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Esteban Villalobos; Isabel Hilliger; Carlos Gonzalez; Sergio Celis; Mar Pérez-Sanagustín; Julien Broisin – Journal of Learning Analytics, 2024
Researchers in learning analytics have created indicators with learners' trace data as a proxy for studying learner behaviour in a college course. Student Approaches to Learning (SAL) is one of the theories used to explain these behaviours, distinguishing between deep, surface, and organized study. In Latin America, researchers have demonstrated…
Descriptors: Learning Analytics, Academic Achievement, Role Theory, Learning Processes
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Dirk Tempelaar; Bart Rienties; Bas Giesbers; Quan Nguyen – Journal of Learning Analytics, 2023
Learning analytics needs to pay more attention to the temporal aspect of learning processes, especially in self-regulated learning (SRL) research. In doing so, learning analytics models should incorporate both the duration and frequency of learning activities, the passage of time, and the temporal order of learning activities. However, where this…
Descriptors: Time Factors (Learning), Learning Analytics, Models, Statistical Analysis
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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
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Kaliisa, Rogers; Kluge, Anders; Mørch, Anders I. – Journal of Learning Analytics, 2020
Learning analytics (LA) constitutes a key opportunity to support learning design (LD) in blended learning environments. However, details as to how LA supports LD in practice and information on teacher experiences with LA are limited. This study explores the potential of LA to inform LD based on a one-semester undergraduate blended learning course…
Descriptors: Learning Analytics, Instructional Design, Decision Making, Blended Learning
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Hartmann, Christian; Rummel, Nikol; Bannert, Maria – Journal of Learning Analytics, 2022
This paper presents a fine-grained process analysis of 22 students in a classroom-based learning setting. The students engaged (and failed) in problem-solving attempts prior to instruction (i.e., the Productive-Failure approach). We used the HeuristicsMiner algorithm to analyze the data of a quasi-experimental study. The applied algorithm allowed…
Descriptors: Heuristics, Problem Solving, Computer Software, Comparative Analysis
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Vigentini, Lorenzo; Swibel, Brad; Hasler, Garth – Journal of Learning Analytics, 2022
While Learning Analytics (LA) have gained momentum in higher education, there are still few examples of application in the school sector. Even fewer cases are reported of systematic, organizational adoption to drive the support of student learning trajectories that includes teachers, pastoral leaders, and academic managers. This paper presents one…
Descriptors: Learning Analytics, Educational Improvement, Secondary School Students, Learning Management Systems
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Stanislav Pozdniakov; Roberto Martinez-Maldonado; Yi-Shan Tsai; Vanessa Echeverria; Zachari Swiecki; Dragan Gaševic – Journal of Learning Analytics, 2025
Recent research on learning analytics dashboards has focused on designing user interfaces that offer various forms of "visualization guidance" (often referring to notions such as "data storytelling" or "narrative visualization") to teachers (e.g., emphasizing data points or trends with colour and adding annotations),…
Descriptors: Visual Aids, Learning Analytics, Technological Literacy, Pedagogical Content Knowledge
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Iraj, Hamideh; Fudge, Anthea; Khan, Huda; Faulkner, Margaret; Pardo, Abelardo; Kovanovic, Vitomir – Journal of Learning Analytics, 2021
One of the major factors affecting student learning is feedback. Although the importance of feedback has been recognized in educational institutions, dramatic changes--such as bigger class sizes and a more diverse student population--challenged the provision of effective feedback. In light of these changes, educators have increasingly been using…
Descriptors: Learner Engagement, Learning Analytics, Feedback (Response), Class Size
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Milligan, Sandra; Griffin, Patrick – Journal of Learning Analytics, 2016
The paper describes empirical investigations of how participants in a MOOC learn, and the implications for MOOC design. A learner capability to generate higher order learning in MOOCs--called crowd-sourced learning (C-SL) capability--was defined from learning science literature. The capability comprised a complex yet interrelated array of…
Descriptors: Online Courses, Large Group Instruction, Thinking Skills, Learning Processes
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Sonnenberg, Christoph; Bannert, Maria – Journal of Learning Analytics, 2015
According to research examining self-regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing…
Descriptors: Metacognition, Cues, Control Groups, Outcomes of Education
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Colthorpe, Kay; Zimbardi, Kirsten; Ainscough, Louise; Anderson, Stephen – Journal of Learning Analytics, 2015
It is well established that a student's capacity to regulate his or her own learning is a key determinant of academic success, suggesting that interventions targeting improvements in self-regulation will have a positive impact on academic performance. However, to evaluate the success of such interventions, the self-regulatory characteristics of…
Descriptors: Data Analysis, Data Collection, Educational Research, Self Control