua; Guo, Lei; Xu, Liancheng; Lu, Ran – International Journal of Distance Education Technologies, 2022
As students in online courses usually show differences in their cognitive levels and lack communication with teachers, it is difficult for teachers to grasp student perceptions of the importance of knowledgepoints and to develop personalized teaching. Though recent studies have paid attention to this topic, existing methods fail to calculate the…
Descriptors: Online Courses, Individualized Instruction, Learning Analytics, Concept Mapping
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Marshall, Ruth; Pardo, Abelardo; Smith, David; Watson, Tony – British Journal of Educational Technology, 2022
For the developers of next-generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology-mediated learning to gain insights…
Descriptors: Educational Technology, Learning Analytics, Ethics, Privacy
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Khajonmote, Withamon; Chinsook, Kittipong; Klintawon, Sununta; Sakulthai, Chaiyan; Leamsakul, Wicha; Jansawang, Natchanok; Jantakoon, Thada – Journal of Education and Learning, 2022
The system architecture of big data in massive open online courses (BD-MOOCs System Architecture) is composed of six components. The first component was comprised of big data tools and technologies such as Hadoop, YARN, HDFS, Spark, Hive, Sqoop, and Flume. The second component was educational data science, which is composed of the following four…
Descriptors: MOOCs, Data Collection, Student Behavior, Computer Software
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Benz, Gregor; Buhlinger, Carsten; Ludwig, Tobias – Physics Education, 2022
With the availability of educational digital data acquisition systems, it has also become possible in physics education to generate 'big' data sets by (a) measuring multiple variables simultaneously, (b) increasing the sample rate, (c) extending the measurement duration, or (d) choosing a combination among these three options. In the context of…
Descriptors: Physics, Science Instruction, Learning Analytics, Data Analysis
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Flávio Marques; Leonardo Lignani; João Quadros; Myrna Amorim; Windson Viana; Eduardo Ogasawara; Joel dos Santos – Technology, Knowledge and Learning, 2025
Educational games help reinforce educational concepts. They help students learn through hypothesizing, probing, and reflecting upon the game environment. Understanding the impact of a game is important before deploying it in a class. Recent studies in learning analysis describe methodologies and approaches for analyzing educational games. However,…
Descriptors: Design, Educational Games, Reinforcement, Game Based Learning
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Stanja, Judith; Gritz, Wolfgang; Krugel, Johannes; Hoppe, Anett; Dannemann, Sarah – British Journal of Educational Technology, 2023
Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for…
Descriptors: Formative Evaluation, Student Attitudes, Learning Analytics, Student Evaluation
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Rotelli, Daniela; Monreale, Anna – Journal of Learning Analytics, 2023
The increased adoption of online learning environments has resulted in the availability of vast amounts of educational log data, which raises questions that could be answered by a thorough and accurate examination of students' online learning behaviours. Event logs describe something that occurred on a platform and provide multiple dimensions that…
Descriptors: Learning Analytics, Learning Management Systems, Time on Task, Student Behavior
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