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Pankaj Chejara; Reet Kasepalu; Luis P. Prieto; María Jesús Rodríguez-Triana; Adolfo Ruiz Calleja; Bertrand Schneider – British Journal of Educational Technology, 2024
Multimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings…
Descriptors: Cooperation, Learning Activities, Models, Learning Modalities
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
Li, Xu; Ouyang, Fan; Chen, WenZhi – Journal of Computing in Higher Education, 2022
Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students' characteristics within a group. But there are two dominant challenges…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Group Dynamics, Grouping (Instructional Purposes)
Lämsä, Joni; Uribe, Pablo; Jiménez, Abelino; Caballero, Daniela; Hämäläinen, Raija; Araya, Roberto – Journal of Learning Analytics, 2021
Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education…
Descriptors: Cooperative Learning, Computer Assisted Instruction, Synchronous Communication, Learning Analytics
An Investigation of High School Students' Errors in Introductory Programming: A Data-Driven Approach
Qian, Yizhou; Lehman, James – Journal of Educational Computing Research, 2020
This study implemented a data-driven approach to identify Chinese high school students' common errors in a Java-based introductory programming course using the data in an automated assessment tool called the Mulberry. Students' error-related behaviors were also analyzed, and their relationships to success in introductory programming were…
Descriptors: High School Students, Error Patterns, Introductory Courses, Computer Science Education
Kuromiya, Hiroyuki; Majumdar, Rwitajit; Ogata, Hiroaki – Educational Technology & Society, 2020
Evidence-based education has become more relevant in the current technology-enhanced teaching-learning era. This paper introduces how Educational BIG data has the potential to generate such evidence. As evidence-based education traditionally hooks on the meta-analysis of the literature, so there are existing platforms that support manual input of…
Descriptors: Evidence Based Practice, Case Studies, Learning Analytics, Data Collection
Spector, Michael, Ed.; Kumar, Vivekanandan, Ed.; Essa, Alfred, Ed.; Huang, Yueh-Min, Ed.; Koper, Rob, Ed.; Tortorella, Richard A. W., Ed.; Chang, Ting-Wen, Ed.; Li, Yanyan, Ed.; Zhang, Zhizhen, Ed. – Lecture Notes in Educational Technology, 2018
This book demonstrates teachers' and learners' experiences with big data in education; education and cloud computing; and new technologies for teacher support. It also discusses the advantages of using these frontier technologies in teaching and learning and predicts the future challenges. As such, it enables readers to better understand how…
Descriptors: Educational Technology, Technological Advancement, Data Use, Technology Uses in Education