Applying Learning Analytics to Support Instruction
The Society for Learning Analytics Research (SOLAR) defines learning analytics as the collection, analysis and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs. In this chapter, we present four case...
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| Vydané v: | Score Reporting Research and Applications s. 126 - 144 |
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| Jazyk: | English |
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2019
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| Abstract | The Society for Learning Analytics Research (SOLAR) defines learning analytics as the collection, analysis and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs. In this chapter, we present four case studies where analytics were used to make sense of data from a variety of technologies to discover and interpret student learning processes and measure students' performances. Data products were developed for practitioners in an effort to inform their instructional practices and to identify areas and strategies for improvement. The case studies vary in context, subjects, focal constructs, analytical approaches, format of data collected, and student learning tasks. The context of the studies ranges from middle school to community college students. The constructs being measured in these case studies include both learning outcomes and learning processes. The data being analyzed ranges from traditional log data of student's individually working in learning systems, to more open-ended programming process data, to speech data of students who worked collaboratively with peers. This chapter examines how the needs of practitioners shaped the work, the processes undertaken to develop data products, and the ways in which data products were ultimately used by stakeholders. |
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| AbstractList | The Society for Learning Analytics Research (SOLAR) defines learning analytics as the collection, analysis and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs. In this chapter, we present four case studies where analytics were used to make sense of data from a variety of technologies to discover and interpret student learning processes and measure students' performances. Data products were developed for practitioners in an effort to inform their instructional practices and to identify areas and strategies for improvement. The case studies vary in context, subjects, focal constructs, analytical approaches, format of data collected, and student learning tasks. The context of the studies ranges from middle school to community college students. The constructs being measured in these case studies include both learning outcomes and learning processes. The data being analyzed ranges from traditional log data of student's individually working in learning systems, to more open-ended programming process data, to speech data of students who worked collaboratively with peers. This chapter examines how the needs of practitioners shaped the work, the processes undertaken to develop data products, and the ways in which data products were ultimately used by stakeholders. |
| Author | Feng, Mingyu Grover, Shuchi Krumm, Andrew |
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| Keywords | Common Wrong Answers Productive Persistence Educational Data Mining Zealand Primary School Teachers Data Intensive Analyses Heat Map Analyses Large Scale Efficacy Trial Applying Learning Analytics MFT Information Visualization Professional Development General Diagnostic Model Online Learning System Research Practice Partnership Unsupervised Machine Learning Von Davier CT Homework Review Collect Student Performance Data LA Administrative Data Systems Digital Learning Environments Data Intensive Research Improve Learning Environments Large Scale Testing Programs |
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| Title | Applying Learning Analytics to Support Instruction |
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