Mission Statement: Harnessing learners’ activity data to help faculty make data-informed decisions on teaching, and enhance student learning experience at Dartmouth.
Contact: jing.qi@dartmouth.edu Research Informatics @ ITC
Resource: https://sites.dartmouth.edu/learninganalytics/
Services:
- Harnessing learning data to support educational research, teaching, and learning
- Advanced data visualization –
- support for publication-quality visualizations
- data mining, social network analysis, and semantic analysis
- Dashboard development using Tableau
- Analytical application development – Convert statistical analysis into a web-based application
- Learning Analytics – Educational Data Mining and Data Visualization
- RStudio Connect – Account creation, Role provision, and RShiny application development
LEARNING ANALYTICS focus on the complex analyses of learners and their contexts, for the purpose of enhancing learning and optimizing the environments in which learning occurs. LA sits at the convergence of Learning Science (e.g. learning and assessment sciences), Analytics (e.g. statistics, visualization, data sciences, artificial intelligence), and Evidence-Centered Design (e.g. participatory design, systems thinking).
KEY USES: The evidence from research and practice shows that there are a wide array of productive and potent ways of using analytics to support teaching and learning. The most popular goal of learning analytics include:
- Provision of personalized and timely feedback to students regarding their learning
- Supporting the development of important skills such as collaboration, critical thinking, communication, and creativity
- Develop student awareness by supporting self-reflection
- Support quality learning and teaching by providing empirical evidence of the success of pedagogical innovations
What Research Informatics @ ITL offer:
Descriptive Analytics: Help faculty create dashboards that represent student learning activities and the dynamics of student interactions over time.
Diagnostic Analytics: Help faculty harness learning data, i.e., quiz results, discussion interactions, and text semantic analysis, and identify factors that can be leveraged to improve student engagement, academic performance, and overall well-being.
Prescriptive Analytics: Help faculty make evidence-based decisions on instruction.