What is he best known for?
The fields of study he is best known for:
- The Internet
- Artificial intelligence
- Social science
Argumentation theory, Design rationale, World Wide Web, Knowledge management and Data science are his primary areas of study.
His research is interdisciplinary, bridging the disciplines of Usability and Argumentation theory.
His research integrates issues of Ontology and Sensemaking in his study of World Wide Web.
His Knowledge management study combines topics from a wide range of disciplines, such as Participatory design and Educational technology.
His biological study focuses on Analytics.
His Analytics research is multidisciplinary, incorporating perspectives in Cultural analytics and Learning analytics.
His most cited work include:
- Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making (425 citations)
- Social learning analytics (331 citations)
- Argumentation-based design rationale (255 citations)
What are the main themes of his work throughout his whole career to date?
Simon Buckingham Shum focuses on Knowledge management, Learning analytics, Analytics, World Wide Web and Data science.
His Knowledge management research integrates issues from Experiential learning and Argumentation theory.
His Learning analytics study incorporates themes from Pedagogy, Formative assessment and Learning sciences.
Simon Buckingham Shum has researched Analytics in several fields, including Cultural analytics, Reflective writing, Human–computer interaction, Teamwork and Critical thinking.
The Usability research Simon Buckingham Shum does as part of his general Human–computer interaction study is frequently linked to other disciplines of science, such as Notation, therefore creating a link between diverse domains of science.
His work on Hypertext and The Internet as part of general World Wide Web research is often related to Digital library and Publishing, thus linking different fields of science.
He most often published in these fields:
- Knowledge management (30.97%)
- Learning analytics (23.89%)
- Analytics (20.35%)
What were the highlights of his more recent work (between 2017-2021)?
- Learning analytics (23.89%)
- Analytics (20.35%)
- Data science (18.14%)
In recent papers he was focusing on the following fields of study:
His main research concerns Learning analytics, Analytics, Data science, Human–computer interaction and Teamwork.
His Learning analytics study integrates concerns from other disciplines, such as Participatory design, Visualization and Conceptual model.
He combines subjects such as Academic writing and Learning design with his study of Analytics.
As part of one scientific family, Simon Buckingham Shum deals mainly with the area of Data science, narrowing it down to issues related to the Design process, and often Data modeling.
His Human–computer interaction research focuses on Set and how it connects with Software architecture, Personalized learning, Learning theory and Communication design.
His biological study deals with issues like Workflow, which deal with fields such as Task, Nursing, Emerging technologies and Interactive visualization.
Between 2017 and 2021, his most popular works were:
- Human-Centred Learning Analytics (32 citations)
- Designing Academic Writing Analytics for Civil Law Student Self-Assessment (27 citations)
- Embracing imperfection in learning analytics (26 citations)
In his most recent research, the most cited papers focused on:
- The Internet
- Artificial intelligence
- Social science
Learning analytics, Analytics, Data science, Human–computer interaction and Set are his primary areas of study.
His work focuses on many connections between Learning analytics and other disciplines, such as Conceptual model, that overlap with his field of interest in Personalization, Software architecture and Personalized learning.
His Analytics study frequently links to other fields, such as Formative assessment.
His study looks at the intersection of Data science and topics like Participatory design with Design process.
His work on Multimodal interaction as part of general Human–computer interaction study is frequently connected to Interpretation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Set research includes themes of Empirical evidence, Communication design and Learning theory.
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