Carnegie Mellon University Reinvents Instructional Technology

New Ed Tech Masters Program Leverages Extensive Research into Learning Science

My thanks to Carnegie Mellon's Michael Bett for putting together this thorough overview of their thoughtfully designed program. Forward thinking graduate programs like these can play a key role in developing the leadership necessary to more effectively leverage technology in teaching and learning on a wider scale. – KW

Carnegie Mellon University in Pittsburgh is preparing a new generation of educators who will challenge the future of learning and re-examine the goals of education and assessment. Nine years of research have been translated into a one-year Professional Masters in Educational Technology and Applied Learning Science (‘METALS’ – see http://metals.cs.cmu.edu). By teaching our students how to provide personalized education for all, we will transfer the best learning science research into practice in the classroom.

LearnLab Created By NSF in 2004 to Study Robust Learning

LearnLab (http://learnlab.org ) was founded nine years ago to address three core goals:

1. To enrich and integrate scientific knowledge about learning of academic content
2. To  transform the technical and social infrastructure of the learning sciences – including training a new and more diverse set of participants in our profession
3. To insure that the learning sciences develop in a manner that makes its work relevant to and useable by those in charge of educating the nation’s students.

Our key strategy for identifying research problems has been to focus on what we call robust learning: human learning of content and skills judged important to society, that transfers to novel circumstances, is retained over long durations, accelerates future learning, and/or produces a desire for future learning. We believe that despite a stated need and notable efforts not enough learning research has achieved a sustained focus on robust learning.

In many studies of learning and in many educational settings, learning is assessed immediately following instruction using test items like those presented in instruction. In contrast to such “immediate learning” assessment, we seek methods to produce and measure robust learning, by which we mean learning that is retained for long durations, transfers to novel situations, or aids future learning. In contrast to the education wars that have plagued progress in the learning sciences and in educational practice, we do not pit foundational skill building against sense-making and conceptual understanding, but instead believe we must address both to improve robust learning. These wars continue, in part, because we do not have adequate scientific basis to guide educational decision-making. We need “rigorous, sustained scientific research in education”, as called for by the National Research Council, and a key part of such sustained research is to better unify and integrate the proliferating variety of today’s educational and learning science theories. As the saying goes, many theories in the learning sciences are like your toothbrush: everyone has one and no one uses anyone else’s.

For the past nine years approximately 200 researchers from eight universities around the world have been studying how to make learning more effective by conducting over 328 in vivo experiments in math, science and language courses. Carnegie Mellon has also revolutionize the primary and secondary analysis of learning data through the open data repository, DataShop, which provides data import and export features as well as advanced visualization, statistical, and data mining tools.

Creating a Cadre of Evidence-Based Learning Engineers

Based on our research, our one-year Professional Masters in Educational Technology and Applied Learning Science (METALS) program teaches instructors how to make innovative change and become a leader in the educational technology revolution. Our goal with this program is to increase the amount of knowledge we export and our impact on the world by opening our leading-edge educational system to a new population.

Students who have a Bachelor's or Master’s degree in such areas as psychology, education, computer science, information technology, business, or design have the opportunity to improve their training with advanced study in educational technology and learning science. In this program, students work collaboratively to 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. The program integrates fundamental skills with project-based studio classes culminating in a final capstone project. Graduates of the program will be prepared to take key positions in corporations, universities and schools as designers, developers, and evaluators of educational technologies as well as learning engineers,  curriculum developers, learning technology policy-makers, and even chief learning officers.

Changing the Future of Education

Upon completion of the Master of Science in Learning Science and Engineering, 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.

We anticipate this program will yield broader impacts as well including:

• Training students for careers in design, implementation, and evaluation of educational interventions based in learning science and design thinking.
• Expanding our impact into industry as well as academic environments.
• Building connections between CMU and educational technology companies that could potentially result in research collaborations.
• Filling the industry need for educational professionals with training in advanced technology, cognitive, social, and learning
  sciences.
• Giving students practical experience in the development of educational technology and courseware based on sound scientific principles and studies.

Licensed teachers who pursue studies in this area will learn methods for integrating technology into their classrooms. Professionals in business and industry who are attracted to the multimedia design and development coursework will be able to focus on software design. Individuals from both P-16 and business will find this degree program useful for understanding the effective design and delivery as well as integration of distance learning environments. We will also attract our own talented undergraduates, who have seen the rapid growth in on-line education and/or are interested in acquiring skills to improve
education.

Because this program features collaborative instruction from four nationally ranked departments within the Carnegie Mellon University —the Human Computer Interaction Institute, Psychology, the Language Technologies Institute, and the Machine Learning department — students benefit from a rich, broad-based curriculum, and faculty members with both research-based and hands-on expertise.

Some students may come from programs that are theory-heavy or implementation-heavy. While here, they will be able to balance and complete their educations by engaging in Master's level work in areas complementary to their current strengths producing world class learning scientists ready to challenge the future of learning.

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