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To harness education technology’s full potential, researchers education decision-makers, product developers, and funders need to investigate the ways in which technology can help — or in some cases hurt — teaching and learning.
By understanding existing research or conducting new research, you can shape important practices and impact how media and technology is used to enhance teaching and learning.
Roles in research and policy often include working in research and development organizations such as Center for Children and Technologies, Cooney Center, Common Sense Education, LEAP Innovations, NYC’s iZone, Digital Promise’s League of Innovative Schools. Academia and ed tech investment in research also supports many centers, labs and initiatives aimed at promoting and supporting innovative solutions.
Included are roles such as:
educational researcher in non-profit sector
policy, advocacy and decision making roles
scholarly researcher / university
learning analytics and educational data scientists
research associate/ research faculty/ educational data analyst / educational researcher / learning management system (LMS) analyst / data scientist / education research assistant / research officer / strategic associate/ diretor of research/ vp of research and development/ design researcher / data analyst / research specialist / instructional coordinator / director of student assessment
Note: These are some, not all, of the areas included in this pathway; specific jobs may span multiple areas of focus
Learning analytics refers to the collection and analysis of data about learners and their environments for the purpose of understanding and improving learning outcomes.
Learning analytics is where big data meets traditional quantitative methods in education. Governments, universities, testing organizations, and massive open online course providers are collecting data about learners and how they learn. All that data, however, has been mostly untapped until the fairly recent development of the methods and tools to do so.
Educational data analysts are responsible for analyzing large-scale data sets, leading efforts into the effectiveness of the product using big data and quasi-experimental methodology, and using data insights to inform design teams as they execute new products. They also need to use data to define typical user characteristics.
Educational data analysts are familiar with evidence-centered design methodology and have excellent data communication skills. They focus on the translation of data into expert recommendations that help share the company's assessment of student learning, pipeline, production, business development, marketing, and other efforts.
* Note this is a sample only. Internship availability varies from semester to semester.
Educational researchers are responsible for conducting education-based research by asking the right questions, leveraging the right technology, and reporting on meaningful findings. They use organized, scientific, and methodical ways to collect and analyze data. Educational researchers also need to know about educational assessments, evaluations, and qualitative and quantitative methods. They learn to plan, design, and implement statistical data projects.
Educational researchers work with academic teams that facilitate educational research and discovery by providing design expertise and measurement tools. They recommend and incorporate existing research knowledge into academic programs and propose independent research projects for potential classes. They maintain collaborative relationships with faculty and other schools. They facilitate collective problem solving of common educational challenges, such as administrative bottlenecks, student barriers, and program inefficiencies.
* Note this is a sample only. Internship availability varies from semester to semester.
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EDCT-GE 2252 Theories & Principles of Learning Analytics
EDCT-GE 2260 Learning Analytics Applications
more soon..
Statistics in Education
Qualitative Research Methods
Participatory Action Research
Design Based Research Methods
APSTA-GE 2014: Statistical Analysis of Networks
APSTA-GE 2011: Supervised and Unsupervised Machine Learning
DS-GA 1011 Natural Language Processing
CS-GY 6313: Information Visualization
ITPG-GT 2941 Data w/o Borders: Data Science in the Service of Humanity
CEH-GA 3016 Data Rules: How Quantification Shapes Science, Selves, and States
more soon
With this specialization, you'll be able to harness the power of data to improve learning and teaching, learn how to design and create information and decision-making tools for educational settings that are based on data science, machine learning, and artificial intelligence.
Learn the foundations of great communication design to better convey complex information
Explore the foundations of quantitative research to approach different parts of the research process
Zoom Recording on Educational Policy & Research
Heather Sherwood, DMDL 2015, Research Associate, Education Development Center
Marc Lesser, VP Research and Technology, National Academic Foundation
Larry Cocco, NJ, Consultant/ Director, Office of Educational Technology NJ Department of Education