Progression of Cognitive-Affective States During Learning in Kindergarteners
Bringing Together Physiological, Observational and Performance Data
Bibliographic Data
| ID | 22042110 |
|---|---|
| Authors | Priyashri Kamlesh Sridhar (University of Auckland, corresponding author), Suranga Nanayakkara (0000-0001-7441-5493, University of Auckland, corresponding author) |
| Year | 2020 |
| Volume | 10 |
| Issue | 7 |
| Pages | 177 |
| Publication date | 2020-07-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci10070177 |
| OpenAlex | W3040472773 |
| Language | EN |
| References cited | 49 |
It has been shown that combining data from multiple sources, such as observations, self-reports, and performance with physiological markers offers better insights into cognitive-affective states during the learning process. Through a study with 12 kindergarteners, we explore the role of utilizing insights from multiple data sources, as a potential arsenal to supplement and complement existing assessments methods in understanding cognitive-affective states across two main pedagogical approaches—constructionist and instructionist—as children explored learning a chosen Science, Technology, Engineering and Mathematics (STEM) concept. We present the trends that emerged across pedagogies from different data sources and illustrate the potential value of additional data channels through case illustrations. We also offer several recommendations for such studies, particularly when collecting physiological data, and summarize key challenges that provide potential avenues for future work
Cognition · Cognitive psychology · Cognitive science · Constructionism · Data science · Developmental psychology · Epistemology · Machine learning · Observational study · Strict constructionism · Computer Science · Innovative Teaching and Learning Methods · Intelligent Tutoring Systems and Adaptive Learning · Neuroscience, Education and Cognitive Function · Psychology
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| Citation velocity | historical |
|---|---|
| Highly cited | No |