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Fan, Yizhou; Matcha, Wannisa; Uzir, Nora'ayu Ahmad; Wang, Qiong; Gaševic, Dragan – International Journal of Artificial Intelligence in Education, 2021
The importance of learning design in education is widely acknowledged in the literature. Should learners make effective use of opportunities provided in a learning design, especially in online environments, previous studies have shown that they need to have strong skills for self-regulated learning (SRL). The literature, which reports the use of…
Descriptors: Learning Analytics, Instructional Design, Independent Study, Multivariate Analysis
Lodge, Jason M.; Alhadad, Sakinah S. J.; Lewis, Melinda J.; Gaševic, Dragan – Technology, Knowledge and Learning, 2017
The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate…
Descriptors: Statistical Inference, Data Interpretation, Interdisciplinary Approach, Higher Education
Mangaroska, Katerina; Sharma, Kshitij; Gaševic, Dragan; Giannakos, Michalis – Journal of Learning Analytics, 2020
Programming is a complex learning activity that involves coordination of cognitive processes and affective states. These aspects are often considered individually in computing education research, demonstrating limited understanding of how and when students learn best. This issue confines researchers to contextualize evidence-driven outcomes when…
Descriptors: Learning Analytics, Data Collection, Instructional Design, Learning Modalities
Lim, Lisa-Angelique; Dawson, Shane; Gaševic, Dragan; Joksimovic, Srecko; Fudge, Anthea; Pardo, Abelardo; Gentili, Sheridan – Australasian Journal of Educational Technology, 2020
Although technological advances have brought about new opportunities for scaling feedback to students, there remain challenges in how such feedback is presented and interpreted. There is a need to better understand how students make sense of such feedback to adapt self-regulated learning processes. This study examined students' sense-making of…
Descriptors: Individualized Instruction, Learning Analytics, Data Collection, Student Attitudes
Gaševic, Dragan; Dawson, Shane; Siemens, George – TechTrends: Linking Research and Practice to Improve Learning, 2015
The analysis of data collected from the interaction of users with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new research field, learning analytics, and its closely related discipline, educational…
Descriptors: Data Collection, Educational Research, Data Analysis, Information Technology
Siadaty, Melody; Gaševic, Dragan; Hatala, Marek – Journal of Learning Analytics, 2016
To keep pace with today's rapidly growing knowledge-driven society, productive self-regulation of one's learning processes are essential. We introduce and discuss a trace-based measurement protocol to measure the effects of scaffolding interventions on self-regulated learning (SRL) processes. It guides tracing of learners' actions in a learning…
Descriptors: Metacognition, Learning Processes, Intervention, Technology Uses in Education
Gaševic, Dragan; Jovanovic, Jelena; Pardo, Abelardo; Dawson, Shane – Journal of Learning Analytics, 2017
The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper…
Descriptors: Foreign Countries, Undergraduate Students, Engineering Education, Educational Research
Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan – Journal of Learning Analytics, 2014
Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…
Descriptors: Data Collection, Data Analysis, Educational Research, Interaction
Pechenizkiy, Mykola; Gaševic, Dragan – Journal of Learning Analytics, 2014
This section offers a compilation of 16 extended abstracts summarizing research of the doctoral students who participated in the Second Learning Analytics Summer Institute (LASI 2014) held at Harvard University in July 2014. The abstracts highlight the motivation, main goals and expected contributions to the field from the ongoing learning…
Descriptors: Educational Research, Data Collection, Data Analysis, Doctoral Programs
Jo, Yohan; Tomar, Gaurav; Ferschke, Oliver; Rosé, Carolyn P.; Gaševic, Dragan – International Educational Data Mining Society, 2016
An important research problem for Educational Data Mining is to expedite the cycle of data leading to the analysis of student learning processes and the improvement of support for those processes. For this goal in the context of social interaction in learning, we propose a three-part pipeline that includes data infrastructure, learning process…
Descriptors: Information Retrieval, Learning Processes, Interaction, Interpersonal Relationship
Macfadyen, Leah P.; Dawson, Shane; Pardo, Abelardo; Gaševic, Dragan – Research & Practice in Assessment, 2014
In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real-time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self-regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the…
Descriptors: Network Analysis, Data Analysis, Data Collection, Educational Assessment