scholarly journals Piloting Data Science Learning Platforms through the Development of Cloud-based interactive Digital Computational Notebooks

2021 ◽  
Author(s):  
Rajesh Kumar Gnanasekaran ◽  
Richard Marciano
Keyword(s):  
Science Learning ◽  
Data Science ◽  
Learning Platforms

Data science, learning, and applications to biomedical and health sciences

2017 ◽  
Vol 1387 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Nabil R. Adam ◽  
Robert Wieder ◽  
Debopriya Ghosh
Keyword(s):  
Science Learning ◽  
Data Science ◽  
Health Sciences

scholarly journals Think big: learning contexts, algorithms and data science

2016 ◽  
Vol 8 (2) ◽  
pp. 69-83 ◽  
Author(s):  
Michele Baldassarre
Keyword(s):  
Big Data ◽  
Theoretical Basis ◽  
Data Science ◽  
Scientific Investigation ◽  
Focus Attention ◽  
Learning Contexts ◽  
Quality Of Teaching ◽  
Learning Platforms ◽  
Definition Of

Abstract Due to the increasing growth in available data in recent years, all areas of research and the managements of institutions and organisations, specifically schools and universities, feel the need to give meaning to this availability of data. This article, after a brief reference to the definition of big data, intends to focus attention and reflection on their type to proceed to an extension of their characterisation. One of the hubs to make feasible the use of Big Data in operational contexts is to give a theoretical basis to which to refer. The Data, Information, Knowledge and Wisdom (DIKW) model correlates these four aspects, concluding in Data Science, which in many ways could revolutionise the established pattern of scientific investigation. The Learning Analytics applications on online learning platforms can be tools for evaluating the quality of teaching. And that is where some problems arise. It becomes necessary to handle with care the available data. Finally, a criterion for deciding whether it makes sense to think of an analysis based on Big Data can be to think about the interpretability and relevance in relation to both institutional and personal processes.

scholarly journals Teaching Challenges in COVID-19 Scenery: Teams Platform-Based Student Satisfaction Approach

2020 ◽  
Vol 12 (18) ◽  
pp. 7514 ◽  
Author(s):  
Leticia Rodriguez-Segura ◽  
Marco Antonio Zamora-Antuñano ◽  
Juvenal Rodriguez-Resendiz ◽  
Wilfrido J. Paredes-García ◽  
José Antonio Altamirano-Corro ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Student Satisfaction ◽  
Data Science ◽  
Educational Process ◽  
Face To Face ◽  
Global Context ◽  
Educational Processes ◽  
Learning Platforms ◽  
Teaching Learning ◽  
The Relationship

At the onset of the crisis caused by COVID-19, the Mexican education system chose to join the global context and suspend face-to-face classes for all educational levels. For the continuity of educational processes, a transition from a traditional educational model (face-to-face) to emergency remote teaching (ERT) was made through virtual learning platforms and learning management system (LMS) schemes. Universidad del Valle de Mexico (UVM), in a collaboration agreement with Microsoft Co., chose to use Teams to continue its educational process. In this work, we intend to identify the factors that can be taken into account regarding the level of student satisfaction in the teaching–learning process in ERT using Teams, and validate the established educational strategy. Statistical analysis was carried out to analyze the academic environment for these scenario changes while considering knowledge assessment, and competencies achievement. A combined sampling method was applied with convenience and statistical analysis. The main results established significant percentages, where more than 60% of the students surveyed were manifested in the use of the teams and the organization of the class sessions by the teachers, and the activities developed. Using the Cronbach’s Alpha coefficient, the reliability of the data collection instruments was determined. The correlations of each of the survey questions were calculated to determine the relationship between themselves and the total answers, giving results similar to those obtained through data science tools. Taking advantage of the situation, data science tools were applied to compare the results with obtained values from RapidMiner software in the correlation of factors in of 0.440, 0.384, 0.246, 0.048 and 0.384.

PENERAPAN METODE PEMBERIAN TUGAS UNTUK MENINGKATKAN HASIL BELAJAR IPA SISWA KELAS IV SD NEGERI 004 TEMBILAHAN KECAMATAN TEMBILAHAN KABUPATEN INDRAGIRI HILIR

2017 ◽  
Vol 6 (1) ◽  
pp. 242
Author(s):  
Muhammad MH Muhammad MH
Keyword(s):  
Action Research ◽  
Student Learning ◽  
Learning Outcomes ◽  
Science Learning ◽  
Data Science ◽  
Fourth Graders ◽  
Student Learning Outcomes ◽  
Research Subjects ◽  
Fourth Grade Students ◽  
Improve Student Learning

The low yield of fourth grade students learn science SD Negeri 004 Tembilahan this is the background of this research, this is evidenced by the acquisition of the average student learning outcomes that is equal to 63.31. This study aims to improve student learning outcomes through the implementation of the method of administration tasks. This research is a classroom action research subjects fourth graders SD Negeri 004 Tembilahan by the number of 32 students, the study was conducted as much as two cycles consisting of two meetings. The data used in this study focuses on improving learning outcomes and student learning completeness. Based on the results obtained under the data science learning outcomes increased in each cycle, the pre-cycle (basic score) average learning results obtained by the students was 63.31 by the number of students who completed by 16 (50.00%), in cycle I rise to 70.31 by the number of students who pass the number 25 (78.12%), and the second cycle increased to 73.59 by the number of students who completed totaling 32 students. Based on these results, it can be concluded that the application of the method of administration tasks can improve student learning outcomes IPA IV SD Negeri 004 Tembilahan.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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