Reading Next: A Vision for Action and Research in Middle and High School Literacy

Outlines fifteen key elements that educators can use to develop an effective adolescent literacy intervention program. Focuses on elements of interventions that are most promising for students that struggle with reading and writing after third grade

Collected notes from the Benchmarks and Metrics Workshop

In recent years there has been a proliferation of proposals in the artificial intelligence (AI) literature for integrated agent architectures. Each architecture offers an approach to the general problem of constructing an integrated agent. Unfortunately, the ways in which one architecture might be considered better than another are not always clear. There has been a growing realization that many of the positive and negative aspects of an architecture become apparent only when experimental evaluation is performed and that to progress as a discipline, we must develop rigorous experimental methods. In addition to the intrinsic intellectual interest of experimentation, rigorous performance evaluation of systems is also a crucial practical concern to our research sponsors. DARPA, NASA, and AFOSR (among others) are actively searching for better ways of experimentally evaluating alternative approaches to building intelligent agents. One tool for experimental evaluation involves testing systems on benchmark tasks in order to assess their relative performance. As part of a joint DARPA and NASA funded project, NASA-Ames and Teleos Research are carrying out a research effort to establish a set of benchmark tasks and evaluation metrics by which the performance of agent architectures may be determined. As part of this project, we held a workshop on Benchmarks and Metrics at the NASA Ames Research Center on June 25, 1990. The objective of the workshop was to foster early discussion on this important topic. We did not achieve a consensus, nor did we expect to. Collected here is some of the information that was exchanged at the workshop. Given here is an outline of the workshop, a list of the participants, notes taken on the white-board during open discussions, position papers/notes from some participants, and copies of slides used in the presentations

The Jefferson Medical College of Philadelphia: From 1825 to 1908

This brief 1908 history of Jefferson Medical College was written by James W. Holland. An 1868 graduate, Holland went on to become the Dean of Jefferson Medical College in 1887https://jdc.jefferson.edu/jeffersonhistorybooks/1007/thumbnail.jp

Reading "all about" computerization: five common genres of social analysis

This paper examines unstated, but critical, social assumptions which underlie analyses of computerization. It focuses on the popular, professional and scholarly literature which claims to describe the actual nature of computerization, the character of computer use, and the social choices and changes that result from computerization. This literature can be usefully segmented five ideal type genres: utopian, anti-utopian, social realism, social theory, and analytical reduction. Each genre is characterized and illustrated. The strengths and weaknesses of each genre are described. In the 1990s, there will be a large market for social analyses of computerization. Utopian analyses are most likely to domínate the popular and professional discourse. The empirically oriented accounts of social realism, social theory and analytical reduction, are likely to be much less common and also less commonly seen and read by computer professionals and policymakers. These genres are relatively subtle, portray a more ambiguous world, and have less rhetorical power to capture the imagination of readers. Even though they are more scientific, these empirically anchored genres don't seem to appeal to many scientists and engineers. It is ironic that computing -- often portrayed as an instrument of knowledge -- is primarily the subject of discourses whose knowledge claims are most suspect. Conversely, the discourses whose claims as valid knowledge are strongest seems to have much less appeal in the mass media and technological communities

Producing a commentary slows concurrent hazard perception responses

Commentary driver training involves teaching drivers how to verbally acknowledge their perceptual and cognitive processes while driving, and has been shown to improve performance in driving-related tasks. However, those studies demonstrating benefits of commentary training have not done so under conditions of live commentary, which is the typical protocol used with advanced drivers. In the current study we present the results of two experiments that show that producing a commentary can actually slow responses to hazards on a concurrent hazard perception task. In Experiment 1 participants producing a live commentary showed significantly longer hazard response times than an untrained, silent, control group. In Experiment 2 a shorter, clipped commentary was introduced to attempt to reduce the demands placed upon participants. However, both the clipped and full commentary conditions showed reduced accuracy and longer response times, relative to a silent condition, and no difference was observed between the two types of commentary. Analysis of eye movements in both experiments revealed that fixation durations were shorter when a commentary was produced, but time to first fixate the hazard was not affected. This suggests that commentaries encourage more active interrogation of the visual scene, but that this can be detrimental to performance in average drivers

Bayesian Inference of Self-intention Attributed by Observer

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We believe that, in order for RL agents to comprehend utterances from human colleagues, RL agents must infer the mental states that people attribute to them because people sometimes infer an interlocutor's mental states and communicate on the basis of this mental inference. This paper proposes PublicSelf model, which is a model of a person who infers how the person's own behavior appears to their colleagues. We implemented the PublicSelf model for an RL agent in a simulated environment and examined the inference of the model by comparing it with people's judgment. The results showed that the agent's intention that people attributed to the agent's movement was correctly inferred by the model in scenes where people could find certain intentionality from the agent's behavior