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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
Zheng, Lanqin; Long, Miaolang; Chen, Bodong; Fan, Yunchao – International Journal of Educational Technology in Higher Education, 2023
Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated…
Descriptors: Learning Analytics, Computer Assisted Testing, Cooperative Learning, Graphs
Milat, Iness Nedji; Seridi, Hassina; Moudjari, Abdelkader – International Journal of Distance Education Technologies, 2020
Recently, discovering learner behaviour has taken more attention in the field of e-learning. It aims to gain useful insights into the learning process of students despite the absence of direct interaction with teachers. In fact, the only available source of information in such environments is the log file that represents all possible interactions…
Descriptors: Student Behavior, Behavior Patterns, Electronic Learning, Learning Analytics
Danielle S. McNamara; Tracy Arner; Elizabeth Reilley; Paul Alvarado; Chani Clark; Thomas Fikes; Annie Hale; Betheny Weigele – Grantee Submission, 2022
Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…
Descriptors: Electronic Learning, Educational Technology, Networks, Learning Analytics
Hernández-Lara, Ana Beatriz; Perera-Lluna, Alexandre; Serradell-López, Enric – Education & Training, 2021
Purpose: With the growth of digital education, students increasingly interact in a variety of ways. The potential effects of these interactions on their learning process are not fully understood and the outcomes may depend on the tool used. This study explores the communication patterns and learning effectiveness developed by students using two…
Descriptors: Game Based Learning, Learning Analytics, Computer Mediated Communication, Asynchronous Communication
Máverick André; Anderson Pinheiro; Vitor Rolim; Rafael Dueire Lins; Taciana Pontual Falcão; Rafael Ferreira Mello – Computer-Based Learning in Context, 2022
Learning Management Systems (LSM) have been adopted to provide interaction between instructors and students in online learning courses. In these environments, large volumes of messages are exchanged, making it difficult for the participants to follow all the discussions. When messages are ignored, students' opportunities to effectively construct…
Descriptors: Learning Analytics, Computer Mediated Communication, Discussion Groups, Portuguese
Danielle S. McNamara; Laura K. Allen; Scott A. Crossley; Mihai Dascalu; Cecile A. Perret – Grantee Submission, 2017
Language is of central importance to the field of education because it is a conduit for communicating and understanding information. Therefore, researchers in the field of learning analytics can benefit from methods developed to analyze language both accurately and efficiently. Natural language processing (NLP) techniques can provide such an…
Descriptors: Natural Language Processing, Learning Analytics, Educational Technology, Automation
Feng, Shihui; Law, Nancy – International Journal of Artificial Intelligence in Education, 2021
In this study, we review 1830 research articles on artificial intelligence in education (AIED), with the aim of providing a holistic picture of the knowledge evolution in this interdisciplinary research field from 2010 to 2019. A novel three-step approach in the analysis of the keyword co-occurrence networks (KCN) is proposed to identify the…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Research, Intelligent Tutoring Systems

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