An Ontology of Choice-Based Learning

From The Theme

What if we could instantiate a framework for a new form of learning ontology which focuses on learning strategies rather than knowledge states?

This ontology development is an outgrowth of research on choice-based assessments. Success at observing student learning strategies requires closely observing how a student goes about learning, and it requires tasks that support student opportunities to apply strategies conducive to rule induction.

Choice-based assessments were developed to capture the learning and motivation strategies that prepare people to continue learning on their own when they no longer have the strict guidance of a teacher.

Using a novel machine-learning algorithm, we were able to detect three dominant strategies: just try to guess the right answer, explore the phenomena haphazardly, or try to find out the equivalence between different units. In a complementary line of investigation, we found that observing which learning strategies students are using and how students are applying learning strategies is much more complex than whether a student is achieving a correct or incorrect answer; teachers have difficulty discerning students’ use of learning strategies when they can’t be seen by the naked eye. Choice-based assessments can help teachers identify students’ use of learning strategies.

mediaX Research Update Fall 2020

AAA Lab, Choice Based Assessments Research

Dan SchwartzDaniel L. Schwartz is the I. James Quillen Dean Stanford Graduate School of Education and Nomellini & Olivier Chair of Educational Technology. He is an expert in human learning and educational technology. Schwartz oversees a laboratory whose computer-focused developments in science and math instruction permit original research into fundamental questions of learning. He has taught math in rural Kenya, English in south-central Los Angeles, and multiple subjects in Kaltag, Alaska. This diversity of experience informs his work. Among many honors, Schwartz was named Graduate School of Education Teacher of the Year for 2015.

Rachel Wolf is a researcher in the AAALab. She is passionate about STEM education and engagement both in and out of the classroom. She has worked at The Griffith Observatory, interned with Discovery Communications, and served as a volunteer outreach scientist with The Franklin Institute. She has taught undergraduate physics laboratories, been trained in and designed curricula for active-learning undergraduate physics courses, and engaged as a math instructor and tutor.

Tanja Käeser is a Post-doc and assistant professor at the EPFL School of Computer and Communication Sciences (IC). She is also head of the D-VET laboratory. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning.

Katie Cheng is a PhD Student in the Stanford Graduate School of Education. She is a researcher who specializes in educational psychology, learning sciences, and technology design. Her research seeks to understand the strategies and mindsets that learners need to learn effectively. From this lens, she designs, builds, and studies educational technologies that empower learners.

Ana Saavedra is a PhD student in the Stanford Graduate School of Education. Her main research interests are understanding how metacognition develops during early life stages, the intersection between cognitive and emotional processes in early childhood, and the use of technology to support these processes.

Main Image: Annie Spratt