Search

Closed laboratories are becoming an increasingly popular approach to teaching introductory computer science courses. Unlike open laboratories that tend to be an informal environment provided for students to practice their skills with attendance optional, closed laboratories are structured meeting times that support the lecture component of the course, and attendance is required. This paper reports on an integrated approach to designing, implementing, and assessing laboratories with an embedded instructional research design. The activities reported here are parts of a departmentwide effort not only to improve student learning in computer science and computer engineering (CE) but also to improve the agility of the Computer Science and Engineering Department in adapting the curriculum to changing technologies, incorporate research, and validate the instructional strategies used. This paper presents the design and implementation of the laboratories and the results and analysis of student performance. Also described in this paper is cooperative learning in the laboratories and its impact on student learning. Index Terms--Computer science education, cooperative learning, instructional design, introductory computer science/computer engineering (CS/CE) courses, laboratories.

We present an intelligent system for satellite sea ice image analysis named Advanced Reasoning using Knowledge for Typing Of Sea ice (ARKTOS). ARKTOS performs fully automated analysis of synthetic aperture radar (SAR) sea ice images by mimicking the reasoning process of sea ice experts. ARKTOS automatically segments a SAR image of sea ice, generates descriptors for the segments of the image, and then uses expert system rules to classify these sea ice features. ARKTOS also utilizes multisource data fusion to improve classification and performs belief handling using Dempster-Shafer. As a software package, ARKTOS comprises components in image processing, rule-based classification, multisource data fusion, and graphical user interface-based knowledge engineering and modification. As a research project over the past ten years, ARKTOS has undergone phases such as knowledge acquisition, prototyping, refinement, evaluation, deployment, and operationalization at the U.S. National Ice Center. In this paper, we focus on the methodology, evaluations, and classification results of ARKTOS. Index Terms--Data fusion, Dempster-Shafer belief theory, intelligent image analysis, rule-based system, sea ice classification.

Research describing the restricted growth concept for separating objects is presented. Algorithms are developed for the analysis of irregular unstructured objects such as those found in natural scenes.

This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters.

Closed laboratories are becoming an increasingly popular approach to teaching introductory computer science courses, as they facilitate structured problem-solving and cooperation. However, most closed laboratories have been designed and implemented without embedded instructional research components for constant evaluation of the laboratories' effectiveness. As a result, it is not convenient to maintain and improve the laboratories over time so that they adapt to changing CS topics, curricula, and student needs. This article reports on an integrated framework for designing, implementing, and maintaining laboratories with embedded instructional research design. Although the activities reported here are part of our department-wide effort to cover CS0, CS1, and CS2, we focus here on the design and implementation of the labs for CS1.

The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly to the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This article focuses on some of the key research findings of the eight nodes, organized into six topics: (1) improving census and survey data-quality and data collection methods; (2) using alternative sources of data; (3) protecting privacy and confidentiality by improving disclosure avoidance; (4) using spatial and spatio-temporal statistical modeling to improve estimates; (5) assessing data cost and data-quality tradeoffs; and (6) combining information from multiple sources. The article concludes with an evaluation of the ability of the FSS to apply the NCRN's research outcomes, suggests some next steps, and discusses the implications of this research-network model for future federal government research initiatives. [ABSTRACT FROM AUTHOR]