A5022503004- Online Learning and Analytics
- scientometrics and bibliometrics research
- E-Learning and Knowledge Management
- Data Visualization and Analytics
- Meta-analysis and systematic reviews
- Health, Environment, Cognitive Aging
- Genetics, Bioinformatics, and Biomedical Research
- Computational Physics and Python Applications
- Bioinformatics and Genomic Networks
- Online and Blended Learning
- Health Policy Implementation Science
- Biomedical and Engineering Education
- Data-Driven Disease Surveillance
- Data Analysis with R
- Biomedical Text Mining and Ontologies
- Educational Innovations and Technology
- Scientific Computing and Data Management
- Health and Medical Research Impacts
- Data Quality and Management
- Single-cell and spatial transcriptomics
- Research Data Management Practices
- Statistics Education and Methodologies
- Cell Image Analysis Techniques
- Complex Systems and Decision Making
Indiana University Bloomington
2016-2025
Indiana University
2024
Boeing (United States)
2019-2020
Boeing (Australia)
2019-2020
In the information age, ability to read and construct data visualizations becomes as important write text. However, while standard definitions theoretical frameworks teach assess textual, mathematical, visual literacy exist, current visualization (DVL) are not comprehensive enough guide design of DVL teaching assessment. This paper introduces a framework (DVL-FW) that was specifically developed define, teach, DVL. The holistic DVL-FW promotes both reading construction visualizations, pairing...
Learning analytics and visualizations make it possible to examine communicate learners' engagement, performance, trajectories in online courses evaluate optimize course design for learners. This is particularly valuable workforce training involving employees who need acquire new knowledge the most effective manner. paper introduces a set of metrics that aim capture key dynamical aspects learner trajectories. The are applied identify prototypical behavior learning pathways through...
Abstract The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Reference (HRA) of the healthy adult body. Experts from 20+ consortia collaborate develop Common Coordinate Framework (CCF), knowledge graphs and tools that describe multiscale structure human body (from organs tissues down cells, genes biomarkers) use HRA characterize changes occur with aging, disease other perturbations. v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types 2,089 biomarkers (such as...
Background Alzheimer's disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about results impact of ADNI-related studies. In this work, we employed advanced visualization techniques to perform comprehensive systematic mapping scientific growth period 12 years. Methods Citation publications from 01/01/2003 05/12/2015 were...
This article introduces work in progress to develop a new, open biomedical map of science (OBMS) using the PubMed citation database. The new represents bimodal network relationships between journals and medical subject heading (MeSH) descriptors, based on journal's articles indexed MEDLINE component PubMed. We review current efforts use data studies mapping. As part development process, we compare with included 2011 UCSD establish baseline disciplinary coverage for period 2009–2019. Journal...
Effective adoption of online platforms for teaching, learning, and skill development is essential to both academic institutions workplaces. Adoption learning has been abruptly accelerated by COVID19 pandemic, drawing attention research on pedagogy practice effective instruction. Online requires a multitude skills resources spanning from management interactive assessment tools, combined with multimedia content, presenting challenges instructors organizations. This study focuses ways that...
Scientific research is a major driving force in knowledge based economy. Income, health and wellbeing depend on scientific progress. The better we understand the inner workings of enterprise, can prompt, manage, steer, utilize Diverse indicators approaches exist to evaluate monitor activities, from calculating reputation researcher, institution, or country analyzing visualizing global brain circulation. However, there are very few predictive models science that used by key decision makers...
The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) provides genetic resources and facilitate research opportunities in qualitative quantitative genetics using ADNI multidimensional phenotypes. Genotyping sequencing data have been generated for ADNI-1/GO/2 participants, are available to scientific community. Here we visualize evaluate growth impact studies published from 2008 through 2015. A Scopus publication search, keywords related both genetics, was performed...
The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) provides genetic resources and facilitate research opportunities in qualitative quantitative genetics using ADNI multidimensional phenotypes. Genotyping sequencing data have been generated for ADNI-1/GO/2 participants, are available to scientific community. Here we visualize evaluate growth impact studies published from 2008 through 2015. A Scopus publication search, keywords related both genetics, was performed...
The study focuses on how learning sciences and visual analytics can be used to design, improve, online workforce training in advanced manufacturing.We analyzed the data from a cohort of 900 professionals enrolled an course regarding additive manufacturing.The results inform strategies for instructors better align assignments, objectives, assessment measures argues synchronized structure use across platforms.
The edX Student and Course Analytics Visualization Pipeline is analytics visualization pipeline using course database user logs, written in R to 1) extract process student users performance data, structures event logs; 2) create learner trajectory networks of use pathways through content activity modules; 3) analyze the students 4) aggregate interaction measurements for a given course.
The edX Student and Course Analytics Visualization Pipeline is analytics visualization pipeline using course database user logs, written in R to 1) extract process student users performance data, structures event logs; 2) create learner trajectory networks of use pathways through content activity modules; 3) analyze the students 4) aggregate interaction measurements for a given course.
The edX Student and Course Analytics Visualization Pipeline is analytics visualization pipeline using course database user logs, written in R to 1) extract process student users performance data, structures event logs; 2) create learner trajectory networks of use pathways through content activity modules; 3) analyze the students 4) aggregate interaction measurements for a given course.
The edX Student and Course Analytics Visualization Pipeline is analytics visualization pipeline using course database user logs, written in R to 1) extract process student users performance data, structures event logs; 2) create learner trajectory networks of use pathways through content activity modules; 3) analyze the students 4) aggregate interaction measurements for a given course.
The edX Student and Course Analytics Visualization Pipeline is analytics visualization pipeline using course database user logs, written in R to 1) extract process student users performance data, structures event logs; 2) create learner trajectory networks of use pathways through content activity modules; 3) analyze the students 4) aggregate interaction measurements for a given course.
The edX Student and Course Analytics Visualization Pipeline is analytics visualization pipeline using course database user logs, written in R to 1) extract process student users performance data, structures event logs; 2) create learner trajectory networks of use pathways through content activity modules; 3) analyze the students 4) aggregate interaction measurements for a given course.