Becoming Racially Literate About Data and Data-Literate About Race: Data Visualizations in the Classroom as a Site of Racial-Ideological Micro-Contestations

Data visualizations are now commonplace in the public media. The ability to interpret and create such visualizations, as a form of data literacy, is increasingly important for democratic participation. Yet, the cross-disciplinary knowledge and skills needed to produce and use data visualizations and to develop data literacy are not fluidly integrated into traditional K–12 subject areas. In this article, we nuance and complicate the push for data literacy in STEM reform efforts targeting youth of color. We explore a curricular reform project that integrated explicit attention to issues pertaining to the collection, analysis, interpretation, representation, visualization, and communication of data in an introductory computer science class. While the study of data in this unit emphasized viewing and approaching data in context, neither the teacher nor the students were supported in negotiating the racialized context of data that emerged in classroom discussions. To better understand these dynamics, we detail the construct of racial literacy and develop an interpretative framework of racial-ideological micro-contestations. Through an in-depth analysis of a classroom interaction using this framework, we explore how contestations about race can emerge when data visualizations from the public media are incorporated into STEM learning precisely because the contexts of data are often racialized. We argue that access to learning about data visualization, without a deep interrogation of race and power, can be counterproductive and that efforts to develop authentic data literacy require the concomitant development of racial literacy.     

“Explain” in scientific discourse

The philosophical literature on scientific explanation contains a striking diversity of accounts. I use novel empirical methods to address this fragmentation and assess the importance and generality of explanation in science. My evidence base is a set of 781 articles from one year of the journal Science, and I begin by applying text mining techniques to discover patterns in the usage of “explain” and other words of philosophical interest. I then use random sampling from the data set to develop and test a classification scheme for scientific explanation. My results show that explanation and inference to the best explanation are ubiquitous in science, that they occur across a wide range of scientific disciplines, and that they are a goal of scientific practise. These explanations and inferences to the best explanation come in a diversity forms, which at least partially justifies the fragmentation of philosophical accounts. I draw two methodological lessons: first that text mining can enhance traditional conceptual analysis by establishing facts about word usage; and second that random sampling of cases can increase our confidence that a philosophical account applies in general. These empirical techniques supplement traditional philosophical methods.     

A computational model of spatial visualization capacity

Visualizing spatial material is a cornerstone of human problem solving, but human visualization capacity is sharply limited. To investigate the sources of this limit, we developed a new task to measure visualization accuracy for verbally-described spatial paths (similar to street directions), and implemented a computational process model to perform it. In this model, developed within the Adaptive Control of Thought-Rational (ACT-R) architecture, visualization capacity is limited by three mechanisms. Two of these (associative interference and decay) are longstanding characteristics of ACT-R’s declarative memory. A third (spatial interference) is a new mechanism motivated by spatial proximity effects in our data. We tested the model in two experiments, one with parameter-value fitting, and a replication without further fitting. Correspondence between model and data was close in both experiments, suggesting that the model may be useful for understanding why visualizing new, complex spatial material is so difficult.     

The SIMCLAS Model: Simultaneous Analysis of Coupled Binary Data Matrices with Noise Heterogeneity Between and Within Data Blocks

In many research domains different pieces of information are collected regarding the same set of objects. Each piece of information constitutes a data block, and all these (coupled) blocks have the object mode in common. When analyzing such data, an important aim is to obtain an overall picture of the structure underlying the whole set of coupled data blocks. A further challenge consists of accounting for the differences in information value that exist between and within (i.e., between the objects of a single block) data blocks. To tackle these issues, analysis techniques may be useful in which all available pieces of information are integrated and in which at the same time noise heterogeneity is taken into account. For the case of binary coupled data, however, only methods exist that go for a simultaneous analysis of all data blocks but that do not account for noise heterogeneity. Therefore, in this paper, the SIMCLAS model, being a Hierarchical Classes model for the simultaneous analysis of coupled binary two-way matrices, is presented. In this model, noise heterogeneity between and within the data blocks is accounted for by downweighting entries from noisy blocks/objects within a block. In a simulation study it is shown that (1) the SIMCLAS technique recovers the underlying structure of coupled data to a very large extent, and (2) the SIMCLAS technique outperforms a Hierarchical Classes technique in which all entries contribute equally to the analysis (i.e., noise homogeneity within and between blocks). The latter is also demonstrated in an application of both techniques to empirical data on categorization of semantic concepts.     

A comparison of binary and rating techniques in the signal detection analysis of recognition memory

Forty-eight subjects were run on a recognition memory task employing low, medium, or high meaningfulness (m) CVC items and utilizing either a binary (yes—no) or rating response technique. Significant differences were obtained between levels of m with higher m items resulting in larger d' values. The interaction term was also significant reflecting a large difference between the binary-rating techniques with high meaningfulness items. The results indicate that the binary and rating techniques do not reveal identical findings in the analysis of recognition memory data and that the frequent use of the rating technique by researchers in the analysis of memory data may not be appropriate.     

Behavioral science integration: A practical framework of multi-level converging evidence for behavioral science theories

Several frameworks exist to help science in organizing known information, connecting previously disparate phenomena, promoting understanding, and identifying gaps in knowledge. This paper integrates previous frameworks that have been used in the behavioral sciences to produce a more comprehensive, specific, and complete framework, consonant with the process of scientific discovery as based on multiple, independent, and converging lines of evidence. This multi-level convergence framework is designed specifically for the analysis and understanding of human cognitive/behavioral traits, in contrast to more general frameworks that are designed to be applied relatively widely (such as across all information processing systems or across all biological organisms). This integrated framework overcomes gaps in prior frameworks, provides a more complete picture of the interrelationships between various aspects of the behavioral sciences, and can aid in evaluating theories, both for comparison and identifying gaps in evidentiary support.     

GazeAlyze: a MATLAB toolbox for the analysis of eye movement data

This article presents GazeAlyze, a software package, written as a MATLAB (MathWorks Inc., Natick, MA) toolbox developed for the analysis of eye movement data. GazeAlyze was developed for the batch processing of multiple data files and was designed as a framework with extendable modules. GazeAlyze encompasses the main functions of the entire processing queue of eye movement data to static visual stimuli. This includes detecting and filtering artifacts, detecting events, generating regions of interest, generating spread sheets for further statistical analysis, and providing methods for the visualization of results, such as path plots and fixation heat maps. All functions can be controlled through graphical user interfaces. GazeAlyze includes functions for correcting eye movement data for the displacement of the head relative to the camera after calibration in fixed head mounts. The preprocessing and event detection methods in GazeAlyze are based on the software ILAB 3.6.8 Gitelman (Behav Res Methods Instrum Comput 34(4), 605-612, 2002). GazeAlyze is distributed free of charge under the terms of the GNU public license and allows code modifications to be made so that the program's performance can be adjusted according to a user's scientific requirements.     

Deduction,inference and illation

From the standpoint of the theory of medicine, a formulation is given of three types of reasoning used by physicians. The first is deduction from probability models (as in prognosis or genetic counseling for Mendelian disorders). It is a branch of mathematics that leads to predictive statements about outcomes of individual events in terms of known formal assumptions and parameters. The second type is inference (as in interpreting clinical trials). In it the arguments from replications of the same process (‘data’) lead to conclusions about the parameters of a system, without calling into question either the probabilistic model or the criteria of evidence. The third is illation (as in the elucidation of symptoms in a patient). It is a process whereby, in the light of the total evidence and the conclusions from the other types of reasoning, one may modify, expand, simplify or demolish a conceptual framework proposed for deductions, and modify the nature of the evidence sought, the criteriology, the axioms, and the surmised complexity of the scientific theory. (The process of diagnosis as applied to a patient may in extreme cases lead to the discovery of an entirely new disease with its own, quite new, set of diagnostic criteria. This course cannot be accommodated inside either of the other two types of reasoning.) Illation has something of the character of Kuhn's ‘scientific revolution’ in physics; but it differs in that it is the nature, not the degree or frequency of change that distinguishes it from Kuhn's ‘normal science.’     

A standard conceptual framework for the study of subjective time

Research on the mental representation of time (‘subjective time’) has provided broad insights into the nature of time perception and temporal processing. As the field comprises different scientific disciplines, such as psychology, philosophy, and neuroscience, studies differ with regard to the basic terms and concepts used. For this reason, research on subjective time lacks a coherent conceptual system. We argue that research in the field of subjective time should aim at establishing such a system, i.e., a more standardized terminology, in order to strengthen its theoretical basis and to support an efficient communication of results. Based on key empirical findings and concepts that are commonly (but inconsistently) used in the literature, we argue for a conceptual framework for the study of subjective time that differentiates between three types of mental representations of time: basic temporal processing, time perception in terms of passage, and time perception in terms of duration.     

An overview of scientific visualization techniques applied to experimental psychology

This paper presents an overview of current data visualization techniques. The various types of graphics, such as contour plots, surface plots, scatterplot matrices, and dynamic spinning, are described. Then, using a data set typical of the field, the potential merits and pitfalls of each visualization technique are presented in terms of what aspects of the data they make explicit. The results illustrate the benefits that a psychological researcher can expect from data visualization capabilities.     

Relationships among measures of visual imagery

As a step toward the development of an objective measure of visualization ability, five tasks which yielded seven separate measures were administered to 74 Ss and intercorrelated. The tasks were: paired associate recall improvement (RI), recognition memory (RM), the Bett's Questionnaire on Mental Imagery (QMI) as revised by Sheehan (1967a, b), a task calling for the production of visual images (PVI) where rated vividness and latency of image attainment were measured, and a task requiring the imagining of emotional scenes (ES) where rated vividness and rated anxiety were measured. The results indicated that although paired associate learning scores per se were related to RM, neither of the memory measures of visualization (RI and RM) were related to any of the other visualization measures. A cluster of intercorrelations was obtained among self-ratings of image vividness on the QMI, PVI and ES tasks. This cluster also included a measure of latency of report of “best image” attainable derived from the PVI task. None of the measures correlated with anxiety ratings on the ES task. These findings were discussed in relation to previous studies and the potential value of the latency measure as an adjunct measure of visualization ability was noted.     

Of red planets and indigo computers: Mars database visualization as an example of platform downsizing

The last decade has witnessed tremendous advancements in the computer hardware and software used to perform scientific visualization. In this paper, we consider how the visualization of a particular data set, the digital terrain model derived from the Viking orbiter imagery, has been realized in four distinct projects over this period. These examples serve to demonstrate how the vast improvements in computational performance both decrease the cost of such visualization efforts and permit an increasing level of interactivity. We then consider how even today’s graphical systems require the visualization designer to make intelligent choices and tradeoffs in database rendering. Finally, we discuss how insights gleaned from an understanding of human visual perception can guide these design decisions, and suggest new options for visualization hardware and software.     

When babies watch television: Attention-getting,attention-holding,and the implications for learning from video material

The recent increase in the availability of infant-directed video material (e.g., Baby Einstein) and the corresponding increase in the amount of time that infants and toddlers spend viewing them have prompted concern among parents and professionals that these media might impede aspects of cognitive and social development. In contrast, supporters and producers of these media contend that, as has been shown with preschool children, age-appropriate videos with carefully selected educational content can provide an opportunity to support and enhance early learning. The scientific evidence for these opposing views is reviewed, evaluated, and interpreted in a developmental framework.     

Analysis of meteorological conditions in Spain by means of clustering techniques

A comprehensive analysis of clustering techniques is presented in this paper through their application to data on meteorological conditions. Six partitional and hierarchical clustering techniques (k-means, k-medoids, SOM k-means, Agglomerative Hierarchical Clustering, and Clustering based on Gaussian Mixture Models) with different distance criteria, together with some clustering evaluation measures (Calinski–Harabasz, Davies–Bouldin, Gap and Silhouette criterion clustering evaluation object), present various analyses of the main climatic zones in Spain. Real-life data sets, recorded by AEMET (Spanish Meteorological Agency) at four of its weather stations, are analyzed in order to characterize the actual weather conditions at each location. The clustering techniques process the data on some of the main daily meteorological variables collected at these stations over six years between 2004 and 2010.     

Proximal Intentions,Non-executed Proximal Intentions and Change of Intentions

This paper investigates the conceptual and empirical possibility of non-executed, non-conscious proximal intentions, i.e., non-conscious proximal intentions to act that do not turn into a final act, but perhaps are vetoed or overcome by an alternative action. It constructs a conceptual framework in which such cases are justifiably considered ‘proximal intentions’. This is achieved by combining Alfred Mele’s notion of non-conscious proximal intentions together with the notion of trying or striving taken from Brian O’Shaughnessy’s model of action. With this framework in hand we analyze empirical electroencephalography (EEG) findings regarding ‘change of intention’ scenarios. Prior to EEG development (and other brain imaging techniques), one would never consider non-executed, non-conscious proximal intentions a case of intention at all. However, with technological and interpretable development of EEG, a whole group of so called ‘intentions’ appears, and the main aim of the paper is to justify this expanded usage.     

The Living Fossil of Human Judgment

Although the biases and anomalies characterizing psychometric data should serve as conclusive evidence of systematic flaws in scientific methodology, these problems are usually ignored, which reduces empirical psychology to the closed system of its error theory. However, psychometric scores are ambiguous, and response-shifts and fluctuating validities point to fundamental differences in what the measuring-apparatus questionnaire records and how the measuring-apparatus person judges. Therefore, empirical methods fail when psychology requires evidence-based knowledge about cognitive processes and phenomena. Correcting these flaws requires a reassessment of basic scientific premises and careful consideration of Homo sapiens’ biosemiotic heuristics. Based on comprehensive biopsychosocial, data, the author reconstructs the evolutionary axioms of self-referenced cognitions and reveals what is usually obscured by the axioms of normal science. He substantiates the need for a paradigm shift toward basic bio–cultural principles and an evolutionary understanding of human thinking and behavior.