Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Science Artificial Intelligence and Communications
Noor Anida Zaria Mohd Noor
Universiti Pendidikan Sultan Idris
Computer Science & IT Earth & Planetary Sciences Engineering

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A review on learning analytics in mobile learning and assessment Teik Heng Sun; Muhammad Modi Lakulu; Noor Anida Zaria Mohd Noor
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1924-1941

Abstract

Employers are facing difficulties in selecting the most suitable candidates for employment and the transition from education to work is challenging for young graduates. Therefore, it is important to have indicators that could show the suitability of a potential candidate for his/her chosen job. A person who possesses knowledge but lacks confidence may struggle to perform assigned tasks, while an overly confident person with limited knowledge is likely to make errors in their job. Although there is existing research on learning analytics related to assessments, the research on learning analytics specifically focused on the confidence-knowledge relationship based on assessment data is still lacking. This article aims to examine the application of analytics in providing insights based on assessment data that can be utilized by potential employers. To achieve this, a systematic review was carried out, analyzing a total of 141 articles. The findings contribute to a better understanding of the use of assessment analytics in identifying the knowledge-confidence quadrants of students.
Web Scraping Design for Text Data Acquisition on Platform X in Adolescent Sexual Deviant Behavior Research Yanti Yusman; Noor Anida Zaria Mohd Noor
Journal of Computer Science, Artificial Intelligence and Communications Vol 3 No 1 (2026): May 2026
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v3i1.182

Abstract

The increasing use of social media among adolescents has generated vast amounts of digital textual data that can be utilized to investigate online behavioral phenomena, including discussions related to sexual behavior. As one of the most active social media platforms, X provides a rich source of publicly available textual information that reflects users' interactions, opinions, and behavioral expressions. However, obtaining high-quality textual data for behavioral research requires a systematic, transparent, and reproducible data acquisition process. This study aims to design a web scraping framework for acquiring textual data from Platform X to support research on adolescent sexual behavior deviance. The research adopts a Design Science Research (DSR) approach to develop a structured data acquisition pipeline consisting of requirement analysis, keyword formulation, web scraping design, data harvesting, data preprocessing, and dataset construction. The proposed framework emphasizes methodological rigor by integrating data quality assessment, reproducibility, and ethical considerations throughout the data acquisition process. The resulting dataset comprises structured textual data and relevant metadata that are prepared for subsequent analytical stages, such as natural language processing, text mining, machine learning, and behavioral pattern analysis. Furthermore, the proposed web scraping framework provides a systematic approach for researchers to collect social media data efficiently while ensuring data consistency and traceability. This study contributes to the field of social media analytics by providing a replicable methodology for acquiring textual data from Platform X and establishing a reliable foundation for future research on adolescent online behavior and digital risk assessment. The findings are expected to facilitate the development of evidence-based analytical models for understanding behavioral patterns in digital environments while supporting future studies employing Big Data Analytics and Computational Social Science.
Co-Authors Muhammad Modi Lakulu Teik Heng Sun Yanti Yusman