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Biedermann, Daniel; Ciordas-Hertel, George-Petru; Winter, Marc; Mordel, Julia; Drachsler, Hendrik – Journal of Learning Analytics, 2023
Learners use digital media during learning for a variety of reasons. Sometimes media use can be considered "on-task," e.g., to perform research or to collaborate with peers. In other cases, media use is "off-task," meaning that learners use content unrelated to their current learning task. Given the well-known problems with…
Descriptors: Learning Processes, Learning Analytics, Information Technology, Behavior Patterns
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
Li Chen; Xuewang Geng; Min Lu; Atsushi Shimada; Masanori Yamada – SAGE Open, 2023
Developed to maximize learning performance, learning analytics dashboards (LAD) are becoming increasingly commonplace in education. An LAD's effectiveness depends on how it is used and varies according to users' academic levels. In this study, two LADs and a learning support system were used in a higher education course to support students'…
Descriptors: Learning Analytics, Learning Management Systems, Cognitive Processes, Learning Strategies
Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
Gomathy Ramaswami; Teo Susnjak; Anuradha Mathrani – Journal of Learning Analytics, 2023
Learning Analytics Dashboards (LADs) are gaining popularity as a platform for providing students with insights into their learning behaviour patterns in online environments. Existing LAD studies are mainly centred on displaying students' online behaviours with simplistic descriptive insights. Only a few studies have integrated predictive…
Descriptors: Learner Engagement, Learning Analytics, Electronic Learning, Student Behavior
Mohd Fazil; Angelica Rísquez; Claire Halpin – Journal of Learning Analytics, 2024
Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…
Descriptors: Learning Analytics, Learning Management Systems, Learning Processes, Decision Making
Weidlich, Joshua; Gaševic, Dragan; Drachsler, Hendrik – Journal of Learning Analytics, 2022
As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they…
Descriptors: Causal Models, Inferences, Learning Analytics, Comparative Analysis
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
Lars de Vreugd; Anouschka van Leeuwen; Renée Jansen; Marieke van der Schaaf – Journal of Learning Analytics, 2024
For university students, self-regulation of study behaviour is important. However, students are not always capable of effective self-regulation. Providing study behaviour information via a learning analytics dashboard (LAD) may support phases within self-regulated learning (SRL). However, it is unclear what information a LAD should provide, how to…
Descriptors: Learning Management Systems, Learning Analytics, Student Behavior, Behavior Patterns
Arjan J. F. Kok; Lex Bijlsma; Cornelis Huizing; Ruurd Kuiper; Harrie Passier – Informatics in Education, 2024
This paper presents the first experiences of the use of an online open-source repository with programming exercises. The repository is independent of any specific teaching approach. Students can search for and select an exercise that trains the programming concepts that they want to train and that only uses the programming concepts they already…
Descriptors: Programming Languages, Computer Science Education, Open Source Technology, Teaching Methods
Kai Li – International Association for Development of the Information Society, 2023
Assessing students' performance in online learning could be executed not only by the traditional forms of summative assessments such as using essays, assignments, and a final exam, etc. but also by more formative assessment approaches such as interaction activities, forum posts, etc. However, it is difficult for teachers to monitor and assess…
Descriptors: Student Evaluation, Online Courses, Electronic Learning, Computer Literacy
Krumm, Andrew; Everson, Howard T.; Neisler, Julie – Journal of Learning Analytics, 2022
This paper describes a partnership-based approach for analyzing data from a learning management system (LMS) used by students in grades 6-12. The goal of the partnership was to create indicators for the ways in which students navigated digital learning activities, referred to as playlists, that were comprised of resources, pre-assessments, and…
Descriptors: Learning Management Systems, Data Analysis, Electronic Learning, Student Behavior
Maloney, Suzanne; Axelsen, Megan; Galligan, Linda; Turner, Joanna; Redmond, Petrea; Brown, Alice; Basson, Marita; Lawrence, Jill – Online Learning, 2022
Driven by the increased availability of Learning Management System data, this study explored its value and sought understanding of student behaviour through the information contained in activity level log data. Specifically, this study examined analytics data to understand students' engagement with online videos. Learning analytics data from the…
Descriptors: Learning Analytics, Video Technology, Learning Management Systems, Comparative Analysis
Kay, Ellie; Bostock, Paul – Student Success, 2023
Providing timely nudges to students has been shown to improve engagement and persistence in tertiary education. However, many studies focus on small-scale pilots rather than institution-wide initiatives. This article assesses the impact of a pan-institution Early Alert System at the University of Canterbury that utilises nudging when students are…
Descriptors: At Risk Students, Learner Engagement, Undergraduate Students, Handheld Devices
de Carvalho, Walisson Ferreira; Zárate, Luis Enrique – International Journal of Information and Learning Technology, 2021
Purpose: The paper aims to present a new two stage local causal learning algorithm -- HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm,…
Descriptors: Causal Models, Algorithms, Learning Analytics, Correlation
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