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Learning analytics contribution in education and child development: a review on learning analytics

Abstract

Learning Analytics is a new field of research that appears as a link between educator data and students. The Learning Analytics is also able to provide information about decision making to understand and optimise the learning process. Early childhood education is believed to be very important because the learner is open and always tries new things and is considered very meaningful for future processes in the development of all aspects of their personality. In this study, we aimed at investigating the application of learning analytics and how the learning process on child development in early childhood education. The Article Search Process is carried out on two databases, ScienceDirect and IEEE. In this study, the most important keywords are Learning Analytics and early childhood. The results of the search are 45 articles: (31/45) ScienceDirect and (14/45) IEEE, from 2012 to 2017. They are thoroughly explored in the Learning Analytics process, data collection and pre-processing, analysi.....

Article Published 2018 908 hits Available

2

Translation errors of noodles product cooking instructions from Indonesian into English

Agus Dwiyanto

Abstract

This paper discussed the translation errors in various top brands of instant noodles in Indonesia, ranging from the products produced by the local industries to the one imported from overseas. In this research, the researchers used a descriptive qualitative research method. The sources of the data for the objects of the research were ten cooking instructions or procedures of various brands of instant noodles in Indonesia, translated from Indonesian into English. The data were collected by documentation techniques. The types of data were words, phrases and sentences. The data were classified and tabulated based on the error types, following the standard of ATA’s Framework for Standard Error Making Classification. The result of the analysis showed that there were 40 translation errors, found in 10 of 23 error categories with Omission as the most frequent error produced. The three most prominent error categories found in the instant noodles’ cooking instructions are Omission, Mistrans.....

Article Published 2019 717 hits Available

3

Industrial mining policy, role of innovation and firm business performance in Indonesia mining companies

Agus Jayana

Abstract

This study endeavors to examine the complex correlation between industrial mining policy (IMP), firm business performance (FBP), and innovation (IN). A positivist approach is employed in this research, utilizing a cross-sectional survey to gather primary data. The collected data is subjected to quantitative analysis using partial least squares structural equation modeling (PLS-SEM). The specific focus of this investigation centers around Indonesian mining firms possessing mining business permits (IUP), with a total sample size of 385 respondents who participated in the survey. The findings indicate all constructs and dimensions are categorized as substantial and have predictive relevance because they have a value of R2 _ 0.75 and Q2 _ 0. This study also demonstrates that innovation acts as a mediator between the industrial mining policy and firm business performance, since the path coefficient of the indirect effect of IMP _ IN _ FBP (0.496) is higher than the direct effect of IMP _ FB.....

Thesis Published 2024 411 hits Available

4

A Game-based Learning Analytics Platform for the assessment of creativity and logic development in 3-4 year old children

Rahmadi Agus

Abstract

The Game-based Learning Analytics Platform (GBLAP) is an innovative solution to the challenges currently facing the education sector. Its main benefits include improving assessment accuracy and providing more personalized and engaging data-driven learning experiences. This study examines the effectiveness of GBLAP in enhancing the accuracy of creativity and logic assessments in 3-4 year-old children compared to traditional methods, evaluates its acceptability among teachers and parents, and assesses the effectiveness of machine learning models in classifying child development data based on game interactions. The platform integrates artificial intelligence to create a more personalized and optimized learning environment. The System Usability Scale (SUS) instrument was adopted and adapted to obtain feedback from 2 teachers, 10 parents, and 10 children from Manhajul Husna Early Childhood Education, South Kalimantan, Indonesia, and 2 teachers, 10 parents, and 10 children from TASKA PERMATA.....

Thesis Published 2024 816 hits Available

5

Learning Analytics contribution in education and child development: a review on learning analytics

Rahmadi Agus

Abstract

Learning Analytics is a new field of research that appears as a link between educator data and students. The Learning Analytics is also able to provide information about decision making to understand and optimise the learning process. Early childhood education is believed to be very important because the learner is open and always tries new things and is considered very meaningful for future processes in the development of all aspects of their personality. In this study, we aimed at investigating the application of learning analytics and how the learning process on child development in early childhood education. The Article Search Process is carried out on two databases, ScienceDirect and IEEE. In this study, the most important keywords are Learning Analytics and early childhood. The results of the search are 45 articles: (31/45) ScienceDirect and (14/45) IEEE, from 2012 to 2017. They are thoroughly explored in the Learning Analytics process, data collection and pre-processing, analysi.....

Article Published 2018 107 hits Available

6

Optimizing ischemic stroke classification using machine learning for clinical applicability at Banjarmasin Hospitals

Byna, Agus

Abstract

The increasing prevalence of ischemic stroke_particularly in Banjarmasin, Indonesia_demands the development of accurate, robust, and interpretable classification models to support timely and effective clinical decision-making. Conventional approaches and standard machine learning techniques often fall short when addressing the challenges posed by highly imbalanced medical datasets (91.40% majority vs. 8.60% minority) and limited model transparency, both of which impede clinical adoption. To overcome these limitations, this study introduces a rigorously optimized framework based on the XGBoost algorithm, enhanced by the Synthetic Minority Over-sampling Technique (SMOTE) to correct for class imbalance. The methodology incorporates a structured Train-Validation-Test split, 10-fold crossvalidation, and performance assessment using mean (_) and standard deviation (_). Two hyperparameter tuning strategies were implemented, with Random Forest employed as a comparative benchmark. SHapley Addit.....

Thesis Published 2025 95 hits Available

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