Smart Assessment & Learning Analytics

Imagine this as a university campus. Imagine this is what we could do with our data. Can we build this at VT? Can we foster an interdisciplinary “meeting of the minds”: instructional design, learning technologies, brain science, education, psychology, computer science, systems engineering, OAA, CIDER, others … to serve the needs of our students and faculty–novice learners and master learners–for the 21st century?

Diana Chapman Walsh, president of Wellesley College from 1993 to 2007, has called for a new “science of learning” that leverages both advances in brain science and fast-changing technology. Her vision of assessment emphasizes that assessing WHAT students have learned is less valuable than finding out HOW they learn. Walsh looks to the work of Michael Polanyi, whose 1958 book Personal Knowledge distinguished explicit knowledge (“learning about”) from tacit knowledge (“learning to be”), as a model for analyzing and understanding socially constructed understanding in the digital age.

This PowerPoint presentation raises some of the core issues we should consider as we move from narrowly quantitative measures of learning to more comprehensive, nuanced, and qualitative understandings of learning.


Many challenges await us as we design assessments to deepen learning.  We need to develop analytics that do far more than function as in loco parentis for students. How do we foster student agency and choice for best decision-making practices on their part as they learn how to learn? So, a refined “best practices” for creating and utilizing sophisticated learning analytics would be ideal.

  • We need multiple ways to assess student success and achievement of learning outcomes.
  • Currently, we often build tools that we only think will fit everyone’s needs—because we don’t have the data.
  • We need innovative approaches to the personalization of curriculum and learning, including an innovative CLE that moves from a checklist model of “general education” to a truly generalizable educational foundation.

How, with learning analytics, can we examine different pieces of our curriculum and pedagogy and determine which are or are not contributing well to student learning? How can we ensure that the university does not simply tell students what to do and when, but instead empowers individual learners by asking them, with guidance from faculty, to reflect deeply on their own learning, and how digital technologies can further their learning? These are questions of personalization, indeed of the mass customization that the digital age makes possible at scale. Without these questions, we cannot generate the richer, more nuanced questions we need for a more sophisticated and effective philosophy of assessment. Cognitive development deserves the richest analytics we can imagine.

[slideshare id=1914779&doc=apgar-090827092551-phpapp01]


Academic advising must become more alert, informed, and helpfully tailored to each student’s needs and interests. While personal mentoring, done well, remains the most intimate and effective form of academic advising, analytics can help both mentor and student make better and more informed decisions. Adding a social dimension to these analytics can make the entire system more robust and effective. Businesses such as Netflix, Amazon, etc. have devised powerful recommendation systems that could serve as models for higher education, as several colleges and universities are already finding. See, for example, “The Netflix Effect: When Software Suggests Students’ Courses” (Jeffrey Young, The Chronicle of Higher Education, April 10, 2011):

Some two-thirds of movies rented on Netflix result from recommen­da­tions made by the site, and users rate recommended films half a star higher than those they find on their own. That’s according to the book Su­per Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart.

[Young] asked the book’s author, Ian Ayres, a Yale Law School professor, why he trusts machines with such personal decisions.

He says humans tend to have blind spots when handling tasks like advising, which involve complex systems. People often give too much weight to certain details based on personal preferences.


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