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

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)

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

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

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