Pedagogies such as the Personal Software Process (PSP) shift metrics definition, collection, and analysis from the organizational level to the individual level. While case study research indicates that the PSP can provide software engineering students with empirical support for improving estimation and quality assurance, there is little evidence that many students continue to use the PSP when no longer required to do so. Our research suggests that this "PSP adoption problem" may be due to two problems: the high overhead of PSP-style metrics collection and analysis, and the requirement that PSP users "context switch" between product development and process recording. This paper overviews our initial PSP experiences, our first attempt to solve the PSP adoption problem with the LEAP system, and our current approach called Hackystat. This approach fully automates both data collection and analysis, which eliminates overhead and context switching. However, Hackystat changes the kind of metrics data that is collected, and introduces new privacy-related adoption issues of its own.

This study was designed to develop measures of student competence in conducting scientific inquiry. Two assessment techniques were developed. The first measures Scientific Inquiry Capabilities, variables which are indicators of diverse aspects of competence in conducting scientific inquiry. The second measures Scientific Discovery, an indicator of success in attaining scientific concepts as a result of direct investigations into natural phenomena. Thirty‐two high school students were presented with tasks requiring the building and testing of logical–mathematical models of natural phenomena. The relationship between each Scientific Inquiry Capability and success in making discoveries was tested. Several Inquiry Capabilities were identified as strongly correlated with success in Discovery, notably: Proportional Reasoning, the Coordination of Theory with Evidence, and the Disposition to Search for Necessary Underlying Principles. © 2000 John Wiley & Sons, Inc. J Res Sci Teach 37: 938–962, 2000

This chapter highlights critical lessons learned during the past six years during the development of a capstone graduate educational technology course, teaching School Library Media (SLMS) pre-service students how to develop learner centered, knowledge centered and assessment centered Web-based learning tools; in short, to enable them to become change agents in their educational communities. A large northeastern University has cultivated educational partnerships that bring together University students with their professional, in-service, PreK-12 counterparts to explore issues of technology in education, pedagogy, theory, curriculum, information literacy, assessment, and evaluation. Unlike traditional courses with prepackaged academic assignments, this course engages school library media specialists with real-world teaching and learning situations that are frequently ill-structured, often chaotic, and collaboratively defined by the learning needs of all participants (PreK-12 through university; in-service and pre-service). The strengths and weaknesses of the course are candidly discussed with recommendations for improvement.

Efforts aimed at broadening participation in science, technology, engineering, and mathematics (STEM) require a holistic presentation of the state of racial and gender participation. Statistics currently used to describe participation often include raw counts of degrees and the percentages of demographic groups receiving STEM degrees. While these data provide insights into demographic trends, they do not present the complete picture because these "traditional" statistics do not capture how well a field of study reflects-or is proportionally similar to-a larger body, such as the college population. If the goal of broadening participation in STEM education is to ensure that all racial and gender groups are proportionally represented, analysts require direct measures of representation. In this article, we present a novel metric that assesses the degree to which groups are overrepresented or underrepresented in a given field. This metric calculates field-specific representation by comparing the proportion of degrees awarded to members of a demographic group in a specific field of study with the proportion of all degrees awarded to that group. Using data from the National Science Foundation and the Department of Education, we demonstrate the efficacy of this representation metric and show that it provides new insights into STEM participation levels for women and other groups considered to be underrepresented. While traditional measurements show the increasing number of degrees awarded to and the increasing share of underrepresented minority students in STEM, our metric revealed that underrepresented minorities remain underrepresented in STEM fields, especially in engineering and the natural sciences.

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