D-Index & Metrics Best Publications
Social Sciences and Humanities
Australia
2023
Computer Science
Australia
2023

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 68 Citations 16,263 512 World Ranking 1323 National Ranking 31
Social Sciences and Humanities D-index 64 Citations 14,698 456 World Ranking 680 National Ranking 49

Research.com Recognitions

Awards & Achievements

2023 - Research.com Computer Science in Australia Leader Award

2023 - Research.com Social Sciences and Humanities in Australia Leader Award

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Let’s not forget: Learning analytics are about learning

Dragan Gašević;Shane Dawson;George Siemens.
Techtrends (2015)

851 Citations

Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success

Dragan Gašević;Shane Dawson;Tim Rogers;Danijela Gasevic.
Internet and Higher Education (2016)

554 Citations

Where is Research on Massive Open Online Courses Headed? A Data Analysis of the MOOC Research Initiative

Dragan Gaševic;Vitomir Kovanovic;Vitomir Kovanovic;Srecko Joksimovic;Srecko Joksimovic;George Siemens.
The International Review of Research in Open and Distributed Learning (2014)

434 Citations

Model Driven Engineering and Ontology Development

Dragan Gasevic;Dragan Djuric;Vladan Devedzic.
(2009)

425 Citations

Preparing for the Digital University: A Review of the History and Current State of Distance, Blended and Online Learning

George Siemens;George Siemens;Dragan Gašević;Shane Dawson.
(2015)

360 Citations

Guest Editorial-Learning and Knowledge Analytics

George Siemens;Dragan Gasevic.
Educational Technology & Society (2012)

318 Citations

Open Learning Analytics: an integrated modularized platform

George Siemens;Dragan Gašević;Caroline Haythornthwaite;Shane Dawson.
(2011)

305 Citations

Current state and future trends: a citation network analysis of the learning analytics field

Shane Dawson;Dragan Gašević;George Siemens;Srecko Joksimovic.
learning analytics and knowledge (2014)

278 Citations

Learning analytics to unveil learning strategies in a flipped classroom

Jelena Jovanović;Dragan Gašević;Shane Dawson;Abelardo Pardo.
Internet and Higher Education (2017)

267 Citations

A qualitative evaluation of evolution of a learning analytics tool

Liaqat Ali;Marek Hatala;Dragan Gašević;Jelena Jovanović.
Computer Education (2012)

264 Citations

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