What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Education
- The Internet
His primary scientific interests are in Social psychology, Mathematics education, Pedagogy, Order and Personalization.
His studies in Social psychology integrate themes in fields like Applied psychology and Media relations.
His Mathematics education research incorporates elements of Variety and Knowledge acquisition.
The various areas that Michail N. Giannakos examines in his Order study include Theory of planned behavior, Social media and Popularity.
His work in Personalization addresses issues such as Advertising, which are connected to fields such as Marketing, Purchasing, Hedonic motivation and Product.
Learning analytics is closely connected to Learning sciences in his research, which is encompassed under the umbrella topic of Qualitative comparative analysis.
His most cited work include:
- Big data analytics capabilities: a systematic literature review and research agenda (166 citations)
- Using social media for work: Losing your time or improving your work? (150 citations)
- Explaining online shopping behavior with fsQCA: The role of cognitive and affective perceptions (148 citations)
What are the main themes of his work throughout his whole career to date?
Michail N. Giannakos spends much of his time researching Multimedia, Mathematics education, Knowledge management, Learning analytics and Analytics.
As part of the same scientific family, Michail N. Giannakos usually focuses on Multimedia, concentrating on Software and intersecting with Scratch.
His work focuses on many connections between Mathematics education and other disciplines, such as Pedagogy, that overlap with his field of interest in Field.
His research investigates the connection with Knowledge management and areas like Qualitative comparative analysis which intersect with concerns in Order.
His Learning analytics research is under the purview of Data science.
The concepts of his Analytics study are interwoven with issues in Educational technology, World Wide Web, Personalization and Human–computer interaction.
He most often published in these fields:
- Multimedia (18.52%)
- Mathematics education (17.28%)
- Knowledge management (14.40%)
What were the highlights of his more recent work (between 2019-2021)?
- Data science (10.70%)
- Eye tracking (6.58%)
- Artificial intelligence (5.35%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Data science, Eye tracking, Artificial intelligence, Human–computer interaction and Cognitive psychology.
His Analytics and Learning analytics study in the realm of Data science connects with subjects such as Thematic map.
Michail N. Giannakos works mostly in the field of Eye tracking, limiting it down to concerns involving Gaze and, occasionally, Massive open online course.
Michail N. Giannakos works mostly in the field of Artificial intelligence, limiting it down to topics relating to Machine learning and, in certain cases, E-commerce and Word error rate, as a part of the same area of interest.
In general Human–computer interaction study, his work on Ubiquitous computing often relates to the realm of Wearable technology, thereby connecting several areas of interest.
His research in Bibliometrics intersects with topics in Mathematics education and Field.
Between 2019 and 2021, his most popular works were:
- Identifying the combinations of motivations and emotions for creating satisfied users in SNSs: An fsQCA approach (15 citations)
- Computational thinking education: Issues and challenges (13 citations)
- Mapping child–computer interaction research through co-word analysis (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Education
- The Internet
His primary areas of investigation include Eye tracking, Coding, Artificial intelligence, Data science and Peer review.
Michail N. Giannakos has researched Eye tracking in several fields, including User-generated content, Marketing, Gaze and Popularity.
His study on Random forest is often connected to Stimulus, Area of interest and Context as part of broader study in Artificial intelligence.
His Data science research incorporates themes from Participatory design and Child computer interaction.
His Social psychology course of study focuses on Qualitative comparative analysis and Adaptive learning.
His study in Cognitive psychology is interdisciplinary in nature, drawing from both Quality, Educational technology and Facial expression.
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.
