46 research outputs found
A new approach for uncovering student resources with multiple-choice questions
The traditional approach to studying student understanding presents a
question and uses the student answers to make inferences about their knowledge.
However, this method does not capture the range of possible alternative ideas
available to students. We use a new approach, asking students to generate a
plausible explanation for every choice of a multiple-choice question, to
capture a range of explanations that students can generate in answering physics
questions. Asking 16 students to provide explanations in this way revealed
alternative possibilities for student thinking that would not have been
captured if they only provided one solution. The findings show two ways these
alternatives can be productive for learning physics: (i) even students who
ultimately chose the wrong answer could often generate the correct explanation
and (ii) many incorrect explanations contained elements of correct physical
reasoning. We discuss the instructional implications of this multiple-choice
questioning approach and of student alternative ideas
Gravity Currents in Aquatic Canopies
A lock exchange experiment is used to investigate the propagation of gravity currents through a random array of rigid, emergent cylinders which represents a canopy of aquatic plants. As canopy drag increases, the propagating front varies from the classic profile of an unobstructed gravity current to a triangular profile. Unlike the unobstructed lock exchange, the gravity current in the canopy decelerates with time as the front lengthens. Two drag-dominated regimes associated with linear and nonlinear drag laws are identified. The theoretical expression for toe velocity is supported by observed values. Empirical criteria are developed to predict the current regime from the cylinder Reynolds number and the array density.National Science Foundation (U.S.) (grant EAR0309188)National Science Foundation (U.S.) (grant EAR0509658)Massachusetts Institute of Technology (Presidential Graduate Fellowship
Space as a Tool for Astrobiology: Review and Recommendations for Experimentations in Earth Orbit and Beyond
Expertise Effects on Sorting Strategies of Causal Phenomena
Recent work has emphasized both the importance of relational categories in cognition, (Gentner & Kurtz, 2005) and role of causal structure in categorization of causal phenomena (Rehder, 2003; Sloman, Love, & Ahn, 1998)
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Elemental Causal Learning from Transitions
Much research on elemental causal learning has focused on
how causal strength is learned from the states of variables. In
longitudinal contexts, the way a cause and effect change over
time can be informative of the underlying causal relationship.
We propose a framework for inferring the causal strength
from different observed transitions, and compare the
predictions to existing models of causal induction. Subjects
observe a cause and effect over time, updating their
judgments of causal strength after observing different
transitions. The results show that some transitions have an
effect on causal strength judgments over and above states
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Temporal Causal Strength Learning with Multiple Causes
When learning the relation between a cause and effect, howdo people control for all the other factors that influence thesame effect? Two experiments tested a hypothesis that peoplefocus on events in which the target cause changes and allother factors remain stable. In both four-cause (Experiment 1)and eight-cause (Experiment 2) scenarios, participants learnedcausal relations more accurately when they viewed datasets inwhich only one cause changed at a time. However,participants in the comparison condition, in which multiplecauses changed simultaneously, performed fairly well; inaddition to focusing on events when a single cause changed,they also used events in which multiple causes changed forupdating their beliefs about causal strength. These findingshelp explain how people are able to learn causal relations insituations when there are many alternative factors
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Politically Motivated Causal Evaluations of Economic Performance
The current study seeks to extend research on motivated
reasoning by examining how prior beliefs influence the
interpretation of objective graphs displaying quantitative
information. The day before the 2018 midterm election,
conservatives and liberals made judgments about four
economic indicators displaying real-world data of the US
economy. Half of the participants were placed in an 'alien
cover story' condition where prior beliefs were reduced under
the guise of evaluating a fictional society. The other half of
participants in the 'authentic condition' were aware they were
being shown real-world data. Despite being shown identical
data, participants in the Authentic condition differed in their
judgments of the graphs along party lines. The participants in
the Alien condition interpreted the data similarly, regardless
of politics. There was no evidence of a „backfire‟ effect, and
there was some evidence of belief updating when shown
objective data