scholarly journals Evaluation of Potential Correlation of Piano Teaching Using Edge-Enabled Data and Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Sibing Sun
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Association Rules ◽  
Data Science ◽  
Specific Model ◽  
Teaching Evaluation ◽  
T Test ◽  
Test Method ◽  
Piano Teaching ◽  
Measure Of Association

Data science has expanded at an exponential growth with the advancement of big data technology. The data analysis techniques need to delve deeper to find valuable information (Sarac 2017). The notion of edge computing is broadly acknowledged. Edge-enabled solutions provide computing, analysis, storage, and control nearer to the edge of the network, which support the efficient processing and decision-making. Machine learning has also attained significant attention in this context due to its flexibility and its ability to provide a variety of supervised, unsupervised, and semisupervised techniques. This research presents a specific model to evaluate the potential correlation of piano teaching using machine learning. The data analysis is performed at the edges of network for efficient results (Tan et al. 2017). The association rule mining technique of machine learning is utilized with the integration of improved T-test method. The improved T-test is performed for the measurement of association rules and proposed a new measure and influence degree of association rules. It is evident from the results that the use of the degree of influence as a measure of association rules to find the potential relevance of multimedia-assistant piano teaching evaluation data is extremely feasible. It overcomes shortcomings of existing measurement standards and reduces the generation of redundant rules. The existing literature highlights the concepts of evaluation of potential correlation and evaluates the advantages. However, there is a lack of an effective strategy for piano teaching. The proposed model performs efficient calculation and storage. The feasibility and effectiveness of the proposed framework are verified using the analysis of the actual dataset. The verification results show that it is feasible and valuable to find the potential relevance of multimedia-assisted piano teaching evaluation.

scholarly journals The democratization of data science education

2017 ◽  
Author(s):  
Sean Kross ◽  
Roger D Peng ◽  
Brian S Caffo ◽  
Ira Gooding ◽  
Jeffrey T Leek
Keyword(s):  
Machine Learning ◽  
Science Education ◽  
Data Analysis ◽  
Data Science ◽  
Online Data ◽  
The Past ◽  
The Us ◽  
Science Curricula ◽  
The Impact ◽  
And Training

Over the last three decades data has become ubiquitous and cheap. This transition has accelerated over the last five years and training in statistics, machine learning, and data analysis have struggled to keep up. In April 2014 we launched a program of nine courses, the Johns Hopkins Data Science Specialization, which has now had more than 4 million enrollments over the past three years. Here the program is described and compared to both standard and more recently developed data science curricula. We show that novel pedagogical and administrative decisions introduced in our program are now standard in online data science programs. The impact of the Data Science Specialization on data science education in the US is also discussed. Finally we conclude with some thoughts about the future of data science education in a data democratized world.

Biased Data

2020 ◽  
pp. 106-122
Author(s):  
Abigail Christina Fernandez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Analysis Data ◽  
Relevant Field

Data is just data if it is not put to proper comprehensive usage. Information is Knowledge and Knowledge gets upgraded to wisdom pertaining to insight in the relevant field of analysis. Data Science has become the key that unravels many pitches of interest in diversified fields of quest. It is of optimal stipulation that the solutions that the Artificial Intelligence Algorithms provide should do justice to the intent for which what it was built. But at times, inadvertently the word bias is declaimed, which has become an implicit or explicit inclusion in the Algorithms and the data collection methodologies incorporated. IT companies manoeuvring this technology need to treat this hushed underplay in prediction and decision making with top-notch priority to epitomise this imminent episode of Machine Learning in Data Analysis.

Data Analysis of COVID-19 THE GREAT EPIDEMIC OF THE YEAR 2020 (Preprint)

2020 ◽  
Author(s):  
Sonia Singla
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Temperature ◽  
Data Science ◽  
Cold Climate ◽  
High Humidity ◽  
Virus Spread ◽  
The World ◽  
Almost All ◽  
Death Cases

UNSTRUCTURED What is COVID-19, and how to deal with COVID-19 during pandemic. Does it is the result of harming nature? Mostly the disease was spread from Wuhan, China and is found bat to be the cause of spreading to other animals which has infected human. Italy, Spain and almost all the world is facing the consequence of COVID-19. In India where the prevalence rate of disease is 1.9% and 2.5% incidence rate, most of the cases are being found in Kerala and Maharashtra with updated data more than 200. Now the question arises is wet and cold climate making the virus spread and can the virus be killed in high temperature and high humidity? After Italy, China more death cases have been found in Spain. Data Science plays a major role by using machine learning to solve various problems and can be used to solve various problems associated with COVID-19. For example, prediction of how many peoples in groups will get effected, which location or area is likely to be get effected, screening of patients, using chat boots for concluding how many peoples have got the symptoms and are likely to be effected and so on. In this paper we have tried to show which areas are more likely to be affected, how lockdown have helped in reduction of death and what in future we can do to not face such situation again in life.

scholarly journals Implementing AutoML in Educational Data Mining for Prediction Tasks

2019 ◽  
Vol 10 (1) ◽  
pp. 90 ◽  
Author(s):  
Maria Tsiakmaki ◽  
Georgios Kostopoulos ◽  
Sotiris Kotsiantis ◽  
Omiros Ragos
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Outcomes ◽  
Learning Strategies ◽  
Data Science ◽  
Learning Algorithms ◽  
Educational Data Mining ◽  
Search Space ◽  
Specific Model ◽  
Machine Learning Algorithms

Educational Data Mining (EDM) has emerged over the last two decades, concerning with the development and implementation of data mining methods in order to facilitate the analysis of vast amounts of data originating from a wide variety of educational contexts. Predicting students’ progression and learning outcomes, such as dropout, performance and course grades, is regarded among the most important tasks of the EDM field. Therefore, applying appropriate machine learning algorithms for building accurate predictive models is of outmost importance for both educators and data scientists. Considering the high-dimensional input space and the complexity of machine learning algorithms, the process of building accurate and robust learning models requires advanced data science skills, while is time-consuming and error-prone in most cases. In addition, choosing the proper method for a given problem formulation and configuring the optimal parameters’ values for a specific model is a demanding task, whilst it is often very difficult to understand and explain the produced results. In this context, the main purpose of the present study is to examine the potential use of advanced machine learning strategies on educational settings from the perspective of hyperparameter optimization. More specifically, we investigate the effectiveness of automated Machine Learning (autoML) for the task of predicting students’ learning outcomes based on their participation in online learning platforms. At the same time, we limit the search space to tree-based and rule-based models in order to achieving transparent and interpretable results. To this end, a plethora of experiments were carried out, revealing that autoML tools achieve consistently superior results. Hopefully our work will help nonexpert users (e.g., educators and instructors) in the field of EDM to conduct experiments with appropriate automated parameter configurations, thus achieving highly accurate and comprehensible results.

scholarly journals Pengaruh Latihan Bench Dip Terhadap Kemampuan Passing Bola Basket Pada SMAN 1 Narmada Tahun 2020

2021 ◽  
Vol 8 (1) ◽  
pp. 57
Author(s):  
Andi Irawan ◽  
Dadang Warta Chandra Wira Kusuma ◽  
Nurtajudin Nurtajudin
Keyword(s):  
Data Analysis ◽  
Null Hypothesis ◽  
Sampling Technique ◽  
T Test ◽  
Test Method ◽  
Male Students ◽  
Significance Level ◽  
Basketball Game ◽  
The Given ◽  
Cluster Random Sampling

The problem with this research is that the students' low shooting results are caused by their passing and shooting abilities that are still not optimal. This can be seen when students throw and catch, passing the ball often does not reach the given friend, deep passing is the key to the basketball game. The purpose of this study is to examine the significance of different samples with the formulation of the problem posed is to find out whether there is an effect of bench dip training on basketball passing abilities at SMAN 1 Narmada in 2020". The research method for taking subjects in this study is cluster random sampling, which is a sampling technique that selects an area or group as the sample. The samples used were male students who took part in basketball extracurricular with a total of 20 people. To obtain data in this study, the test method and the method of documentation were used. The data analysis method used is the t-test (t-test). The results of the study based on the results of the t-test (t-test) showed the calculated value of the t-test was 26.29, so the significance level was 5% and N was 20, it turned out that the number of rejection of the null hypothesis stated in the table was 2.069. This fact indicates that the value of t arithmetic from the results of data analysis of 26.29 is above the number of rejection of the null hypothesis which is 2.069. (T value = 26.29 > r table 2.069) it can be concluded that "There is an effect of bench dip training on basketball passing skills at SMAN 1 Narmada in 2020.

scholarly journals The democratization of data science education

2017 ◽  
Author(s):  
Sean Kross ◽  
Roger D Peng ◽  
Brian S Caffo ◽  
Ira Gooding ◽  
Jeffrey T Leek
Keyword(s):  
Machine Learning ◽  
Science Education ◽  
Data Analysis ◽  
Data Science ◽  
Online Data ◽  
The Past ◽  
The Us ◽  
Science Curricula ◽  
The Impact ◽  
And Training

Over the last three decades data has become ubiquitous and cheap. This transition has accelerated over the last five years and training in statistics, machine learning, and data analysis have struggled to keep up. In April 2014 we launched a program of nine courses, the Johns Hopkins Data Science Specialization, which has now had more than 4 million enrollments over the past three years. Here the program is described and compared to both standard and more recently developed data science curricula. We show that novel pedagogical and administrative decisions introduced in our program are now standard in online data science programs. The impact of the Data Science Specialization on data science education in the US is also discussed. Finally we conclude with some thoughts about the future of data science education in a data democratized world.

scholarly journals Data Science Methods for Psychology

Psychology ◽  
2020 ◽  
Author(s):  
Jeffrey Stanton
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Large Data ◽  
Large Data Sets ◽  
Predictive Analysis ◽  
Data Sets ◽  
The Impact

The term “data science” refers to an emerging field of research and practice that focuses on obtaining, processing, visualizing, analyzing, preserving, and re-using large collections of information. A related term, “big data,” has been used to refer to one of the important challenges faced by data scientists in many applied environments: the need to analyze large data sources, in certain cases using high-speed, real-time data analysis techniques. Data science encompasses much more than big data, however, as a result of many advancements in cognate fields such as computer science and statistics. Data science has also benefited from the widespread availability of inexpensive computing hardware—a development that has enabled “cloud-based” services for the storage and analysis of large data sets. The techniques and tools of data science have broad applicability in the sciences. Within the field of psychology, data science offers new opportunities for data collection and data analysis that have begun to streamline and augment efforts to investigate the brain and behavior. The tools of data science also enable new areas of research, such as computational neuroscience. As an example of the impact of data science, psychologists frequently use predictive analysis as an investigative tool to probe the relationships between a set of independent variables and one or more dependent variables. While predictive analysis has traditionally been accomplished with techniques such as multiple regression, recent developments in the area of machine learning have put new predictive tools in the hands of psychologists. These machine learning tools relax distributional assumptions and facilitate exploration of non-linear relationships among variables. These tools also enable the analysis of large data sets by opening options for parallel processing. In this article, a range of relevant areas from data science is reviewed for applicability to key research problems in psychology including large-scale data collection, exploratory data analysis, confirmatory data analysis, and visualization. This bibliography covers data mining, machine learning, deep learning, natural language processing, Bayesian data analysis, visualization, crowdsourcing, web scraping, open source software, application programming interfaces, and research resources such as journals and textbooks.

scholarly journals Application of Machine Learning in medical data analysis illustrated with an example of association rules

2021 ◽  
Vol 192 ◽  
pp. 3134-3143
Author(s):  
Beata Butryn ◽  
Iwona Chomiak-Orsa ◽  
Krzysztof Hauke ◽  
Maciej Pondel ◽  
Agnieszka Siennicka
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Association Rules ◽  
Medical Data

scholarly journals PENGARUH METODE KOOPERATIF JIGSAW TERHADAP PRESTASI BELAJAR MATA PELAJARAN IPS PADA SISWA KELAS III

2016 ◽  
Vol 4 (02) ◽  
Author(s):  
Maya Kartika Sari
Keyword(s):  
Student Achievement ◽  
Data Analysis ◽  
Cooperative Learning ◽  
Social Studies ◽  
T Test ◽  
Test Method ◽  
Control Group ◽  
Class Iii ◽  
Significance Level ◽  
School Year

<p>This study aims to determine the effect of the use of cooperative learning of jigsaw method in social studies on student achievement of class III SD Pakualaman Bantul and SD Gandok Islamiyah in 2014/2015 school year. This research design uses quantitative research methods. Collecting data in this study uses the test method. The used test methods in this research are the pre-test and post-test given to the experimental group and the control group. While the data analysis is a statistical method t test (t-test).</p><p> The results of data analysis t test (t-test) obtained value = 3.34. At the significance level (α) = 0.05 and with db = 38 obtained value = 1.6859. So that is 3.34 ≥ 1.6859, therefore Ho is rejected H<sub>1</sub> accepted. The conclusion of this study is that there is an effect on the use of cooperative learning of jigsaw model in social studies on student achievement of class III SD Islamiyah Pakualaman at school year 2014/2015.</p><p> </p><p><strong>Keyword</strong>s: Cooperative Model Jigsaw Type, Learning Achievement</p>

scholarly journals Pengaruh Penerapan Model Pembelajaran Berbasis Masalah Terhadap Kemampuan Generik Sains Siswa Pada Materi Larutan Penyangga

2020 ◽  
Vol 2 (2) ◽  
pp. 81
Author(s):  
Ratu Evina Dibyantini ◽  
Widya Azaria
Keyword(s):  
Data Analysis ◽  
Problem Based Learning ◽  
Direct Interaction ◽  
T Test ◽  
Test Method ◽  
Science Students ◽  
Learning Models ◽  
Choice Data ◽  
Buffer Solution ◽  
Test Technique

This study was conducted to determine the effect of the forward model of Problem Based Learning on the generic ability of science students subject to buffer solutions. This research was conducted in class XI MIA Negeri 3 Medan in the academic year 2018/2019. The sample in this study consisted of two classes taken by random sampling, namely the experimental class with Problem Based Learning models and the control class with Direct Interaction models. Data collection techniques with the test method, in the form of questions about the subject of a buffer solution to measure the results of learning chemistry and the generic ability of science students (multiple choice). Data analysis using t test with independent sample t-test technique using SPSS version 20. Based on data analysis, the average value of student learning outcomes using problem based learning models increased from 34.02 to 80 compared to DI models from 31.94 to 74.58. This is supported by the results of hypothesis testing, obtained data that sig (2-tailed) <α or 0.003 <0.05, so that Ha is accepted and Ho is rejected. The data shows that the use of problem based learning models can affect the generic abilities of students' science.Keywords:Generic science skills, Problem based learning

Sign in / Sign up

Export Citation Format

Share Document