Using the Science Writing Heuristic as a Tool for Learning from Laboratory Investigations in Secondary Science     

Carolyn W. Keys  Brian Hand  Vaughn Prain  Susan Collins 《科学教学研究杂志》1999,36(10):1065-1084

This article presents and discusses preliminary research on a new heuristic tool for learning from laboratory activities in secondary science. The tool, called the science writing heuristic, can be used by teachers as a framework from which to design classroom activities. Theoretically, the science writing heuristic represents a bridge between traditional laboratory reports and types of writing that promote personal construction of meaning. Two eighth‐grade classes participated in using the science writing heuristic during an 8‐week stream study. The teacher and one of the researchers collaboratively developed activities based on the science writing heuristic that the teacher implemented. Nineteen target students were studied in depth. Characteristics of report writing and students' understanding of the nature of science were investigated, using interpretive techniques. There is evidence that use of the science writing heuristic facilitated students to generate meaning from data, make connections among procedures, data, evidence, and claims, and engage in metacognition. Students' vague understandings of the nature of science at the beginning of the study were modified to more complex, rich, and specific understandings. The implications of the study for writing in science classrooms is discussed. © 1999 John Wiley & Sons, Inc. J Res Sci Teach 36: 1065–1084, 1999  相似文献   

Language as an indicator of meaning generation: An analysis of middle school students' written discourse about scientific investigations     

Carolyn W. Keys 《科学教学研究杂志》1999,36(9):1044-1061

The purposes of this study were to (a) investigate how one group of middle school students generated meanings for scientific data and expressed them in writing, and (b) develop a methodology for assessing the relationship between students' written text and their scientific thinking. Previous research on writing to learn has focused on the reformulation of content material supplied by the teacher, rather than authentic inquiry data that provide opportunities for meaningful interpretation. The research design was a content analysis of documents produced in a naturalistic setting. Data analysis focused on the frequency, placement, and purpose of meaningful inferences embedded in the reports, as well as coding for the elaboration, extension, and enhancement of science ideas. Results indicated that many student reports included a minimal number of written inferences, expressing only vague meanings for the data. However, some students integrated inference and data, using inference to (a) explain specific meanings for data points in context, and (b) pose new hypotheses to explain data. An analysis of expansion characteristics revealed that some students were able to elaborate richly upon their initial ideas through the use of language, thereby generating new meaning for the investigations. © 1999 John Wiley & Sons, Inc. J Res Sci Teach 36: 1044–1061, 1999  相似文献   

Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics     

Andy Nguyen  Sanna Järvelä  Carolyn Rosé  Hanna Järvenoja  Jonna Malmberg 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):293-312

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.

Practitioner notes

What is already known about this topic

• Socially shared regulation has been recognised as an essential aspect of collaborative learning success.
• It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings.
• Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.

What this paper adds

• Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.
• Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.
• Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.
• Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.

Implications for practice and/or policy

• Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.
• This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.

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