scholarly journals Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy

2019 ◽  
Vol 18 (2) ◽  
pp. es2 ◽  
Author(s):  
Melissa K. Kjelvik ◽  
Elizabeth H. Schultheis
Keyword(s):  
Data Science ◽  
Scientific Research ◽  
Quantitative Reasoning ◽  
Future Research ◽  
Data Sets ◽  
Data Literacy ◽  
Using Data ◽  
And Mathematics ◽  
Authentic Data ◽  
The Impact

Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students’ data-literacy abilities.

scholarly journals Bridging the gap: blending spatial skills instruction into a technology teacher preparation programme

2021 ◽  
Author(s):  
Diarmaid Lane ◽  
Sheryl Sorby
Keyword(s):  
Teacher Preparation ◽  
Gender Gap ◽  
Future Research ◽  
First Year ◽  
Spatial Skills ◽  
First Year Students ◽  
Base Level ◽  
Technology Teacher ◽  
And Mathematics ◽  
The Impact

AbstractIn recent years, there has been a surge in research in spatial thinking across the international community. We now know that spatial skills are malleable and that they are linked to success across multiple disciplines, most notably Science, Technology, Engineering and Mathematics (STEM). While spatial skills have been examined by cognitive scientists in laboratory environments for decades, current research is examining how these skills can be developed in field-based environments. In this paper, we present findings from a study within a Technology Teacher preparation programme where we examined first-year students’ spatial skills on entry to university. We explain why it was necessary to embed a spatial skills intervention into Year 1 of the programme and we describe the impact that this had on students’ spatial scores and on academic performance. The findings from our study highlight a consistent gender gap in spatial scores at the start of the first-year with female students entering the Technology Teacher preparation programme at a lower base level than male students. We describe how we integrated spatial development activities into an existing course and how an improvement in spatial scores and overall course performance was observed. The paper concludes by discussing the long-term sustainability of integrating spatial interventions within teacher preparation programmes while also highlighting the importance of future research to examine spatial skills as a fundamental component of technological capability.

The Impact of Job Characteristics on Burnout Among Chinese Correctional Workers

2016 ◽  
Vol 62 (2) ◽  
pp. 551-570 ◽  
Author(s):  
Xiaohong Jin ◽  
Ivan Y. Sun ◽  
Shanhe Jiang ◽  
Yongchun Wang ◽  
Shufang Wen
Keyword(s):  
Role Conflict ◽  
Job Stress ◽  
Correctional Officers ◽  
Job Characteristics ◽  
Community Correction ◽  
Future Research ◽  
Role Clarity ◽  
Occupational Burnout ◽  
Using Data ◽  
The Impact

Job burnout has long been recognized as a common occupational hazard among correctional workers. Although past studies have investigated the effects of job-related characteristics on correctional staff burnout in Western societies, this line of research has largely been absent from the literature on community corrections in China. Using data collected from 225 community correction workers in a Chinese province, this study assessed the effects of positive and negative job characteristics on occupational burnout. Positive job characteristics included job autonomy, procedural justice, and role clarity. Negative characteristics included role conflict, job stress, and job dangerousness. As expected, role clarity tended to reduce burnout, whereas role conflict, job stress, and job dangerousness were likely to produce greater burnout among Chinese community correction workers. Male correctional officers were also subjected to a higher level of burnout than their female coworkers. Implications for future research and policy were discussed.

scholarly journals The Efficacy of Ethnic Stacking: Military Defection during Uprisings in Africa

2019 ◽  
Vol 5 (4) ◽  
pp. 695-702 ◽  
Author(s):  
Julien Morency-Laflamme ◽  
Theodore McLauchlin
Keyword(s):  
Large Scale ◽  
Armed Forces ◽  
Future Research ◽  
Group Loyalty ◽  
Limited Support ◽  
History Of ◽  
Group Members ◽  
Using Data ◽  
The Cost ◽  
The Impact

Abstract Does ethnic stacking in the armed forces help prevent military defection? Recent research, particularly in Africa and the Middle East, suggests so; by favoring in-groups, regimes can keep in-group soldiers loyal. In-group loyalty comes at the cost of antagonizing members of out-groups, but many regimes gladly run that risk. In this research note, we provide the first large-scale evidence on the impact of ethnic stacking on the incidence of military defection during uprisings from below, using data on fifty-seven popular uprisings in Africa since formal independence. We find clear evidence for the downside: ethnic stacking is associated with more frequent defection if out-group members are still dominant in the armed forces. We find more limited support for the hypothesized payoff. Ethnic stacking may reduce the risk of defection, but only in regimes without a recent history of coup attempts. Future research should therefore trace the solidification of ethnic stacking over time.

scholarly journals Limitations of information extraction methods and techniques for heterogeneous unstructured big data

2019 ◽  
Vol 11 ◽  
pp. 184797901989077 ◽  
Author(s):  
Kiran Adnan ◽  
Rehan Akbar
Keyword(s):  
Big Data ◽  
Information Extraction ◽  
Extraction Methods ◽  
Unstructured Data ◽  
Future Research ◽  
Data Sets ◽  
Data Types ◽  
Efficiency And Effectiveness ◽  
Single Data ◽  
The Impact

During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

Outliers in L2 Research in Applied Linguistics: A Synthesis and Data Re-Analysis

2020 ◽  
Vol 40 ◽  
pp. 26-55 ◽  
Author(s):  
Christopher Nicklin ◽  
Luke Plonsky
Keyword(s):  
Phase I ◽  
Relative Effectiveness ◽  
Reaction Time Data ◽  
Future Research ◽  
Time Data ◽  
Two Phase ◽  
Raw Data ◽  
Study Results ◽  
Using Data ◽  
The Impact

AbstractData from self-paced reading (SPR) tasks are routinely checked for statistical outliers (Marsden, Thompson, & Plonsky, 2018). Such data points can be handled in a variety of ways (e.g., trimming, data transformation), each of which may influence study results in a different manner. This two-phase study sought, first, to systematically review outlier handling techniques found in studies that involve SPR and, second, to re-analyze raw data from SPR tasks to understand the impact of those techniques. Toward these ends, in Phase I, a sample of 104 studies that employed SPR tasks was collected and coded for different outlier treatments. As found in Marsden et al. (2018), wide variability was observed across the sample in terms of selection of time and standard deviation (SD)-based boundaries for determining what constitutes a legitimate reading time (RT). In Phase II, the raw data from the SPR studies in Phase I were requested from the authors. Nineteen usable datasets were obtained and re-analyzed using data transformations, SD boundaries, trimming, and winsorizing, in order to test their relative effectiveness for normalizing SPR reaction time data. The results suggested that, in the vast majority of cases, logarithmic transformation circumvented the need for SD boundaries, which blindly eliminate or alter potentially legitimate data. The results also indicated that choice of SD boundary had little influence on the data and revealed no meaningful difference between trimming and winsorizing, implying that blindly removing data from SPR analyses might be unnecessary. Suggestions are provided for future research involving SPR data and the handling of outliers in second language (L2) research more generally.

A Network Data Science Approach to People Analytics

2019 ◽  
Vol 32 (2) ◽  
pp. 28-51 ◽  
Author(s):  
Nan Wang ◽  
Evangelos Katsamakas
Keyword(s):  
Power Law ◽  
Data Science ◽  
Weighted Graph ◽  
Future Research ◽  
Network Data ◽  
Development Organization ◽  
Centrality Metrics ◽  
Science Approach ◽  
Future Research Directions ◽  
Using Data

The best companies compete with people analytics. They maximize the business value of their people to gain competitive advantage. This article proposes a network data science approach to people analytics. Using data from a software development organization, the article models developer contributions to project repositories as a bipartite weighted graph. This graph is projected into a weighted one-mode developer network to model collaboration. Techniques applied include centrality metrics, power-law estimation, community detection, and complex network dynamics. Among other results, the authors validate the existence of power-law relationships on project sizes (number of developers). As a methodological contribution, the article demonstrates how network data science can be used to derive a broad spectrum of insights about employee effort and collaboration in organizations. The authors discuss implications for managers and future research directions.

Core Self-Evaluations as a Mediator Between Functional Disability and Life Satisfaction in College Students With Disabilities Majoring in Science and Technology

2015 ◽  
Vol 29 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Susan Miller Smedema ◽  
Joseph S. Pfaller ◽  
Rana A. Yaghmaian ◽  
Hayley Weaver ◽  
Elizabeth da Silva Cardoso ◽  
...  
Keyword(s):  
College Students ◽  
Life Satisfaction ◽  
Students With Disabilities ◽  
Functional Disability ◽  
Future Research ◽  
College Students With Disabilities ◽  
And Mathematics ◽  
Quantitative Descriptive ◽  
Core Self ◽  
The Impact

Purpose: To examine the mediational effect of core self-evaluations (CSE) on the relationship between functional disability and life satisfaction.Methods: A quantitative descriptive design using multiple regression analysis. The participants were 97 college students with disabilities receiving services through Hunter College’s Minority-Disability Alliance (MIND Alliance) in science, technology, engineering, and mathematics.Results: CSE was a partial mediator between functional disability and life satisfaction. After controlling for CSE, functional disability was no longer a significant predictor of life satisfaction.Conclusions: CSE partially mediated the impact of functional disability on life satisfaction. Future research should explore the development of interventions to increase CSE to reduce the effect of disability and to improve life satisfaction and employment outcomes for individuals with disabilities.

scholarly journals The Impact of Perceived Subgroup Formation on Transactive Memory Systems and Performance in Distributed Teams

2016 ◽  
Vol 12 (1) ◽  
pp. 44-66 ◽  
Author(s):  
Yide Shen ◽  
Michael J. Gallivan ◽  
Xinlin Tang
Keyword(s):  
Team Performance ◽  
Memory Systems ◽  
Transactive Memory ◽  
Distributed Teams ◽  
Future Research ◽  
Negative Effect ◽  
Group Members ◽  
And Performance ◽  
Using Data ◽  
The Impact

With distributed teams becoming increasingly common in organizations, improving their performance is a critical challenge for both practitioners and researchers. This research examines how group members' perception of subgroup formation affects team performance in fully distributed teams. The authors propose that individual members' perception about the presence of subgroups within the team has a negative effect on team performance, which manifests itself through decreases in a team's transactive memory system (TMS). Using data from 154 members of 41 fully distributed teams (where no group members were colocated), the authors found that members' perceptions of the existence of subgroups impair the team's TMS and its overall performance. They found these effects to be statistically significant. In addition, decreases in a group's TMS partially mediate the effect of perceived subgroup formation on team performance. The authors discuss the implications of their findings for managerial action, as well as for researchers, and they propose directions for future research.

scholarly journals A Systematic Review of Recommendations of Long-Term Strategies for Researchers Using Data Science Techniques

Computers ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 35
Author(s):  
Gilberto Ayala-Bastidas ◽  
Hector G. Ceballos ◽  
Francisco J. Cantu-Ortiz
Keyword(s):  
Systematic Review ◽  
Data Science ◽  
Process Mining ◽  
Research Strategies ◽  
Scientific Content ◽  
Open Research ◽  
Using Data ◽  
Long Term Impact ◽  
The Impact

The impact of the strategies that researchers follow to publish or produce scientific content can have a long-term impact. Identifying which strategies are most influential in the future has been attracting increasing attention in the literature. In this study, we present a systematic review of recommendations of long-term strategies in research analytics and their implementation methodologies. The objective is to present an overview from 2002 to 2018 on the development of this topic, including trends, and addressed contexts. The central objective is to identify data-oriented approaches to learn long-term research strategies, especially in process mining. We followed a protocol for systematic reviews for the engineering area in a structured and respectful manner. The results show the need for studies that generate more specific recommendations based on data mining. This outcome leaves open research opportunities from two particular perspectives—applying methodologies involving process mining for the context of research analytics and the feasibility study on long-term strategies using data science techniques.

Expertise in STEM Disciplines

2019 ◽  
pp. 528-550
Author(s):  
David F. Feldon ◽  
Soojeong Jeong ◽  
Joana Franco
Keyword(s):  
Modern Society ◽  
Future Research ◽  
Expert Performance ◽  
Science And Engineering ◽  
Training And Education ◽  
Science Technology ◽  
Stem Disciplines ◽  
New Directions ◽  
And Mathematics ◽  
The Impact

Enhancing expertise in science, technology, engineering, and mathematics (STEM) is vital to promoting both the intellectual and economic development of a modern society. This chapter synthesizes relevant studies on the acquisition and development of STEM expertise from different areas of research, including cognitive psychology, the psychology of science, sociology and anthropology, and educational research. Specifically, first, the structure of relevant STEM disciplines in conceptualizing the domain of expertise are discussed. Then the fundamental mechanisms of thinking and problem-solving practices in science and engineering that underlie expert performance within these disciplines are presented. Issues pertaining to assessment and recognition of expertise in STEM fields are also examined. Lastly, evidence pertaining to the impact of training and education on the development of STEM expertise is reviewed. The chapter closes with a critical analysis of STEM expertise research to date and identifies unanswered critical questions and new directions for future research.

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