Discrimination of the Contextual Features of Top Performers in Scientific Literacy Using a Machine Learning Approach

Chen, Jiangping; Zhang, Yang; Wei, Yueer; Hu, Jie

doi:10.1007/s11165-019-9835-y

Cited by 53 publications

(55 citation statements)

References 75 publications

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“…In contrast, Trump's speeches were peppered with criticisms of the current administration, and contained more negative vocabulary (e.g., bad, illegal, disaster, etc.). This finding, too, is supported by recent research findings that identified Trump' predisposition toward negative description of the reality (e.g., Chen, Zhang, Wei, & Hu, 2019;Liu et al, 2018;Savoy, 2018).…”

Section: Differences Between Clinton's and Trump's Linguistic Stylessupporting

confidence: 74%

A Corpus-Based Study of Hillary Clinton’s and Donald Trump’s Linguistic Styles

Chen¹,

Yan²,

Hu³

2019

IJEL

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Since the 2016 U.S. presidential election, research on Hillary Clinton's and Donald Trump's linguistic styles has witnessed an exponential increase, with a lopsided focus on Trump in particular. This study compared Clinton's and Trumps' campaign speeches during the general election using a corpus-based approach. Discourse analysis of the corpora was conducted using the textual analysis software AntConc 3.2.4. The results showed that Clinton used a more diverse vocabulary compared with Trump, and that both candidates stuck to their core campaign messages in their speeches. Three major differences between Clinton's and Trump's linguistic styles were identified: 1) Clinton was inclined towards rational discussions of public policy, while Trump was adept at appealing to voters' emotions; 2) Clinton was more positive and focused on her vision of the future, while Trump was more negative and fixated at depicting a dystopian reality; 3) Clinton aimed to find commonalities with the American people, while Trump aimed to highlight differences between himself and his opponents. By putting Clinton's rhetoric on a par with Trump's, this study highlighted their linguistic style differences as part of their grand campaign strategy, which could contribute to current understanding of the two candidates' rhetorical preferences, political beliefs and strategies in their 2016 campaigns.

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Section: Differences Between Clinton's and Trump's Linguistic Stylessupporting

confidence: 74%

A Corpus-Based Study of Hillary Clinton’s and Donald Trump’s Linguistic Styles

Chen¹,

Yan²,

Hu³

2019

IJEL

Self Cite

View full text Add to dashboard Cite

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“…Self‐efficacy (Sahin et al, 2017; Schunk et al, 2014) and epistemological beliefs (Chen, 2012; Kampa et al, 2016) have raised the same concerns. Both the former and the latter have been tied to science‐related outcomes (Chen et al, 2019; Grabau & Ma, 2017; Jansen et al, 2015; Mason et al, 2013; Trautwein & Ludtke, 2007), but also have been investigated for their mutual interdependency (Mason et al 2013; Pajares, 2012).…”

Section: Introductionmentioning

confidence: 99%

Are students in Italy really disinterested in science? A person‐centered approach using the PISA 2015 data

et al. 2021

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Seen as one of the essential domains for active citizenship, examining how students relate to science has become crucial. Based on a person‐centered approach, this article investigates self‐related dispositions and motivation in science using the 2015 Programme for International Student Assessment (PISA) data set. By employing a latent profile analysis, student profiles were investigated among 11,583 15‐year‐old‐students in Italy. Five distinct student groups were identified. The index of economic, social, and cultural status (ESCS), immigrant background, gender, study programs, and the accompanying learning environment were also inspected. Each group was observed against particular subdomains in science competence in line with the PISA framework and environmental awareness. The results indicate the profiles differed on the examined covariates and showed distinct patterns relative to achievement and environmental awareness. However, differences in achievement between the profiles could not be explained by students’ variability in immigrant background or ESCS across the examined groups.

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“…For instance, pedagogical impact factors of 4th-grade students with excellent reading proficiency were identified based on Progress in International Reading Literacy Study (PIRLS) (Xiao and Hu, 2019). Regarding the PISA-based analysis, a set of impact factors influencing top students’ science performance was explored (e.g., Chen et al, 2019). However, few of these studies have targeted ICT impact factors and 15-year-olds’ reading proficiency in high-performing countries.…”

Section: Introductionmentioning

confidence: 99%

Regression Analysis of ICT Impact Factors on Early Adolescents’ Reading Proficiency in Five High-Performing Countries

Xiao

Liu

2019

Front. Psychol.

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The popularity of information and communication technology (ICT) has had a significant influence on the reading proficiency of early adolescents. Achieving excellent reading proficiency, which is related not only to a student’s inherent talent but also to various impact factors, can greatly enhance the effectiveness of reading education. The Program for International Student Assessment (PISA) 2015 provides an international view on the reading proficiency of 15-year-olds in a computer-based testing environment. In this study, a multiple linear regression model was constructed using the computing language R to investigate the association between student-level ICT impact factors (the availability of ICT, the use of ICT and attitudes toward ICT) and reading proficiency among early adolescents. The sample included 37,155 15-year-olds from five representative countries with extremely high reading proficiency. The results showed that the students’ ICT-related attitudinal factors concerning their interest in ICT and perceived autonomy in using ICT, rather than ICT availability and ICT use, were closely associated with high reading proficiency. In addition, ICT devices should be integrated not only as instructional media but also as a cognitive tool for teaching reading with timely and appropriate scrutiny.

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Discrimination of the Contextual Features of Top Performers in Scientific Literacy Using a Machine Learning Approach

Cited by 53 publications

References 75 publications

A Corpus-Based Study of Hillary Clinton’s and Donald Trump’s Linguistic Styles

A Corpus-Based Study of Hillary Clinton’s and Donald Trump’s Linguistic Styles

Are students in Italy really disinterested in science? A person‐centered approach using the PISA 2015 data

Regression Analysis of ICT Impact Factors on Early Adolescents’ Reading Proficiency in Five High-Performing Countries

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