Exploring Quantitative Skills Area 107 Pdf Download

Research has demonstrated the importance of integrating information literacy into teacher education programs.23 Lee, Reed, and Laverty explored the degree to which one teacher education program had prepared preservice teachers for teaching information literacy and found than more than half of the participants had neither acquired new skills nor felt that they had the opportunity to improve research skills in their program.24 In a survey of education majors and school media specialists, Stockham and Collins found that many education students and recent graduates were unfamiliar with information literacy terminology and concepts.25 To ameliorate this deficiency, teacher educators and librarians alike have encouraged collaboration between the two to effectively teach information literacy skills to students.26 Nonetheless, a review of the literature reveals a lack of studies that provide a direct assessment of the information literacy skills among teacher education students. Studies that do address this topic use surveys in which these students self-report knowledge and familiarity with various information literacy skill areas and topics.27

The purpose of this quantitative analysis is to both get a better sense of the information literacy skills of these students, as measured by the iSkills assessment, and to determine whether certain variables are predictive of performance on that assessment. Descriptive statistics and correlations among study variables were examined to provide an indication of possible patterns and relationships in the data. Hierarchical linear regression analyses were conducted to examine the unique effects of demographic and academic variables as predictors of the overall performance on the iSkills assessment. Several of the predictor variables were recoded to be dichotomous. These variables included gender (0 = female, 1 = male), best language spoken (0 = English as best language, 1 = other language reported as best language), race (0 = Caucasian, 1 = other race reported), and transfer credits (0 = no transfer credits, 1 = has transfer credits). This coding for some of the variables differs from Fabbi due to the composition of the sample in this study. Among this study group, for example, the number of individuals in different ethnic groups was too small for unique comparisons.

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The Institutional Skill Area report was also useful in breaking down areas of student need in broad categories, indicating a need for explicit instruction related to defining an information need and accessing information. However, the Aggregate Task Feedback report was too explicitly tied to specific subtasks within the iSkills assessment to be as useful to the researcher. For example, indicating that a certain percentage of test takers had answered a single question within a seven-part task correctly while a different percentage of test takers had answered a similar question within a different task correctly was difficult to parse, especially given that ETS does not provide copies of the scenarios and test questions. Ultimately, the Institutional Skill Area Report was more useful for gaining a broad understanding of student skills for use in future semesters, as it provided insight into student performance on the seven skill areas rather than individual test items. Prior instruction in first- and second-year courses emphasized the evaluation of information, incorporating topics such as the difference between popular and scholarly sources or identifying criteria for the evaluation of information sources. The results of this study indicated that upper-level students are performing well in this area but less so in defining an information need and accessing that information. As a result, instruction in the lower grades has been adjusted to more deliberately address those skill areas highlighted by the iSkills results.

DS 101 uses a quantitative approach to explore fundamental concepts in data science. Students will develop key skills in programming and statistical inference as they interact with real-world data sets across a variety of domains. Ethical ramifications of data collection, data-driven decision-making, and privacy will be explored.

DSCI 311 prepares students to successfully apply computational and statistical techniques to upper-division coursework in data science as well as quantitative, data-driven courses in other domains or subject areas. Topics include managing data with software programs, data cleaning, handling text, dimensionality, principal components analysis, regression, classification and inference.

We typically think of early literacy and numeracy skills as separate areas of development. Generally, we assess these skills using different tasks, and we use different instructional activities to promote skill acquisition in these areas.

These spatial skills have been linked to mathematical competence (Cheng & Mix, 2014; Verdine, Irwin, Golinkoff, & Hirsh-Pasek, 2014). Interestingly, the development of both quantitative and spatial language appears to be shaped by the kinds of experiences and interactions that young children have with their caregivers (e.g., Gunderson & Levine, 2011; Jirout & Newcombe, 2015; Pruden, Levine, & Huttenlocher, 2011).

What do these connections between early literacy and numeracy mean in the preschool classroom? If we focus on language as a foundation for skills in both areas, research suggests that rich language environments may support the development of both early literacy and early numeracy skills.

The purpose of the Synthesis course is to provide students with the opportunity to synthesize the knowledge, skills, and values gained from the Mason Core curriculum. Synthesis courses strive to expand students' ability to master new content, think critically, and develop life-long learning skills across the disciplines. While it is not feasible to design courses that cover "all" areas of general education, Synthesis courses should function as a careful alignment of disciplinary goals with a range of Mason Core learning outcomes.

Students must fulfill disciplinary area requirements by taking no fewer than two course credits in the humanities and arts, two in the sciences, and two in the social sciences. Students must also fulfill skills requirements by taking at least two course credits in quantitative reasoning, two course credits in writing, and courses to further their language proficiency. Depending on their level of accomplishment in foreign languages at matriculation, students may fulfill this last requirement with one, two, or three courses or by certain combinations of course work and approved study abroad.

Start with a pre-calculus course with the Math Learning Center or build skills in our innovative calculus sequence, statistics, quantitative reasoning, discrete math, math education, and many other courses.

By crafting personalized plans of study, we prepare you to be an educational researcher, theorist, or practitioner who works to serve diverse populations. Our department comprises four interconnected areas of emphasis, which provide depth and flexibility to customize your program of study and become an expert in the research skills to build knowledge about the ways students think and learn.

The focus of this paper is on the design of affective-oriented e-learning courses. Educational Psychology and new developments in Human Computer Interaction stress the need for affective considerations, which are neglected in the current practice of e-learning design. In this study the main affective determinant motivation to learn is under investigation. In order to meet this objective a research framework is proposed based on multidisciplinary approach that integrates usability and pedagogical quality and an empirical study exploring quantitative relations between usability attributes and motivation to learn is described. Results provide e-learning designers with eight usability attributes that highly correlate and predict motivation to learn, a crucial affective factor for learning success.

Click the image above to browse teaching activities related to quantitative skills, thinking, and reasoning. Collections are contributed by faculty and may include working with real data, models, and more.

This summer and fall, teams of faculty and technologists collaborated intensively to launch QLAB, a shared framework for curating, implementing and assessing online instructional modules for quantitative skills (QS) and reasoning for just-in-time review and skill-building across disciplines. The goal of the QLAB project is to assist faculty teaching quantitative subjects who find they need methods to support students with gaps in preparation. The strategy draws on a body of research in higher education and experience at our institutions showing that online modules can be a beneficial component of an overall QS support program.

Developing online resources that can be used in multiple contexts to help students strengthen their quantitative skills serves two purposes. First, by demonstrating the relevance of specific QS in various disciplinary contexts, students learn to view quantitative skills as fundamental and transferable skills that they can draw on in many areas of their liberal arts experience. Second, the consortial effort allows us to collect meaningful data about the effectiveness of the various modules for a greater number of students in a wider variety of contexts. Using what we learn in this pilot, we plan to expand the collection of useful modules.

A major initiative of the QS group is the QLAB Project, an initiative to develop a set of online Q-bit modules to support students with quantitative skills and reasoning across the disciplines.

Quantitative skills are increasingly important in biological sciences (Bialek and Botstein, 2004; Cohen, 2004; Ramaley, 2004; Speth et al., 2010; Colon-Berlingeri and Burrowes, 2011; Feser et al., 2013). Despite the importance of such skills, it has been found that many biology students struggle with quantitative skills such as performing simple calculations, creating and interpreting graphical representations of data or functional relationships, and creating arguments based on numerical data (Speth et al., 2010; Feser et al., 2013). There is a concern that, even though biology students are required to take many math courses leading up to and as part of their biology degrees, they are still not prepared to a deep enough level of quantitative thinking. Students often seem unable to synthesize their own analyses or develop novel mathematical representations of biological processes, as required in the new professional world of biology (Bialek and Botstein, 2004). To be successful as biologists, students need to understand mathematical concepts, but they must also be able to fluently connect these concepts to variables in the natural world and relate mathematical measures to measurements that are taken in the lab or in the field (Aikens and Dolan, 2014). 9af72c28ce