Development of the attitude toward science in school assessment and its use to investigate the relationship between science achievement and attitude toward science in school

There is much interest in science courses in high school and student attitudes toward those courses. A valid and reliable instrument that measures such attitudes is not available. This study first developed and validated the Attitude toward Science in School Assessment (ATSSA). The instrument was used to evaluate the relationship between such attitude and achievement in science. A low correlation was found between attitude and various achievement tests. A moderate correlation was found between attitude and achievement that included an evaluation of the quality of work, as in a course grade.     

Reasoning about data in middle school science

This case study illustrates instruction in an urban 6th‐grade classroom in which students were learning about mass, volume, and density by attempting to layer (stack) three miscible solutions with differing densities atop one another. The study examines classroom discourse and interaction on the basis of four teaching goals: (a) reaching consensus about which stacks were possible, (b) developing persuasive arguments that separated data from noise, (c) establishing social norms for collective inquiry, and (d) appreciating the epistemological status of scientific knowledge. The study traces the fate of three stacks that students claimed were possible after initial investigations with the solutions. These claims underwent a process of collective validation in which consensus without coercion was the goal, which illustrates emergent standards for backing claims with evidence, as well as for replicability, among the students. Students were successful in achieving three of the four goals, with some qualifications. In relation to Goal 3, which required generalization to other situations, somewhat less success is reported. Limitations in the current standards, difficulties of time allotment in current curricula, and establishing classroom cultures of inquiry are discussed. © 1999 John Wiley & Sons, Inc. J Res Sci Teach 36: 179–199, 1999     

Race-mixing and science in the United States

Scientific racism was widely used as a justification to oppose race-mixing in the United States. Historians have justly criticized this abuse of science, but have overlooked some of the important ways in which science was used in the 1930s and 1940s to overturn scientific racism and opposition to race-mixing. Of particular importance was the cultural anthropology of Franz Boas and the evolutionary biology of Theodosius Dobzhansky, which supplied arguments against racism and fundamentally altered the scientific understanding of race.     

The information seeking behaviour of the users of digital scholarly journals

The article employs deep log analysis (DLA) techniques, a more sophisticated form of transaction log analysis, to demonstrate what usage data can disclose about information seeking behaviour of virtual scholars – academics, and researchers. DLA works with the raw server log data, not the processed, pre-defined and selective data provided by journal publishers. It can generate types of analysis that are not generally available via proprietary web logging software because the software filters out relevant data and makes unhelpful assumptions about the meaning of the data. DLA also enables usage data to be associated with search/navigational and/or user demographic data, hence the name ‘deep’. In this connection the usage of two digital journal libraries, those of EmeraldInsight, and Blackwell Synergy are investigated. The information seeking behaviour of nearly three million users is analyzed in respect to the extent to which they penetrate the site, the number of visits made, as well as the type of items and content they view. The users are broken down by occupation, place of work, type of subscriber (“Big Deal”, non-subscriber, etc.), geographical location, type of university (old and new), referrer link used, and number of items viewed in a session.     

Nature's agents or agents of empire? Entomological workers and environmental change during the construction of the Panama Canal

This essay examines the role that entomological workers played in U.S. public health efforts during the construction of the Panama Canal (1904-1914). Entomological workers were critical to mosquito control efforts aimed at the reduction of tropical fevers such as malaria. But in the process of studying vector mosquitoes, they discovered that many of the conditions that produced mosquitoes were not intrinsic to tropical nature per se but resulted from the human-caused environmental disturbances that accompanied canal building. This realization did not mesh well with an American ideology of tropical triumphalism premised on the notion that the Americans had conquered unalloyed tropical nature in Panama. The result, however, was not a coherent counternarrative but a set of intra-administrative tensions over what controlling nature meant in Panama. Ultimately, entomological workers were loyal not just to the U.S. imperial mission in Panama but also to a modernist culture of science and to the workings of mosquito ecology as they understood them.     

Modeling Skipped and Not‐Reached Items Using IRTrees

When dealing with missing responses, two types of omissions can be discerned: items can be skipped or not reached by the test taker. When the occurrence of these omissions is related to the proficiency process the missingness is nonignorable. The purpose of this article is to present a tree‐based IRT framework for modeling responses and omissions jointly, taking into account that test takers as well as items can contribute to the two types of omissions. The proposed framework covers several existing models for missing responses, and many IRTree models can be estimated using standard statistical software. Further, simulated data is used to show that ignoring missing responses is less robust than often considered. Finally, as an illustration of its applicability, the IRTree approach is applied to data from the 2009 PISA reading assessment.     

Application of the Levenshtein Distance Metric for the Construction of Longitudinal Data Files

The analysis of longitudinal data in education is becoming more prevalent given the nature of testing systems constructed for No Child Left Behind Act (NCLB). However, constructing the longitudinal data files remains a significant challenge. Students move into new schools, but in many cases the unique identifiers (ID) that should remain constant for each student change. As a result, different students frequently share the same ID, and merging records for an ID that is erroneously assigned to different students clearly becomes problematic. In small data sets, quality assurance of the merge can proceed through human reviews of the data to ensure all merged records are properly joined. However, in data sets with hundreds of thousands of cases, quality assurance via human review is impossible. While the record linkage literature has many applications in other disciplines, the educational measurement literature lacks details of formal protocols that can be used for quality assurance procedures for longitudinal data files. This article presents an empirical quality assurance procedure that may be used to verify the integrity of the merges performed for longitudinal analysis. We also discuss possible extensions that would permit merges to occur even when unique identifiers are not available.     

RIM: A Random Item Mixture Model to Detect Differential Item Functioning

In this paper we present a new methodology for detecting differential item functioning (DIF). We introduce a DIF model, called the random item mixture (RIM), that is based on a Rasch model with random item difficulties (besides the common random person abilities). In addition, a mixture model is assumed for the item difficulties such that the items may belong to one of two classes: a DIF or a non-DIF class. The crucial difference between the DIF class and the non-DIF class is that the item difficulties in the DIF class may differ according to the observed person groups while they are equal across the person groups for the items from the non-DIF class. Statistical inference for the RIM is carried out in a Bayesian framework. The performance of the RIM is evaluated using a simulation study in which it is compared with traditional procedures, like the likelihood ratio test, the Mantel-Haenszel procedure and the standardized p -DIF procedure. In this comparison, the RIM performs better than the other methods. Finally, the usefulness of the model is also demonstrated on a real life data set.