Design Principles for High School Engineering Design Challenges: Experiences from High School Science Classrooms

At the University of Pittsburgh, we have been exploring a range of approaches to design challenges for implementation in high school science classrooms (Apedoe, Reynolds, Ellefson, & Schunn, 2008; Ellefson, Brinker, Vernacchio, & Schunn, 2008; Schunn, Silk, & Apedoe, in press). In general, our approach has always involved students working during class time over the course of many weeks. So, our understanding of what works must be contextualized to that situation (i.e., without significant home support, by students enrolled in traditional classrooms, involving content that is connected to traditional science classrooms). However, our approach has been implemented with thousands of students in over 80 classrooms ranging from 9th grade biology or general science to 11th grade physics, from traditional mainstream science classrooms to elective Biology II or Honors Chemistry, and from high needs urban classrooms to affluent suburban classrooms. In other words, there is some important generality to these experiences. We have also conducted a number of studies on students in these settings, to understand a range of factors that influence student learning and affect outcomes (Apedoe & Schunn, 2009; Doppelt & Schunn, 2008; Reynolds, Mehalik, Lovell, & Schunn, 2009; Silk, Schunn, & Strand-Cary, 2009). This white paper provides a brief summary of principles that appear to guide successful experiences for students

Comprehension through explanation as the interaction of the brain’s coherence and cognitive control networks

Discourse comprehension processes attempt to produce an elaborate and well-connected representation in the reader’s mind. A common network of regions including the angular gyrus, posterior cingulate, and dorsal frontal cortex appears to be involved in constructing coherent representations in a variety of tasks including social cognition tasks, narrative comprehension, and expository text comprehension. Reading strategies that require the construction of explicit inferences are used in the present research to examine how this coherence network interacts with other brain regions. A psychophysiological interaction analysis was used to examine regions showing changed functional connectivity with this coherence network when participants were engaged in either a non-inferencing reading strategy, paraphrasing, or a strategy requiring coherence-building inferences, self-explanation. Results of the analysis show that the coherence network increases in functional connectivity with a cognitive control network that may be specialized for the manipulation of semantic representations and the construction of new relations among these representations

Impact of Annotation Difficulty on Automatically Detecting Problem Localization of Peer-Review Feedback

We believe that providing assessment on students ’ reviewing performance will enable students to improve the quality of their peer reviews. We focus on assessing one particular aspect of the textual feedback contained in a peer review – the presence or absence of problem localization; feedback containing problem localization has been shown to be associated with increased understanding and implementation of the feedback. While in prior work we demonstrated the feasibility of learning to predict problem localization using linguistic features automatically extracted from textual feedback, we hypothesize that inter-annotator disagreement on labeling problem localization might impact both the accuracy and the content of the predictive models. To test this hypothesis, we compare the use of feedback examples where problem localization is labeled with differing levels of annotator agreement, for both training and testing our models. Our results show that when models are trained and tested using only feedback where annotators agree on problem localization, the models both perform with high accuracy, and contain rules involving just two simple linguistic features. In contrast, when training and testing using feedback examples where annotators both agree and disagree, the model performance slightly drops, but the learned rules capture more subtle patterns of problem localization. Keywords problem localization in text comments, data mining of peer reviews, inter-annotator agreement, natural langua

A modular neural-network model of the basal ganglia's role in learning and selecting motor behaviours Action editors

Abstract This work presents a modular neural-network model (based on reinforcement-learning actor-critic methods) that tries to capture some of the most relevant known aspects of the role that basal ganglia play in learning and selecting motor behavior related to different goals. The model uses a mixture of experts network for the critic and a hierarchical network with two levels for the actor. Some simulations with the model show that basal ganglia select 'chunks' of behavior whose 'details' are specified by direct sensory-motor pathways, and how emergent modularity can help to deal with tasks with asynchronous multiple goals. A 'top-down' approach is adopted that first analyses some adaptive non-trivial interaction of a whole (simulated) organism with the environment, and its capacity to learn, and then attempts to implement these functions with neural architectures and mechanisms that have an empirical neuroanatomical and neurophysiological foundation