Education is a hot topic. From the stage of presidential debates to tonight's dinner table, it is an issue that most Americans are deeply concerned about. While there are many strategies for improving the educational process, we need a way to find out what works and what doesn't work as well. Educational assessment seeks to determine just how well students are learning and is an integral part of our quest for improved education. The nation is pinning greater expectations on educational assessment than ever before. We look to these assessment tools when documenting whether students and institutions are truly meeting education goals. But we must stop and ask a crucial question: What kind of assessment is most effective? At a time when traditional testing is subject to increasing criticism, research suggests that new, exciting approaches to assessment may be on the horizon. Advances in the sciences of how people learn and how to measure such learning offer the hope of developing new kinds of assessments-assessments that help students succeed in school by making as clear as possible the nature of their accomplishments and the progress of their learning. Knowing What Students Know essentially explains how expanding knowledge in the scientific fields of human learning and educational measurement can form the foundations of an improved approach to assessment. These advances suggest ways that the targets of assessment-what students know and how well they know it-as well as the methods used to make inferences about student learning can be made more valid and instructionally useful. Principles for designing and using these new kinds of assessments are presented, and examples are used to illustrate the principles. Implications for policy, practice, and research are also explored. With the promise of a productive research-based approach to assessment of student learning, Knowing What Students Know will be important to education administrators, assessment designers, teachers and teacher educators, and education advocates.

Knowing What Students Know: The Science and Design of Educational Assessment

Innovative uses of global and local networks of linked computers make new ways of collaborative working, learning, and acting possible. In Group Cognition Gerry Stahl explores the technological and social reconfigurations that are needed to achieve computer-supported collaborative knowledge building--group cognition that transcends the limits of individual cognition. Computers can provide active media for social group cognition where ideas grow through the interactions within groups of people; software functionality can manage group discourse that results in shared understandings, new meanings, and collaborative learning. Stahl offers software design prototypes, analyzes empirical instances of collaboration, and elaborates a theory of collaboration that takes the group, rather than the individual, as the unit of analysis.Stahl's design studies concentrate on mechanisms to support group formation, multiple interpretive perspectives, and the negotiation of group knowledge in applications as varied as collaborative curriculum development by teachers, writing summaries by students, and designing space voyages by NASA engineers. His empirical analysis shows how, in small-group collaborations, the group constructs intersubjective knowledge that emerges from and appears in the discourse itself. This discovery of group meaning becomes the springboard for Stahl's outline of a social theory of collaborative knowing. Stahl also discusses such related issues as the distinction between meaning making at the group level and interpretation at the individual level, appropriate research methodology, philosophical directions for group cognition theory, and suggestions for further empirical work.

Group Cognition: Computer Support for Building Collaborative Knowledge

AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer. It provides the student with positive, neutral, or negative feedback on the student’s typed responses, pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects erroneous ideas, answers the student’s questions, and summarizes answers. AutoTutor has produced learning gains of approximately .70 sigma for deep levels of comprehension.

/pdf/autotutor-a-tutor-with-dialogue-in-natural-language-3iubab04kj.pdf

AutoTutor: a tutor with dialogue in natural language.

Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP. It assembles a diverse views from internationally recognized researchers and emphasizes caveats in the attempt to apply Natural Language Processing to text mining. This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.

Natural Language Processing and Text Mining

Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1756-8765.2010.01106.x

Symbol interdependency in symbolic and embodied cognition.

This paper describes a series of classroom trials during which we developed Summary Street, an educational software system that uses Latent Semantic Analysis to support writing and revision activities. Summary Street provides various kinds of feedback, primarily about whether a student summary adequately covers important source content and fulfills other requirements, such as length. The feedback allows students to engage in extensive, independent practice in writing and revising without placing excessive demands on teachers for feedback. We first discuss the underlying educational rationale, then present some results of the trials conducted with the system. We describe the collaborative process among researchers and teachers which enabled the development of a viable and supportive educational tool and its integration into classroom instruction.

/pdf/developing-summarization-skills-through-the-use-of-lsa-based-53e3trw3x0.pdf

Developing Summarization Skills through the Use of LSA- Based Feedback*

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Developing Summarization Skills through the Use of LSA- Based Feedback*