Engineer the Future of Learning

Are you looking for graduate education to prepare you and your students for the future? Consider a Masters in Educational Technology and Applied Learning Science. Because CEI is focused on educational innovations, we are enthusiastic about this new program at Carnegie Mellon University.

The application deadline is Jan. 31, 2014.

The Masters in Educational Technology and Applied Learning Science is an interdisciplinary program jointly taught by Carnegie Mellon’s Human-Computer Interaction in the School of Computer Science and Psychology in Dietrich College of Humanities and Social Sciences.

Carnegie Mellon educational technology applied learning science

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This program offers students who have a Bachelor’s or Master’s degree in such areas as psychology, education, computer science, information technology, business, or design the opportunity to improve their training with advanced study in educational technology and applied learning science.

Students will gain the knowledge, skills, and techniques to develop and evaluate programs in learning settings that range from schools to workplaces, museums to computer-based environments—as well as other formal, informal and non-traditional educational settings. Graduates of the program will take key positions in corporations and private and public universities and schools; they will become designers, developers, and evaluators of educational technologies and learning environments as well as domain experts, learning technology policy-makers, or Chief Learning Officers.

The program integrates fundamental skills with project-based studio classes culminating in a final capstone project. It is distinct from the Learning Science track in the HCII PhD program, and, in particular, it is not designed as a feeder to that PhD program.

Upon completion of the Masters in Educational Technology and Applied Learning Science, graduates will:

  •  Be able to design, develop, and implement advanced educational solutions that make use of state-of-the-art technologies and methods such as artificial intelligence, machine learning, language technologies, intelligent tutoring systems, educational data mining, tangible interfaces.
  • Understand how these technologies can be applied to engineer and implement innovative and effective educational solutions.
  • Understand cognitive and social psychology principles relevant to research-informed instructional design.
  • Have skills for instructional and interaction design needed to create solutions that not only enhance learning, but are also desirable.
  • Understand the role of and have skills in using psychometric and educational data mining methods in evaluating and improving educational solutions
  • Be able to develop continual improvement programs that employ “in vivo” experiments and educational data mining to reliably identify best practices and opportunities for change.

 


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