Developing Big Data Analytics Course for Non-ICT Major University Students

Lim, Joo Hyun; Moon, ChangHak

doi:10.18517/ijaseit.11.6.12539

Cited by 2 publications

(2 citation statements)

References 27 publications

(27 reference statements)

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“…Visualization using Discover, in Figure 2, shows the transaction pattern has increased from 2012 to 2015 and appears to have decreased from 2016 to 2018. This stage is in line with the previous research [34] because preprocessing the data into an intelligent format facilitates the practical analysis of the volume of data transactions.…”

Section: Determining Time Interval Using Discoversupporting

confidence: 67%

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Henderi,

Irawatia,

Indra

et al. 2023

HighTech. Innov. J.

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Big data collection involves enormous amounts of raw data. To boost the sustainability of corporate value and support business intelligence and decision-making systems, in-depth data analysis is necessary. The data storage, analysis, and visualization methods, as well as the discovery of patterns and linkages, all depend on extensive data analysis. This study aims to process datasets to learn things like how ratings impact market sales transactions and how much of an impact factor connected to consumers and items have on ratings. Elasticsearch and Kibana were used for the dataset processing. This study evaluated traits related to the test parameters using a variety of test procedures. The product is scored as a representation of the product types involved in the sales transaction, and the name is assessed as a reflection of the customer. Kibana and Elasticsearch, a full-text search engine, were used in this work to do extensive data analysis on data sets. It is a visualization tool that is employed in a controlled environment to evaluate how ratings impact market exchanges for electronic goods, and it offers suggestions. The study found a substantial relationship between electronic product sales on the Amazon marketplace from 2012 to 2018. It suggested the importance of buyer constituents as users and how different product categories relate to ratings in business transactions. Doi: 10.28991/HIJ-2023-04-03-09 Full Text: PDF

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Section: Determining Time Interval Using Discoversupporting

confidence: 67%

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Henderi,

Irawatia,

Indra

et al. 2023

HighTech. Innov. J.

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“…Understanding student statistics depends on statistical knowledge, including descriptive statistics and inference statistics [29]. Meanwhile, students' statistical thinking depends on their inductive thinking ability [21], [30]. Students' ability of statistical knowledge and inductive thinking affects their ability to model statistics from the real world and interpret them in the real world [5], [23].…”

Section: Resultsmentioning

confidence: 99%

Mental Action Way of Understanding (WoU) and Way of Thinking (WoT) Students in Statistics Learning in Higher Education

Lukman¹,

Wahyudin²,

Suryadi³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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This study aims to examine in depth the Mental Action Way of Understanding (WoU) and Way of Thinking (WoT) of students in learning Statistics in Higher Education and the relationship between WoU and WoT. This research is qualitative research using the Grounded Theory approach. The participants involved were 36 people in the city of Bandung, West Java, Indonesia. Validity and reliability using the Krippendorff content analysis method with the Inter-coder Agreement (ICA) test using Krippendorff alpha coefficients. The results showed that there was a significant relationship between students' mental action statistics and students' WoU and WoT in Statistical Understanding and Thinking. The relationship between WoU and WoT: (1) Students' WoU will build such a statistical framework. (2) Students' WoT affects how statistical modeling from their statistical knowledge. This relationship fits the philosophy of statistics and inductive inference. The statistical mental action of WoU students was to make models, analyze, calculate and generalize. The mental actions of Statistics students from WoT were interpreting, analyzing, predicting, reasoning, and inferring. The WoU and WoT mental action models developed the PPDC Cycle model and the Gal's statistical literacy model. the PPDAC Cycle model by Wild and Pfankuch, consists of five dimensions, and one of the dimensions was the PPDAC Cycle. The statistical literacy model by Gal consists of two dimensions, namely the dimensions of statistical knowledge and disposition. The novelty of this research was developing the PPDAC Cycle statistical thinking model by Wild and Pfankuch, and the statistical literacy model on the statistical knowledge dimension by Gal. This study's results can be considered in designing statistical learning in universities and measuring students' understanding and statistical thinking skills.

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Developing Big Data Analytics Course for Non-ICT Major University Students

Cited by 2 publications

References 27 publications

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Mental Action Way of Understanding (WoU) and Way of Thinking (WoT) Students in Statistics Learning in Higher Education

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