scholarly journals Interactive computational modelling to improve teaching of physics and mathematics in marine geophysics

2020 ◽  
Author(s):  
Rui Gomes Neves ◽  
Maria C. Neves

With the tremendous growth in the areas of computing, statistics, and mathematics has led to the rise of the emerging field of expertise, named ‘Data Science’. This paper focuses on the comparative study and evaluation of the data science libraries used in Python Programming Languages, named ‘Matplotlib’ and ‘Seaborn’. The sole purpose of this paper is to identify areas and evaluate the strengths and weaknesses of these libraries with the implementation of code and identify the classification of the univariate and multivariate plotting of data concerned with patterns of data visualization and computational modelling of data in the form of processed information using techniques of big data and data mining


JAMA ◽  
1965 ◽  
Vol 194 (3) ◽  
pp. 269-272
Author(s):  
J. T. Apter
Keyword(s):  

2016 ◽  
Vol 75 (3) ◽  
pp. 123-132 ◽  
Author(s):  
Marie Crouzevialle ◽  
Fabrizio Butera

Abstract. Performance-approach goals (i.e., the desire to outperform others) have been found to be positive predictors of test performance, but research has also revealed that they predict surface learning strategies. The present research investigates whether the high academic performance of students who strongly adopt performance-approach goals stems from test anticipation and preparation, which most educational settings render possible since examinations are often scheduled in advance. We set up a longitudinal design for an experiment conducted in high-school classrooms within the context of two science, technology, engineering, and mathematics (STEM) disciplines, namely, physics and chemistry. First, we measured performance-approach goals. Then we asked students to take a test that had either been announced a week in advance (enabling strategic preparation) or not. The expected interaction between performance-approach goal endorsement and test anticipation was moderated by the students’ initial level: The interaction appeared only among low achievers for whom the pursuit of performance-approach goals predicted greater performance – but only when the test had been scheduled. Conversely, high achievers appeared to have adopted a regular and steady process of course content learning whatever their normative goal endorsement. This suggests that normative strivings differentially influence the study strategies of low and high achievers.


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