We are infusing inquiry-driven programming activities into algebra and physics courses with the objective of strengthening disciplinary understandings and providing all students a meaningful first experience with programming.

Best Fit Least-Squares (BFLS) is a required technique for many STEM subjects. It is a method to compute a linear model for a set of data points. Due to its utility, BFSL is frequently taught to STEM students before they have sufficient mathematical experience to follow the mechanics of a derivation. Not only does this fail to produce procedural and conceptual understandings, but also it encourages students to view formulae and algorithms as things to be looked up, rather than derived. This is discourages students from developing productive dispositions. In this paper, we describe the “Close Fit Rod-Spring” (CFRS) approach to the problem. This approach computes the resting position of a rigid rod, which is connected by vertically oriented springs to a set of data points. This method of derivation results in two linear equations that may be solved for the slope and intercept of the best fit line. The result is equivalent to BFSL. However, it is achieved in an intuitively understandable and mathematically accessible way for high school students.

“Computer Programming for Science/ Engineering” (CPSE) is an introductory programming course for STEM students other than Computer Science (CS) major. Typical attendees are required to attend either CPSE or the Java-based first programming course of the CS majors' sequence (CS1). The previous curriculum for CPSE was a traditional introductory programming course with chronically low enrollment despite less its substantially relaxed learning outcomes. Post-reform, CPSE has become a popular alternative to CS1. Pass rates in CPSE are substantially higher than both local and national pass rates for CS1 and equivalent courses, and in-class tests of CPSE students indicate that 70% satisfy the majority of CS1 outcomes, which exceeds the pass rate for most CS1 sections at our institution. While both CPSE and CS1 teach the Java language, CPSE integrates inductive teaching strategies developed by the second author's iMPaCT program that exploit the relaxed syntax of the Jython language. Like immersive foreign language programs, lessons during the first half of the course exploit the relaxed grammatical requirements of a simpler programming language (Jython) to introduce programming concepts (semantics) incrementally in a conversational manner. These early lessons are motivated by accessible graphical problems that incidentally review foundational math concepts.

A neural net can learn to discriminate among a set of classes without explicitly training to do so. It does not even need exposure to any instances of those classes. The learning occurs while the net is being trained to discriminate among a set of related classes. This form of transfer learning is referred to as `Latent Learning' by psychologists, because until specifically elicited, the knowledge remains latent. Evidence that latent learning has occurred lies in the existence of consistent, unique responses to the unseen classes. Standard supervised learning can improve the accuracy of those responses with exceedingly small sets of labeled images. In this paper, we use a convolutional neural net (CNN) to demonstrate not only a method of determining a net's latent responses, but also simple ways to optimize latent learning. Additionally, we take advantage of the fact that CNN's are deep nets in order to show how the latently learned accuracy of the CNN may be greatly improved by allowing only its output layer to train. We compare our results both to those obtained with standard backpropagation training of the CNN on small datasets without any transfer learning and to a related set of current published results.

Psychologists define latent learning as learning that occurs without task-specific reinforcement and is not demonstrated until needed. Since this knowledge is acquired while mastering some other task(s), it is a form of transfer learning. We utilize latent learning to enable a deep neural net to distinguish among a set of handwritten numerals. The accuracies obtained are compared to those achievable with a simplistic `group-mean' classification technique, which is explained later in this paper. The deep neural net architecture used was a Le-Net 5 convolutional neural net with only minor differences in the output layer.

Media-Propelled Computational Thinking (MPCT - pronounced impact) is a course designed to introduce programming in the context of engaging problems in media computation, math, and physics. Programming concepts are introduced as incremental steps needed to solve pragmatic problems students already understand. The problems, graphical API, and hands-on program features are intended to expose fundamental concepts in mathematics and quantitative science.MPCT is offered in an entering students program for freshmen who plan to specialize in a variety of STEM (science, technology, engineering and math) and non-STEM subjects. The curriculum is intended to strengthen student intuition and interest in mathematical modeling and programming by engaging students in the direct manipulation of simple mathematical systems that model and display familiar physical phenomena. MPCT uses programs as concrete and manipulatable examples of fundamental concepts to engage a diverse range of students including women and underrepresented minorities.Variants of MPCT are being developed for high schools, and as a means to introduce computational science to upper division undergraduates studying non-computational STEM disciplines. This paper provides an overview of MPCT and representative problem studies including models of ballistics and resonant systems. The evaluation plan is described and very preliminary results are presented.

We describe a generic framework for determining and monitoring access rights derived from credential documents. Distributed authorization systems intended to support collaborative coalitions (such as trust management systems) typically incorporate mechanisms to both validate credentials, and to dete

The DisCo middleware infrastructure facilitates the construction and deployment of decomposable applications for environments with dynamic network connectivity properties and unstable trust relationships spanning multiple administrative domains. Consumers of these services, who are mutually anonymous, must be able to discover, securely acquire the code for, and install service components over the network with only minimal a priori knowledge of their locations. Once installed, these components must be able to intemperate securely and reliably across the network. Solutions exist that address individual challenges posed by such an environment, but they rely upon mutually incompatible authorization models that are frequently insufficiently expressive. The primary contributions of DisCo are (1) a middleware toolkit for constructing such applications, (2) a unifying authorization abstraction, and (3) a realization of this authorization well suited for expressing partial trust relationships typical of such environments. We focus on the first two of these contributions, [E. Freudenthal et al., (2002)] presents the third.

distributed role-based access control (dRBAC) is a scalable, decentralized trust-management and access-control mechanism for systems that span multiple administrative domains. dRBAC utilizes PKI identities to define trust domains, roles to define controlled activities, and role delegation across domains to represent permissions to these activities. The mapping of controlled actions to roles enables their namespaces to serve as policy roots. dRBAC distinguishes itself from previous approaches by providing three features: (1) third-party delegation of roles from outside a domain's namespace, relying upon an explicit delegation of assignment; (2) modulation of transferred permissions using scalar valued attributes associated with roles; and (3) continuous monitoring of trust relationships over long-lived interactions. The paper describes the dRBAC model and its scalable implementation using a graph approach to credential discovery and validation.

Article Process coordination with fetch-and-increment Share on Authors: Eric Freudenthal View Profile , Allan Gottlieb View Profile Authors Info & Claims ASPLOS IV: Proceedings of the fourth international conference on Architectural support for programming languages and operating systemsApril 1991 Pages 260–268https://doi.org/10.1145/106972.106998Online:01 April 1991Publication History 27citation502DownloadsMetricsTotal Citations27Total Downloads502Last 12 Months17Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access