Measuring Search Engine Quality

The effectiveness of twenty public search engines is evaluated using TREC-inspired methods and a set of 54 queries taken from real Web search logs. The World Wide Web is taken as the test collection and a combination of crawler and text retrieval system is evaluated. The engines are compared on a range of measures derivable from binary relevance judgments of the first seven live results returned. Statistical testing reveals a significant difference between engines and high intercorrelations between measures. Surprisingly, given the dynamic nature of the Web and the time elapsed, there is also a high correlation between results of this study and a previous study by Gordon and Pathak. For nearly all engines, there is a gradual decline in precision at increasing cutoff after some initial fluctuation. Performance of the engines as a group is found to be inferior to the group of participants in the TREC-8 Large Web task, although the best engines approach the median of those systems. Shortcomings of current Web search evaluation methodology are identified and recommendations are made for future improvements. In particular, the present study and its predecessors deal with queries which are assumed to derive from a need to find a selection of documents relevant to a topic. By contrast, real Web search reflects a range of other information need types which require different judging and different measures.     

Analysis of format duplication in an academic library collection

The issue of format duplication in academic library collections is increasingly complicated and perplexing in an environment of static or dwindling resources, soaring user expectations, dynamic access models, and inconsistent and changing publisher and vendor pricing structures. The problem is further complicated for libraries serving a university with multiple campus locations. This paper surveys recent investigative projects and highlights the work of a Duplicate Formats Task Force at Pennsylvania State University charged with determining the extent and nature of format duplication at that institution.     

“There’s nothing I hate more than a racist:” (Re)centering whiteness in American Horror Story: Coven

This article examines race and racism in the popular cable horror-drama American Horror Story: Coven. Centered around a feud between a white witch coven and a black, voodoo group of witches, Coven overtly deals with the issue of racism, but presents racism as a phenomenon contained in the past; something that does not happen anymore. Using critical post-race theory and scholarship on colorblind racism, whiteness, as well as black feminist theory, I show that although the narrative of Coven explicitly decries racism, it simultaneously invests in colorblind racist narratives and dialogue. Whiteness is central in Coven, operating through discourse that ignores racial difference and contemporary forms of oppression and erasure. Coven uses graphic depictions of violence against black bodies, makes explicit connections between black sexuality and animals, and uses exotic and aggressive stereotypes of black women to tell its story. Thus, Coven’s narrative both condemns racism and relies on it. This article shows that the contemporaneous presentations of explicit anti-racist dialogue and racist imagery work to (re)place whiteness at the center of Coven, a TV show that purports to forward a message of equality and justice for those who are different.     

Community College Counseling: Why Are Research Outcomes So Elusive?

This article focuses on counseling research in the community college context. The article suggests the need for a robust community college knowledge base, describes some limitations of the current community college literature, and suggests a framework for more effective work in this area. The authors’ own experiences and selected examples of published studies are used as illustrations of the hurdles encountered and solutions available when examining counseling theories, practices, and outcomes in 2‐year settings.     

The Potential Impact of COVID-19 on Student Learning and How Schools Can Respond

There is no denying the impact that the coronravirus disease (COVID-19) outbreak has had on many aspects of our lives. This article looks at the potential impact of COVID-19 on student learning as schools abruptly morphed into virtual learning environments using data from several instructional, practice, and assessment solutions offered by Renaissance. First, three hypothetical learning scenarios are considered using normative data from Star assessments to explore the potential impact on reading and math test performace. Next, data on Focus Skills are used to highlight which grades may have missed the most foundational math and reading content if instruction was stopped or reduced. Last, data from two of Renaissance's practice tools are used to evaluate whether students were practicing key skills following school closures. The article concludes that academic decline will likely occur but may be tempered by the increased use of practice tools; effects may look different for math and reading; and may impact grades and schools differently. As such, schools may need to leverage decision-making frameworks, such as the Multi-tiered Systems of Support/Response-to-Intervention (MTSS/RTI) framework, more than ever to identify needs and target instruction where it matters most when school begins in fall 2020.