What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Software
His primary scientific interests are in Learning analytics, Data science, Educational technology, Ontology and Knowledge management.
His Learning analytics research includes elements of Social learning, Educational research, Academic achievement, Analytics and Blended learning.
His Data science study combines topics in areas such as Social network analysis, Collaborative learning, Synchronous learning and Context.
His Educational technology study combines topics in areas such as Experiential learning, Active learning, Algorithmic learning theory and Human–computer interaction.
His Ontology research is multidisciplinary, incorporating perspectives in Programming language, Unified Modeling Language and World Wide Web.
His Knowledge management study integrates concerns from other disciplines, such as E-learning, Conceptual model, Strategic planning and Distance education.
His most cited work include:
- Let’s not forget: Learning analytics are about learning (349 citations)
- Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (247 citations)
- Current state and future trends: a citation network analysis of the learning analytics field (230 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Learning analytics, Data science, World Wide Web, Knowledge management and Software engineering.
Dragan Gašević interconnects Mathematics education, Educational technology, Self-regulated learning, Blended learning and Analytics in the investigation of issues within Learning analytics.
Particularly relevant to Educational data mining is his body of work in Data science.
His World Wide Web research incorporates elements of Ontology and Context.
His Ontology research incorporates themes from Ontology and Learning object.
His studies examine the connections between Software engineering and genetics, as well as such issues in Software product line, with regards to Data mining.
He most often published in these fields:
- Learning analytics (35.30%)
- Data science (18.30%)
- World Wide Web (17.19%)
What were the highlights of his more recent work (between 2017-2021)?
- Learning analytics (35.30%)
- Data science (18.30%)
- Mathematics education (9.80%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Learning analytics, Data science, Mathematics education, Self-regulated learning and Artificial intelligence.
His research in Learning analytics intersects with topics in Context, Process mining, Knowledge management, Flipped classroom and Analytics.
The concepts of his Data science study are interwoven with issues in Social network analysis, Learning theory, Learning sciences and Interpersonal ties.
His work on Massive open online course, Student learning, Learning Management and Educational technology as part of his general Mathematics education study is frequently connected to Community of inquiry, thereby bridging the divide between different branches of science.
His research in Self-regulated learning tackles topics such as Time management which are related to areas like Blended learning.
His biological study spans a wide range of topics, including Machine learning and Natural language processing.
Between 2017 and 2021, his most popular works were:
- Using Learning Analytics to Scale the Provision of Personalised Feedback. (69 citations)
- SENS: Network analytics to combine social and cognitive perspectives of collaborative learning (49 citations)
- SENS: Network analytics to combine social and cognitive perspectives of collaborative learning (49 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Software
The scientist’s investigation covers issues in Learning analytics, Mathematics education, Data science, Massive open online course and Community of inquiry.
His Learning analytics research is multidisciplinary, relying on both Knowledge management, Self-regulated learning, Educational technology, Blended learning and Analytics.
His studies deal with areas such as Individualized instruction and Software deployment as well as Knowledge management.
Dragan Gašević has included themes like Computer-mediated communication and Student engagement in his Educational technology study.
His Mathematics education study incorporates themes from Salience, Theme, Sensemaking and Social comparison theory.
His Data science research is multidisciplinary, incorporating elements of Social network analysis, Replication, Learning theory, Construct and Interpersonal ties.
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