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Engagement data of robotic students in a synchronous-hybrid course.

ABSTRACT: This article includes data on the engagement behaviors of seven robotically-embodied graduate students who attended class with an on-campus instructor, three robotically-embodied classmates, and two physically-embodied classmates; the classmates were not part of the study [1]. Purposive sampling was used to collect data from the target population of robot users. The present data were collected through video recordings of a class session and an online survey. Initial coding of the students' actions was based on an adaptation of the Telepresence and Engagement Measurement Scale (TEMS) [2], which was based on the National Survey of Student Engagement [3,4]. Thematic analysis was used to identify the potential determinants of robotic students' engagement. From these observations, we discovered that robotic students used their bodies differently than what they self-report, and their behaviors were dependent on classroom structure and actions of the instructor.

SUBMITTER: Lei M

PROVIDER: S-EPMC6527919 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Engagement data of robotic students in a synchronous-hybrid course.

Lei Ming M Clemente Ian M IM Hu Ying Y

Data in brief 20190429

This article includes data on the engagement behaviors of seven robotically-embodied graduate students who attended class with an on-campus instructor, three robotically-embodied classmates, and two physically-embodied classmates; the classmates were not part of the study [1]. Purposive sampling was used to collect data from the target population of robot users. The present data were collected through video recordings of a class session and an online survey. Initial coding of the students' actio ...[more]

PMID: 31193436

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Project description:BackgroundSystems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.ResultsIn order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.ConclusionsOur SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Dataset Information

Engagement data of robotic students in a synchronous-hybrid course.

Publications

Engagement data of robotic students in a synchronous-hybrid course.

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