Natural language processing techniques for researching and improving peer feedback

Peer review has been viewed as a promising solution for improving studennts' writing, which still remains a great challenge for educators. However, one core problem with peer review of writing is that potentially useful feedbback from peers is not always presented in ways that lead to revision. Our prior investigations found that whether students implement feedback is significantly correlated with two feedback features: localization information and concrete solutions. But focusing on feedback features is time-intensive for researchers and instructors. We apply data mining and Natural Languagee Processing techniques to automatically code reviews for these feedback features. Our results show that it is feasible to provide intelligent suppport to peer review systems to automatically assess students' reviewing performance with respect to problem localization and solution. We also show that similar research conclusions about helpfulness perceptions of feedback across students and different expert types can be drawn from automatically coded data and from hand-coded data. © Earli

Understanding the agreements and controversies surrounding childhood psychopharmacology

The number of children in the US taking prescription drugs for emotional and behavioral disturbances is growing dramatically. This growth in the use of psychotropic drugs in pediatric populations has given rise to multiple controversies, ranging from concerns over off-label use and long-term safety to debates about the societal value and cultural meaning of pharmacological treatment of childhood behavioral and emotional disorders. This commentary summarizes the authors' eight main findings from the first of five workshops that seek to understand and produce descriptions of these controversies. The workshop series is convened by The Hastings Center, a bioethics research institute located in Garrison, New York, U.S.A

Priming Analogical Reasoning with False Memories

Like true memories, false memories are capable of priming answers to insight-based problems. Recent research has attempted to extend this paradigm to more advanced problem-solving tasks, including those involving verbal analogical reasoning. However, these experiments are constrained inasmuch as problem solutions could be generated via spreading activation mechanisms (much like false memories themselves) rather than using complex reasoning processes. In three experiments we examined false memory priming of complex analogical reasoning tasks in the absence of simple semantic associations. In Experiment 1, we demonstrated the robustness of false memory priming in analogical reasoning when backward associative strength among the problem terms was eliminated. In Experiments 2a and 2b, we extended these findings by demonstrating priming on newly created homonym analogies that can only be solved by inhibiting semantic associations within the analogy. Overall, the findings of the present experiments provide evidence that the efficacy of false memory priming extends to complex analogical reasoning problems