Enhancing Capacity on Data Analysis among Gender and Development Focal Persons Through Training

2020 ◽  
Vol 1 (1) ◽  
pp. 25-35
Author(s):  
Maria Yna Diane Manalastas ◽  
◽  
Maria Excelsis Orden ◽  
Ana Maria Lourdes Latonio ◽  
◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Analytics ◽  
Limiting Factors ◽  
Economic Research ◽  
Technical Knowledge ◽  
Gender And Development ◽  
Assessment Scores ◽  
Level Of Knowledge ◽  
Post Assessment ◽  
Focal Person

Gender and development (GAD)-related issues are important topics in nation building. GAD focal persons in government agencies are identified to mainstream implementation of GAD-related activities to include research and development. However, one of the limiting factors in gender-related research is the lack of technical knowledge on data analytics which is fundamental for decision-making. The Socio-Economics Research and Data Analytics Center in Luzon (SERDAC–Luzon) was established as a government’s response to this limitation. The Center aims to enhance the capacity of researchers in basic and advanced socio-economic research, especially on the use of statistical software for data analytics to a range of gender issues. Trainings were conducted among GAD focal persons to enhance their competency on the use of SPSS for data analytics. Lectures, discussions, and workshops using the software were the teaching modalities. Data on the GAD focal person participants in the trainings conducted at two universities were used. The pre- and post-assessment scores were the variables, and the differences of the scores indicated the effects of the training on their competency and level of knowledge. Differential response of the male and female participants was also analyzed. There was a difference in the level of competency and knowledge in data analysis and the use of the software by gender as indicated in the participants’ pre- and post-assessment scores. The training was effective in providing the participants knowledge and skills. The trainings contributed to the improvement of the participants’ competency on the use of the software and knowledge for data analytics.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

scholarly journals The swarm intelligence algorithms and their application for the educational data analysis

2019 ◽  
Vol 23 (5) ◽  
pp. 33-43
Author(s):  
Y. Yu. Dyulicheva
Keyword(s):  
Data Analysis ◽  
Swarm Intelligence ◽  
Learning Objects ◽  
Ant Algorithm ◽  
First Year ◽  
Optimal Learning ◽  
First Year Students ◽  
Learning Content ◽  
Learning Path ◽  
Level Of Knowledge

The purpose of the paper is the investigation of the modern approaches and prospects for the application of swarm intelligence algorithms for educational data analysis, as well as the possibility of using of ant algorithm modifications for organizing educational content in adaptive systems for conducting project seminars.Materials and methods. The review of the modern articles on the educational data analysis based on swarm intelligence algorithms is provided; the approaches to solving problem of the optimal learning path construction (optimal organization of the learning objects) based on the algorithm and its modifications taking into account the students’ performance in the process of the optimal learning path construction are investigated; the application of particle swarm optimization and its modification based on Roccio algorithm for the reduction of curse dimension in the problem of the auto classifying questions; the application of ant algorithm, bee colony algorithm and bat algorithm for recommender system construction are studied; the prediction of students’ performance based on particle swarm optimization is researched in the article. The modification of ant algorithm for optimal organization of learning objects at projects seminars is proposed.Results. The modern approaches based on swarm intelligence algorithms to problem solving in educational data analysis are investigated. The various approaches to pheromones updating (their evaporation) when building the optimal learning path based on students’ performance data and search of group with “similar" students are studied; the abilities of the hybrid swarm intelligence algorithms for recommendation construction are investigated.Based on the modification of ant algorithm, the approach to the learning content organization at project seminars with individual preferences and students’ level of basic knowledge is proposed. The python classes are developed: the class for statistical data processing; the classfor modifica -tion of ant algorithm, taking into account the current level of knowledge and interest of student in studying a specific topic at the project seminar; the class for optimal sequence of the project seminars ’ topics for students. The developed classes allow creating the adaptive system that helps first year students with a choice of topics of project seminars.Conclusion. According to the results of the study, we can conclude about the effectiveness of swarm intelligence algorithms usage to solve a wide range of tasks connected with learning content and students’ data analysis in the e-learning systems and perspectives to hybrid approaches development based on swarm intelligence algorithms for realizing the adaptive learning systems on the paradigm of “demand learning".The results can be used to automate the organization of learning content during project seminars for the first-year students, when it is important to understand the basic level of knowledge and students’ interest in learning new technologies.

scholarly journals Knowledge retention in capstone experiences: An analysis of online and face-to-face courses

2016 ◽  
pp. 528-539 ◽  
Keyword(s):  
Deep Learning ◽  
Knowledge Retention ◽  
Online Environment ◽  
Comprehensive University ◽  
Face To Face ◽  
Assessment Scores ◽  
Class Time ◽  
Significant Difference ◽  
Level Of Knowledge ◽  
One Way Anova

This research chronicles the development of a capstone experience by a regional comprehensive university. The process began with a multi-year project during which the faculty annually reviewed the results with a view to determining if the class provided the deep learning culminating experiences anticipated. A major measure of success was the desire to replicate the deep learning common in face-to-face classes in the online environment. The results of 166 students were analyzed, 82 online and 84 face-to-face, to determine if a difference existed. A one-way ANOVA tested the score differences among 10 sections and determined the students’ scores did not differ significantly. Finally, a two-sample t-test between proportions determined that there was not a significant difference between the online and face-to-face students with respect to the level of assessment scores earned. Given that online and face-to-face students demonstrate the same level of knowledge, does this beg the question, what value does face-to-face class time offer?

An examination of the importance of big data analytics in supply chain agility development

2018 ◽  
Vol 41 (10) ◽  
pp. 1201-1219 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Technology Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Technical Knowledge ◽  
Content Type ◽  
Relational Knowledge ◽  
Management Knowledge ◽  
Business Knowledge

Purpose This paper aims to investigate the influence of big data analytics (BDA) personnel expertise capabilities in the development of supply chain (SC) agility. Based on extant literature, the study explores the role of BDA technical knowledge, BDA technology management knowledge, BDA business knowledge and BDA relational knowledge in SC agility development. Furthermore, the author also explores the inter-relationships among these four BDA personnel expertise capabilities. Design/methodology/approach An expert team consisting of IT practitioners (with a minimum experience of five years) were chosen to comment and modify the established scale items of the constructs used in the study. Subsequently, the measures were further pre-tested with 61 students specializing in computer science and information technology. The final survey was mailed to 651 IT professionals with a minimum experience of five years or more in an allied field. Repeated follow-ups and reminders resulted in 176 completed responses. The responses were analysed using partial least squares in SmartPLS 2.0.M3. Findings Findings suggested that BDA technology management knowledge, BDA business knowledge and BDA relational knowledge are prominent enablers of SC agility. Furthermore, BDA technology management knowledge is an essential precursor of BDA technical knowledge and BDA business knowledge. Originality/value The study is the foremost in addressing the importance of BDA personnel expertise capabilities in the development of SC agility. Furthermore, it is also the foremost in exploring the inter-relationships among the BDA personnel expertise capabilities.

Learning Analytics and Technology Through Teaching

2021 ◽  
Author(s):  
Matthew Kaufman ◽  
Kristi Yuthas
Keyword(s):  
Learning Analytics ◽  
Data Analytics ◽  
New Technologies ◽  
Teaching Approach ◽  
Knowledge Gap ◽  
Technical Knowledge ◽  
Self Regulated Learning ◽  
Logical Sequence ◽  
Regulated Learning ◽  
Pedagogical Framework

Data analytics problems, methods and software are changing rapidly. Learning how to learn new technologies might be the most important skill for students to develop in an analytics course. We present a pedagogical framework that promotes self-regulated learning and metacognition and three student-driven assignments that can be used in accounting analytics and other courses that incorporate technology. The assignment can be used by faculty who do not have training in analytics. The assignments adopt a learn-through-teaching approach that helps students: 1) define a conceptual or technical knowledge gap; 2) identify resources available for filling that gap; 3) work independently to acquire the desired knowledge; 4) break knowledge into components and arrange in a logical sequence; and 5) reinforce knowledge by presenting to others in an accessible manner. These assignments equip students with confidence and capabilities that will enable them to keep up with advances in technology.

Data Analytics in Tax Research: Analyzing Worker Agreements and Compensation Data to Distinguish Between Independent Contractors and Employees Using IRS Factors

2020 ◽  
Vol 35 (3) ◽  
pp. 1-23
Author(s):  
A. Faye Borthick ◽  
Lucia N. Smeal
Keyword(s):  
Data Analysis ◽  
Data Analytics ◽  
Internal Revenue Service ◽  
Database Query ◽  
Employment Practices ◽  
Accounting Data ◽  
Independent Contractors ◽  
Client Data ◽  
Tax Research ◽  
Tax Practice

ABSTRACT This case prompts learners to analyze compensation data and worker agreements to assess a company's likely compliance with requirements for classifying workers as independent contractors rather than employees based on the factors the Internal Revenue Service (IRS) uses for compliance with IRS Rev. Rul. 87-41 and Treas. Reg. § 31.3401(c)-1. Students combine tax research and data analysis to identify risky employment practices, recommend corrective action to bring the company into compliance, and estimate potential penalties if the IRS were to declare the company not in compliance. Students complete a data analysis report as a basis for preparing a research memorandum. Students electing tax practice will need to be able to perform similar analyses of client data in advance of IRS audits given that the IRS analyzes accounting data when auditing taxpayers. Given the guidance in the Teaching Notes, no database query experience is necessary on the part of instructors.

Big Data Analytics in Action

2019 ◽  
pp. 266-294 ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Insight Into

Have you ever wondered how companies that adopt big data and analytics have generated value? Which algorithm are they using for which situation? And what was the result? These points will be discussed in this chapter in order to highlight the importance of big data analytics. To this end, and in order to give a quick introduction to what is being done in data analytics applications and to trigger the reader's interest, the author introduces some applications examples. This will allow you, in more detail, to gain more insight into the types and uses of algorithms for data analysis. So, enjoy the examples.

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

Sign in / Sign up

Export Citation Format

Share Document