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Nina Bergdahl; Melissa Bond; Jeanette Sjöberg; Mark Dougherty; Emily Oxley – International Journal of Educational Technology in Higher Education, 2024
Educational outcomes are heavily reliant on student engagement, yet this concept is complex and subject to diverse interpretations. The intricacy of the issue arises from the broad spectrum of interpretations, each contributing to the understanding of student engagement as both complex and multifaceted. Given the emergence and increasing use of…
Descriptors: Learner Engagement, College Students, Student Behavior, Educational Technology
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Lars de Vreugd; Anouschka van Leeuwen; Marieke van der Schaaf – Journal of Computer Assisted Learning, 2025
Background: University students need to self-regulate but are sometimes incapable of doing so. Learning Analytics Dashboards (LADs) can support students' appraisal of study behaviour, from which goals can be set and performed. However, it is unclear how goal-setting and self-motivation within self-regulated learning elicits behaviour when using an…
Descriptors: Learning Analytics, Educational Technology, Goal Orientation, Learning Motivation
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Krieter, Philipp – IEEE Transactions on Learning Technologies, 2022
The time students spend in a learning management system (LMS) is an important measurement in learning analytics (LA). One of the most common data sources is log files from LMS, which do not directly reveal the online time, the duration of which needs to be estimated. As this measurement has a great impact on the results of statistical models in…
Descriptors: Integrated Learning Systems, Learning Analytics, Electronic Learning, Students
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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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Qi Zhou; Wannapon Suraworachet; Mutlu Cukurova – Education and Information Technologies, 2024
Collaboration is argued to be an important skill, not only in schools and higher education contexts but also in the workspace and other aspects of life. However, simply asking students to work together as a group on a task does not guarantee success in collaboration. Effective collaborative learning requires meaningful interactions among…
Descriptors: Learning Analytics, Cooperative Learning, Nonverbal Communication, Speech Communication
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Prat, Alain; Code, Warren J. – International Journal of Mathematical Education in Science and Technology, 2021
The online homework system WeBWorK has been successfully used at several hundred colleges and universities. Despite its popularity, the WeBWorK system does not provide detailed metrics of student performance to instructors. In this article, we illustrate how an analysis of the log files of the WeBWorK system can provide information such as the…
Descriptors: Data Analysis, Homework, Student Behavior, Educational Technology
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Ameloot, Elise; Rotsaert, Tijs; Schellens, Tammy – Journal of Computer Assisted Learning, 2022
Background: Although blended learning (BL) has multiple educational prospects, it also poses challenges such as keeping students motivated. Objectives: This study investigates students' perceptions of how learning analytics (LA) can be used to support the design of a BL environment in order to promote students' basic need for relatedness, which is…
Descriptors: Learning Analytics, Blended Learning, Student Attitudes, Need Gratification
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Aleksandra Maslennikova; Daniela Rotelli; Anna Monreale – Journal of Learning Analytics, 2023
Students organize and manage their own learning time, choosing when, what, and how to study due to the flexibility of online learning. Each person has unique learning habits that define their behaviours and distinguish them from others. To investigate the temporal behaviour of students in online learning environments, we seek to identify suitable…
Descriptors: Learning Analytics, Online Courses, Time Management, Self Management
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Zhang, Mo; Guo, Hongwen; Liu, Xiang – International Educational Data Mining Society, 2021
We present an empirical study on the use of keystroke analytics to capture and understand how writers manage their time and make inferences on how they allocate their cognitive resources during essay writing. The results suggest three distinct longitudinal patterns of writing process that describe how writers approach an essay task in a writing…
Descriptors: Keyboarding (Data Entry), Learning Analytics, Data Collection, Cognitive Processes
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Aziman Abdullah – International Society for Technology, Education, and Science, 2023
This study explores the potential of using screen time data in learning management systems (LMS) to estimate student learning time (SLT) and validate the credit value of courses. Gathering comprehensive data on actual student learning time is difficult, so this study uses LMS Moodle logs from a computer programming course with 490 students over 16…
Descriptors: Time Factors (Learning), Handheld Devices, Computer Use, Television Viewing
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Thai, Thuan; Hartup, Kate; Colbourn, Adelle; Yeung, Amanda – Australian Journal of Teacher Education, 2021
In Australia, teacher education students must pass the Literacy and Numeracy Test for Initial Teacher Education (LANTITE) to meet accreditation requirements. Although this has been mandated since 2016, there are currently few resources available for students to use in preparation for the test. To help students prepare for the numeracy component of…
Descriptors: Foreign Countries, Computer Assisted Testing, Mathematics Tests, Numeracy
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Bronson Hui; Björn Rudzewitz; Detmar Meurers – Language Learning & Technology, 2023
Interactive digital tools increasingly used for language learning can provide detailed system logs (e.g., number of attempts, responses submitted), and thereby a window into the user's learning processes. To date, SLA researchers have made little use of such data to understand the relationships between learning conditions, processes, and outcomes.…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Learning Processes
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Poole, Frederick J.; Clarke-Midura, Jody – Language Learning & Technology, 2023
Research involving digital games and language learning is rapidly growing. One advantage of using digital games to support language learning is the ability to collect data on students learning in real time. In this study, we use educational data mining methods to explore the relationship between in-game data and elementary students' Chinese…
Descriptors: Computer Games, Second Language Learning, Second Language Instruction, Data Analysis
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Xu, Cuiqin; Xia, Jun – Computer Assisted Language Learning, 2021
The last two decades have witnessed a quick shift from pen-and-paper writing to computer keyboard writing. Corresponding to this shift in the writing medium are vigorous research efforts to understand new features of writing in computer keyboard settings. Using Inputlog7.0, this study investigated the writing process of 60 Chinese English as a…
Descriptors: Scaffolding (Teaching Technique), Writing Processes, Writing Skills, English (Second Language)
Yang, Dandan; Zargar, Elham; Adams, Ashley Marie; Day, Stephanie L.; Connor, Carol McDonald – Assessment for Effective Intervention, 2021
Stealth assessment has been successfully embedded in educational games to measure students' learning in an unobtrusive and supportive way. This study explored the possibility of applying stealth assessment in a digital reading platform and sought to identify potential in-system indicators of students' digital learning outcomes. Utilizing the user…
Descriptors: Electronic Publishing, Books, Computer Assisted Instruction, Reading Processes