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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
N. Godbold; K. E. Matthews; D. Gannaway – International Journal for Academic Development, 2024
This study outlines an approach that captures the messy landscape of the learning opportunities on which teaching focused academics draw. The Activity Centred Analysis and Design framework was employed to analyse data from a focused ethnographic study of seven TFAs in a research-intensive university. The Activity Centred Analysis and Design…
Descriptors: Authentic Learning, Learning Analytics, Learning Processes, Instructional Design
Yuanyuan Yang; Rwitajit Majumdar; Huiyong Li; Brendan Flanagan; Hiroaki Ogata – Interactive Learning Environments, 2024
Self-directed learning (SDL) requires students to take initiative to learn and control their own learning process. Literature highlights the importance of SDL for lifelong learning. Yet, little understanding is known regarding how to support SDL at the school level, specifically for out-of-class learning context. To fill up this gap, this research…
Descriptors: Learning Analytics, Independent Study, Learning Processes, Reading Habits
Kaliisa, Rogers; Mørch, Anders I.; Kluge, Anders – Technology, Knowledge and Learning, 2022
The literature until 2020 has forecasted a significant uptake of learning analytics (LA) to support learning design in higher education. However, there remain only a few investigations into teachers' course design practices and their perspectives on LA as a tool to support their design practices. This paper presents findings from an examination of…
Descriptors: Learning Analytics, College Faculty, Teacher Attitudes, Instructional Design
Chia-Yu Hsu; Izumi Horikoshi; Rwitajit Majumdar; Hiroaki Ogata – Educational Technology & Society, 2024
This study focuses on the problem that the process of building learning habits has not been clearly described. Therefore, we aim to extract the stages of learning habits from log data. We propose a data model to extract stages of learning habits based on the transtheoretical model and apply the model to the learning logs of self-directed extensive…
Descriptors: Habit Formation, Behavior Change, Learning Analytics, Data Interpretation
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
Tanjun Liu; Dana Gablasova – Computer Assisted Language Learning, 2025
Collocations, a crucial component of language competence, remain a challenge for L2 learners across all proficiency levels. While the data-driven learning (DDL) approach has shown great potential for collocation learning from a shorter-term perspective, this study investigates its effectiveness in the long term, examining both linguistic gains and…
Descriptors: Phrase Structure, Learning Analytics, English (Second Language), Second Language Instruction
Ian Hardy; Vicente Reyes; Louise G. Phillips; M. Obaidul Hamid – Journal of Education Policy, 2024
Data infrastructures exist in a variety of formats. This article draws on the insights of senior personnel involved in developing a new data dashboard in one state jurisdiction in Australia. While literature on dashboards often focuses on the teachers and learners influenced by them, there is less attention to those involved in their development…
Descriptors: Learning Analytics, Learning Processes, Learning Management Systems, Computer Software
Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models
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
Using Learning Analytics to Support STEAM Students' Academic Achievement and Self-Regulated Learning
García-Senín, Stéphanie; Arguedas, Marta; Daradoumis, Thanasis – Research on Education and Media, 2022
The assessment of students' academic achievements helps to increase learning effectiveness by encouraging each student to recognise his/her strengths and areas for improvement. To do so, pedagogical activities that encourage direct and frequent evaluation must be considered. This paper focuses on how a learning management system such as Google…
Descriptors: Learning Analytics, STEM Education, Art Education, Academic Achievement
Amaya, Edna Johanna Chaparro; Restrepo-Calle, Felipe; Ramírez-Echeverry, Jhon J. – Journal of Information Technology Education: Research, 2023
Aim/Purpose: This article proposes a framework based on a sequential explanatory mixed-methods design in the learning analytics domain to enhance the models used to support the success of the learning process and the learner. The framework consists of three main phases: (1) quantitative data analysis; (2) qualitative data analysis; and (3)…
Descriptors: Learning Analytics, Guidelines, Student Attitudes, Learning Processes
Arnon Hershkovitz; Norbert Noster; Hans-Stefan Siller; Michal Tabach – ZDM: Mathematics Education, 2024
Learning Analytics is concerned with the use of data collected in educational settings to support learning processes. We take a Learning Analytics approach to study the use of immediate feedback in digital classification tasks in mathematics. Feedback serves as an opportunity for learning, however its mere existence does not guarantee its use and…
Descriptors: Learning Analytics, Classification, Geometry, Mathematics Instruction
Oliveira, Wilk; Tenório, Kamilla; Hamari, Juho; Pastushenko, Olena; Isotani, Seiji – Smart Learning Environments, 2021
The flow experience (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, concentration, sense of control, loss of self-consciousness, transformation of time, and "autotelic" experience) is an experience highly related to the learning experience. One of the current challenges is to identify whether…
Descriptors: Prediction, Psychological Patterns, Learning Processes, Student Behavior
Karmijn van de Oudeweetering; Jeremy Knox; Mathias Decuypere – Learning, Media and Technology, 2024
This paper examines the enactment of feedback in Massive Open Online Courses (MOOCs), focusing on analytics dashboards. Building on scholarship that recognizes data practices as entangled and 'messy', the paper problematizes the model of the feedback loop that assumes that analytics dashboards 'feed back' data to instructors and/or learners…
Descriptors: MOOCs, Learning Analytics, Instructional Design, Student Role