Syllabus: College of Natural Sciences, Junior Year Writing

CNS JYW is a multidisciplinary professional writing course. Every discipline comes complete with instructions on how to think, talk, and write in order to act like a member of that discipline and to recognize and comprehend others within that field. This writing course brings these distinctions to life by focusing on both formal and informal argumentative and technical writing for different genres and audiences. Writing intensive, the course presents the methods of inquiry, evidentiary procedures, genres, and text conventions that characterize the way scholars and professionals craft written texts. The course reinforces college-level vocabulary, critical analysis, and textual evidence and referencing strategies, and extends their application to specific concerns and practices within the disciplines encompassed by the College of Natural Sciences. To support students’ research and referencing, the course reinforces and extends technology and information-based literacies introduced in previously required writing courses

Literature Study Groups with At-Risk Readers: Extending the Grand Conversation

As schools heed the ever-widening call to involve students with quality literature, we are forced to confront two questions. The first refers to the grand conversation (Eeds and Wells, 1989) alluded to in the title of this article: How do we enable literature study groups to engage in mutual discussions of ideas (which constitute the grand conversations described by Eeds and Wells) rather than teacher-led inquiries about surface meaning (which Eeds and Wells characterize as gentle inquisitions )? The second refers to an issue of equity: How do we provide equal access to quality literature for students with limited reading ability? This article describes the attempts of one school district to extend the grand conversation of literature study groups to students with reading difficulties

"The Predication Semantics Model: The Role of Predicate: Class in Text Comprehension and Recall"

This paper presents and tests the predication semantics model, a computational model of text comprehension. It goes beyond previous case grammar approaches to text comprehension in employing a propositional rather than a rigid hierarchical tree notion, attempting to maintain a coherent set of propositions in working memory. The authors' assertion is that predicate class contains semantic information that readers use to make generally accurate predictions of a given proposition. Thus, the main purpose of the model-which works as a series of input and reduction cycles-is to explore the extent to which predicate categories play a role in reading comprehension and recall. In the reduction phase of the model, the propositions entered into the memory during the input phase are decreased while coherence is maintained among them. In an examination of the working memory at the end of each cycle, the computational model maintained coherence for 70% of cycles. The model appeared prone to serial dependence in errors: the coherence problem appears to occur because (unlike real readers) the simulation docs not reread when necessary. Overall, the experiment suggested that the predication semantics model is robust. The results suggested that the model emulates a primary process in text comprehension: predicate categories provide semantic information that helps to initiate and control automatic processes in reading, and allows people to grasp the gist of a text even when they have only minimal background knowledge. While needing refinement in several areas presenting minor problems-for example, the lack of a sufficiently complex memory to ensure that when the simulation of the model goes wrong it does not, as at present, stay wrong for successive intervals-the success of the model even at the current restrictive level of detail demonstrates the importance of the semantic information in predicate categories.

Deborah McCutcheon interview

Webcast file name: McCutchen_jul06_2011Date: July 6, 2011Voice of Literacy host, Dr. Betsy Baker, interviews Dr. Deborah McCutcheon, Professor of Educational Psychology, College of Education Associate Dean for Research and Faculty Support, University of Washington

Deborah McCutchen interview

Webcast file name: mccutchen_jun26_2009Date: June 26, 2009Voice of Literacy host, Dr. Betsy Baker, interviews Dr. Deborah McCutchen, Professor of Educational Psychology and Learning Sciences at the University of Washington

The Predication Semantics Model: The Role of Predicate Class in Text Comprehension and Recall

Previous models of text comprehension generally do not maintain a coherent set of propositions in working memory. Readers produce coherent summaries of texts, however, even when they lack extensive background knowledge. We present a computational model of text comprehension and recall that makes extensive use of predicate class information, which explains how readers extract the gist of a text. Predicate class determines the sequence in which propositions are reduced, and the rules that are used for gist extraction. A computer simulation of the model significantly predicted subjects ’ immediate recall and maintained coherence in working memory on most processing cycles. Predication Semantics Model 3 The primary assertion of this paper is that predicate class contains semantic information that readers use to make generally accurate predictions about the relative importance of a given proposition, and then which arguments to hold, pass to another proposition, or eliminate as they construct a summary representation of the text. We will show how different classes of predicates do more than define the relationship among each proposition’s arguments