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Students' engagement with real-time graphs in CSCL settings: scrutinizing the role of teacher support.

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Title: Students' engagement with real-time graphs in CSCL settings: scrutinizing the role of teacher support.
Authors: Ingulfsen, Line1 line.ingulfsen@ils.uio.no, Furberg, Anniken1, Strømme, Torunn Aanesland1
Source: International Journal of Computer-Supported Collaborative Learning. Dec2018, Vol. 13 Issue 4, p365-390. 26p.
Subject Terms: *Collaborative learning, *Teacher-student relationships, *Computer software, Computer-supported cooperative work, Ocean acidification
Abstract: This paper reports on a study of teacher support in experimental computer-supported collaborative learning (CSCL) settings where students engage with graphs in real-time labs within the context of school science. Real-time labs are digital devices and software connected to student-controlled sensors or probes that can measure and visualize data graphically. The empirical setting was a science project about ocean acidification (OA) where lower secondary school students conducted measurements of the pH value of water with increased concentrations of CO2. The analytical focus is on student-teacher interaction during group-work activities where the students carried out, reviewed and reported on the real-time lab experiment. The analyses show that students needed additional support from the teacher in interpreting the real-time graphs and in making connections between the graphic representation, the practical undertakings of the experiment and the underlying scientific phenomena. Most importantly, the study demonstrates the complexity of teacher support in CSCL settings and how this type of support intersects with the support provided by digital resources, peer collaboration and applied instructional design. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Computer-Supported Collaborative Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer-Supported+Collaborative+Learning%22">International Journal of Computer-Supported Collaborative Learning</searchLink>. Dec2018, Vol. 13 Issue 4, p365-390. 26p.
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  Data: This paper reports on a study of teacher support in experimental computer-supported collaborative learning (CSCL) settings where students engage with graphs in real-time labs within the context of school science. Real-time labs are digital devices and software connected to student-controlled sensors or probes that can measure and visualize data graphically. The empirical setting was a science project about ocean acidification (OA) where lower secondary school students conducted measurements of the pH value of water with increased concentrations of CO2. The analytical focus is on student-teacher interaction during group-work activities where the students carried out, reviewed and reported on the real-time lab experiment. The analyses show that students needed additional support from the teacher in interpreting the real-time graphs and in making connections between the graphic representation, the practical undertakings of the experiment and the underlying scientific phenomena. Most importantly, the study demonstrates the complexity of teacher support in CSCL settings and how this type of support intersects with the support provided by digital resources, peer collaboration and applied instructional design. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Computer-Supported Collaborative Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2018
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