Developmental change in the effectiveness of active learning strategies
In collaboration with Tania Lombrozo, Fei Xu, Tom Griffiths, Alison Gopnik, Zi Lin Sim - University of California, Berkeley; Caren Walker - University of California, San Diego; Markus Feufel - Charitè University Medicine, Berlin
Deciding what evidence is most valuable to obtain is a basic challenge faced by learners of any age. Research investigating children’s active information search has used variants of the “20-questions game”, where the task is to identify an unknown target object by asking as few yes-or-no questions as possible, either generating the questions from scratch or selecting them from a list of given alternatives. In my work, I take a novel developmental and computational approach to explore the efficiency of information search across the life span, by designing original tasks and developing formal, finer grained analyses of performance modeled within a Bayesian framework and based on expected information gain.
Ecological learning: Selecting the most efficient active learning strategies
In collaboration with Tania Lombrozo, Fei Xu, Zi Lin Sim - University of California, Berkeley; Todd Gureckis - New York University
Active learning strategies cannot be defined as optimal tout court. Instead, their efficiency depends on children’s prior knowledge and expectations, as well as on the task’s information structure, that is, the number of hypotheses available and their likelihood (Ruggeri & Lombrozo, 2015). For example, traditionally, the quality of a question has been tied to whether it is a constraint-seeking or hypothesis-scanning question (Mosher & Hornsby, 1966). Constraint-seeking questions target a feature shared by multiple hypotheses (e.g., “Was the boy late because of something related to means of transportation?”), whereas hypothesis-scanning questions target a single hypothesis (e.g., “Was the boy late because his bike was broken?”). Because constraint-seeking questions are able to rule out multiple hypotheses at each step of the search process, they are usually considered superior to hypothesis-scanning questions; however, this is not always the case. In my work, I consider for the first time how the traditional distinction between different question types maps onto the more formal distinction between more and less informative questions, as measured by their expected information gain, depending on the information structure of the problem being considered.
Benefits of active learning and its potential to inform education
In collaboration with Tania Lombrozo, Alison Gopnik, Fei Xu - University of California, Berkeley; Todd Gureckis - New York University; Doug Markant - ARC, Max Planck Institute for Human Development, Berlin; Henrik Olsson - Santa Fe Institute; Konstantinos Katsikopoulos - ABC, Max Planck Institute for Human Development, Berlin.
Despite widespread consensus that active learning leads to better outcomes than comparatively passive forms of instruction, it is often unclear when and why active learning benefits arise, and ore research is needed to identify what kinds of self-directed activities are beneficial in particular learning environments and how such benefits depend on underlying cognitive processes and more general developmental factors (Markant, Ruggeri, Gureckis, & Xu, 2015). Findings from active learning research also have general implications for informing educational practice, which is increasingly incorporating the model of inquiry-based learning. In particular, it is crucial to stress the importance of how children learn, besides what they learn, providing children in school with a flexible toolbox of learning heuristics and strategies. In this sense, I am currently collaborating with the Abdul Latif Jameel Poverty Action Lab at MIT on a project in India that has begun to explore how different self-directed activities can be integrated in current preschool environments and curricula.