Automated Author ProfilePapavlasopoulou, Sofia
Papavlasopoulou, Sofia
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The interest in computing learning activities is continuously increasing, keeping pace with the evolution of educational technologies. This rise in engagement is evident within approaches grounded in constructivism and constructionism, which provide an ideal setting for the spectrum of skills, literacies, and competencies that global initiatives capitalise on in K–12 education. Due to recent advancements in technology and societal changes, we are addressing the need for an updated perspective focussed on designing and implementing computational activities. To achieve this, we have analysed forty-six peer-reviewed empirical studies from the last decade to summarise technology-supported learning strategies' current direction and future potential. The main findings describe programming software and IoT as the preferred digital tools, often presented in novel combinations, extensions, and contextualisations. Implementation primarily occurs within the domain of computer science but exhibits a high degree of interdisciplinarity. Furthermore, there is an increase in activities referring to real-life scenarios, encouraging learners to develop computational skills beyond the classroom and expand their digital awareness through interaction with emerging technologies. We leverage these trends and significant patterns by proposing a summary of focal points for future research and practice, with a focus on activity design.
Authors
- Possaghi, Isabella ;
- Papavlasopoulou, Sofia
The interest in computing learning activities is continuously increasing, keeping pace with the evolution of educational technologies. This rise in engagement is evident within approaches grounded in constructivism and constructionism, which provide an ideal setting for the spectrum of skills, literacies, and competencies that global initiatives capitalise on in K–12 education. Due to recent advancements in technology and societal changes, we are addressing the need for an updated perspective focussed on designing and implementing computational activities. To achieve this, we have analysed forty-six peer-reviewed empirical studies from the last decade to summarise technology-supported learning strategies' current direction and future potential. The main findings describe programming software and IoT as the preferred digital tools, often presented in novel combinations, extensions, and contextualisations. Implementation primarily occurs within the domain of computer science but exhibits a high degree of interdisciplinarity. Furthermore, there is an increase in activities referring to real-life scenarios, encouraging learners to develop computational skills beyond the classroom and expand their digital awareness through interaction with emerging technologies. We leverage these trends and significant patterns by proposing a summary of focal points for future research and practice, with a focus on activity design.
Authors
- Possaghi, Isabella ;
- Papavlasopoulou, Sofia