What's It Going To Take To Move From Mass-Production To Mass-Personalization?

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Brief Abstract

What is it going to take for us to toss the 19th century mass-production model of education onto the trash heap of history and embrace the 21st century model of mass-personalization? Artificial intelligence is facilitating the delivery of mass-personalization in many aspects of our lives. From medicine to manufacturing, we see intelligent systems creating personalized solutions to meet the unique needs of every individual. However, in education mass-personalization is still considered experimental and has not gained widespread acceptance yet. In this session, we will explore the factors enabling or inhibiting this change.


Dale P. Johnson is the director of digital innovation for the University Design Institute at Arizona State University. He works with university leaders to develop and implement digital solutions to enable student success. Those efforts have earned him the 2016 Sally M. Johnstone Award from WCET recognizing his thought leadership, excellence in practice, and demonstrated leadership capabilities. In 2018, he was honored by the IMS Global Learning Consortium with an outstanding service award for his leadership of the adaptive courseware community of practice. Mr. Johnson has spoken on the topic of digital innovation in higher education at more than 20 conferences in the USA, Rwanda, Brazil, South Korea, Germany, Mexico, Russia and Vietnam, and led workshops on the subject at numerous universities. He has a bachelor of science in design degree from ASU and a master in public policy degree from Harvard University, a learning path that combined his interests in design, engineering and education policy. In his spare time, he enjoys traveling and building things. He’s traveled to more than 40 countries, studied in Barcelona for a year as a Rotary Foundation Ambassadorial Scholar, and built his own solar home in Phoenix.

Extended Abstract

Arizona State University has made the transformation from mass-production to mass-personalization in college algebra. To do that, we had to reverse the relationship between technology and teaching. Traditional pedagogy places the professor at the center of the instructional process to deliver content to students who can then use technology to practice the lessons. The new approach changes those relationships and places the technology at the center of the instructional process.

The redesign resulted in a 17 percentage point increase in the student success rate (grade of C or higher) at ASU over three years. It also enabled us to develop a scalable and sustainable framework for deploying this model in the course which enrolls over 10,000 students each year.

The model is based on a new set of design principles that ASU developed to guide the college algebra transformation program.

  • Each student has unique math learning needs.
  • Students learn math best by solving problems (not by watching someone else solve them).
  • Students must demonstrate mastery of each lesson to advance.
  • Professors must use diagnostic data to provide individualized instruction.
  • Course design must provide a test-when-ready process for summative assessments.

These statements form the intellectual foundation for mass-personalization in education. They also stand in stark contrast to the principles of mass-production which emphasize standardization of curriculum, assessment, and pedagogy.

Guided by these new principles, the ASU team redefined the relationships between technology, pedagogy and professors in the course while holding the curriculum, learning objectives, and assessments constant with the prior course design. To do that, we employed the McGraw Hill ALEKS system which uses artificial intelligence to personalize each student’s lesson plan by recommending instructional content and conducting diagnostic assessments on each student’s progress and performance as they learn. The professor’s new role in the teaching process is akin to that of a doctor who uses data from advanced technology to diagnose a patient's personal needs and help them get better. Data from ALEKS enables the professor to provide individual instruction to each student to help them learn the lesson they need as effectively as possible.

In this session, we will explore factors enabling or inhibiting this change. These will include the topics of faculty development, policy innovation, pedagogical transformation, and technology adoption to successfully accelerate the implementation of mass-personalization in education.