scholarly journals Use of reorderable matrices and heatmaps to support data analysis of students transcripts

2019 ◽  
Author(s):  
Rafael Tavares Carvalho Barros ◽  
Thiago Gonçalves Mendes ◽  
Celmar Guimarães Da Silva
Keyword(s):  
Data Analysis ◽  
Visual Representations ◽  
Numerical Approach ◽  
Specific Subject ◽  
On Demand ◽  
Color Scale ◽  
Student Grades ◽  
Order Algorithm

For a course coordinator, the analysis of several students’ transcripts to identify the situation of subjects or students is often an old-fashioned process executed through a textual and numerical approach. This work is part of a larger project aimed at choosing appropriate visual representations to help course coordinators to analyze sets of students transcripts. In this work, we developed a system that allows the visualization of student transcripts through a heatmap of student grades per subject. The heatmap represent grades based on a user-defined color scale. To assist in the analysis, it is possible to reorder subjects and students using the optimal leaf order algorithm, or even to reorder according to the grades of a specific subject or student. In addition, some features have been developed to meet visual guidelines, such as overview, zoom, filter and details-on-demand.

Are You Hooked on Paid Music Streaming?

2018 ◽  
Vol 14 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Charlie C. Chen ◽  
Steven Leon ◽  
Makoto Nakayama
Keyword(s):  
Data Analysis ◽  
Music Industry ◽  
Clear Understanding ◽  
On Demand ◽  
Behavioral Dynamics ◽  
Revenue Sources ◽  
Streaming Services ◽  
Facilitating Conditions ◽  
Communication Control ◽  
Music Streaming

The proliferation of free on-demand music streaming services (e.g., Spotify) is offsetting the traditional revenue sources (e.g., purchases of downloads or CDs) of the music industry. In order to increase revenue and sustain business, the music industry is directing its efforts toward increasing paid subscriptions by converting free listeners into paying subscribers. However, most companies are struggling with these attempts because they lack a clear understanding of the psychological and social purchase motivations of consumers. This study compares and contrasts the two different phases of Millennial generation consumer behaviors: the alluring phase and the hooking phase. A survey was conducted with 73 paying users and 163 non-paying users of on-demand music streaming services. The authors' data analysis shows two separate behavioral dynamics seen between these groups of users. While social influence and attitude are primary drivers for the non-paying users in the alluring phase, facilitating conditions and communication control capacity play critical roles for the paying users in the hooking phase. These results imply that the music industry should apply different approaches to prospective and current customers of music streaming services.

scholarly journals ANALISIS REPRESENTASI MATEMATIS SISWA DALAM MENYELESAIKAN MASALAH MATEMATIKA BERDASARKAN TAHAPAN KRULIK DAN RUDNICK DITINJAU DARI ADVERSITY QUOTIENT

KadikmA ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
SETYA DEWI
Keyword(s):  
Data Analysis ◽  
Visual Representations ◽  
Mathematical Expression ◽  
Linear Program ◽  
Mathematical Representation ◽  
Verbal Representation ◽  
Mathematics Test ◽  
Mathematical Expressions ◽  
Collecting Methods ◽  
Verbal Representations

This research purpose to describe the mathematical representation abilities of students based on the Krulik and Rudnick stages in terms of students' AQ. This research type is qualitative descriptive research. The research location is in  SMAN Darussholah Singojuruh with the subjects of class XI MIPA 3 and XI MIPA 7 as many as 30 students. The data collecting methods used test and interview methods, with research instruments, such as Adversity Response Profile (ARP) questionnaire, mathematics test questions on linear program material, interview guidelines, and validation sheets. The subjects selected as interview respondents were two students from each level of AQ who had significant differences in ARP scores (scores between AQ Climber, Camper, and Quiter), as well as based on the timeliness and accuracy of students' answers. Based on the test result data analysis and interview data analysis, the following results were obtained, such as 1) Climber students could solve linear program questions based on Krulik and Rudnick stages coherently, and be able to used forms of verbal representations, visual representations, and mathematical expressions properly and correctly; 2) Camper students could solve linear program questions based on Krulik and Rudnick stages coherently but had not been able to use forms of verbal representations, visual representations, and mathematical expressions properly and correctly; 3) Quitter students had not been able to solve linear program questions based on Krulik and Rudnick stages correctly and had not been able to use their verbal representation, visual representation, and mathematical expression skills properly.  

scholarly journals ADEGA: Análise de Dados Estatísticos da Grade Acadêmica

2021 ◽  
Vol 5 (3) ◽  
pp. 55-64
Author(s):  
Ana Paula Andrade Sodré ◽  
Bruno Henrique Meyer ◽  
Bruno Müller Junior ◽  
Henrique Margotte ◽  
Matheus Paolini De Miranda ◽  
...  
Keyword(s):  
Higher Education ◽  
Data Analysis ◽  
Statistical Data ◽  
Relevant Information ◽  
Management Systems ◽  
Educational Institutions ◽  
Statistical Data Analysis ◽  
History Of ◽  
Student Grades

Este artigo descreve o sistema ADEGA (Análise de Dados Estatísticos da Grade Acadêmica). Os sistemas de gestão acadêmica, aplicativos utilizados por instituições de ensino para fazer o acompanhamento acadêmico de alunos, normalmente não fornecem maiores funcionalidades do que um repositório de notas e frequências de alunos. Desse modo, o ADEGA tem como objetivo auxiliar a gestão de coordenações de curso de instituições de ensino superior, utilizando o histórico de notas e frequências para fornecer dados e gráficos com informações relevantes e que não costumam ser abordadas em outros sistemas, como uma lista de alunos em risco de jubilamento, um quadro comparativo entre reprovações por disciplina, entre outras. AGEGA (Academic Grid Statistical Data Analysis)This paper describes the ADEGA system (Academic Grid Statistical Data Analysis). The academics management systems, applications used by educational institutions to monitor students academically, usually don’t provide greater functionality than a repository of student grades and attendance. Therefore, ADEGA aims to assist in the management of higher education institution’s course coordination, it uses the history of grades and attendance to provide data and graphics with relevant information that don’t usually are included in other systems, like a risk of academic retirement’s list, a comparative chart of failures by subjects, among others.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PERMINTAAN SALAK PONDOH DI PASAR BUAH KOTA LHOKSEUMAWE

2019 ◽  
Vol 2 (2) ◽  
pp. 93
Author(s):  
Ita Puspita Sari ◽  
Cut Putri Mellita Sari
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Income Level ◽  
Primary Data ◽  
Analysis Method ◽  
Income Levels ◽  
On Demand ◽  
Negative Effect ◽  
Positive Effect

The purpose of this study was to know the effect of Salak Pondok Prices, Medan Salak Prices and Income Levels on the Demand for Salak Pondok in the Fruit Market of Lhokseumawe City. This study used primary data sourced from 100 respondents. The data analysis method used in this study was multiple linear regression with the help of Eviews 10. The results of the study showed that Salak Pondok prices had a positive effect on demand, while the Salak Medan prices had a negative effect on demand, and the income levels had an effect positively on demand, but simultaneously, Salak Pondok prices and Salak Medan prices, and income level had a positive effect on the demand for Salak Pondok in the fruit market of Lhokseumawe City and the magnitude of the effect of Salak Pondok prices, Salak Medan Prices and income levels on demand (R2) was 0.5340 (53.40%).

GPS: Working Backward with Data

2020 ◽  
Vol 113 (3) ◽  
pp. 257-259
Author(s):  
Matt Enlow ◽  
S. Asli Özgün-Koca
Keyword(s):  
Data Analysis ◽  
Categorical Data ◽  
Visual Representations ◽  
Central Tendency ◽  
Problem Solvers ◽  
Working Backwards

This month's Growing Problem Solvers focuses on Data Analysis across all grades beginning with visual representations of categorical data and moving to measures of central tendency using a “working backwards” approach.

scholarly journals Dynamic and on demand data streams

2019 ◽  
Vol 214 ◽  
pp. 04030
Author(s):  
Matteo Duranti ◽  
Valerio Formato ◽  
Valerio Vagelli
Keyword(s):  
Data Analysis ◽  
Data Streams ◽  
Original Data ◽  
End Users ◽  
Experiment Data ◽  
On Demand ◽  
Analysis Strategy ◽  
Computing Centers ◽  
Definition Of ◽  
High Level

Replicability and efficiency of data processing on the same data samples are a major challenge for the analysis of data produced by HEP experiments. High level data analyzed by end-users are typically produced as a subset of the whole experiment data sample to study interesting selection of data (streams). For standard applications, streams may be eventually copied from servers and analyzed on local computing centers or user machine clients. The creation of streams as copy of a subset of the original data results in redundant information stored in filesystems and may be not efficient: if the definition of streams changes, it may force a reprocessing of the low-level files with consequent impact on the data analysis efficiency. We propose an approach based on a database of lookup tables intended for dynamic and on-demand definition of data streams. This enables the end-users, as the data analysis strategy evolves, to explore different definitions of streams with minimal cost in computing resources. We also present a prototype demonstration application of this database for the analysis of the AMS-02 experiment data.

scholarly journals On-Demand Big Data Analysis in Digital Repositories: A Lightweight Approach

2015 ◽  
pp. 274-277 ◽  
Author(s):  
Zhiwu Xie ◽  
Yinlin Chen ◽  
Tingting Jiang ◽  
Julie Speer ◽  
Tyler Walters ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Digital Repositories ◽  
On Demand
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