KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Kraishan, Obada

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Predicting the Risk of Ultimate Low Ratings in Online Courses Using Machine Learning: Analyzing Engagement and Complexity for AI-Driven Early Instructional Communication Interventions
    (2026-01-01)
    Kraishan, Obada
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Vijaranakul, Nattadet
    ;
    Kee, Kerk
    Online asynchronous courses have become a common way of learning, especially after the COVID-19 epidemic. Despite their convenience and flexibility, these courses often suffer from low engagement and/or poor course quality, ultimately reflected by a low rating. A low rating is often noted at the end of a course, by when it is often too late to intervene or improve the course. The goal of this study is twofold. First, we aim to identify key factors influencing the ultimate ratings for online asynchronous courses. Second, we then build an AI model to predict the risk of ultimate low ratings before a course is fully completed. Inspired by Moore’s model of interaction, which includes two main factors: learner-content and learner-instructor interactions, we derived two features from the dataset: course complexity and course engagement. We used a dataset of 191,849 Udemy courses collected from May 2024 to August 2024. To predict the ultimate low ratings in advance so mid-course interventions are possible, we applied machine learning models, including Logistic Regression, Random Forest, and XGBoost. In our experiments, the XGBoost model achieved the highest performance with 71% accuracy, with course complexity and the number of reviews left by learners being the two most influential factors in predicting a low course rating. Constantly monitoring course complexity and the number of course reviews can help with early detection. The findings can provide valuable, actionable insights and guidance for online education platforms, especially for instructors to adjust their courses before the courses are complete.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Visualizing Political Communication Trends across Generations on X (Twitter): Insights Through Topic Modeling and Word Clouds
    (2024-01-01)
    Udomwisanpat, Prinwat
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Kraishan, Obada
    ;
    Srestasatheirn, Panu
    ;
    Lawawirojwong, Siam
    This study examines the interests and significance of words on Twitter (or X) across different generational groups: Baby Boomers, Generation X, Generation Y, and Generation Z. Using Topic Modeling with Latent Dirichlet Allocation (LDA), the research explores relationships and word importance within each group. As the results of topic modeling are not always easy to interpret, we used word cloud visualization to help make sense of the results for each generation. The findings reveal distinct patterns: Baby Boomers frequently mention print media, news websites, and prominent Thai political figures; Generation X emphasizes individuals and local political issues in Bangkok; Generation Y discusses political and social events; and Generation Z uniquely questions political and social activities. This research methodology is applicable across languages and tasks, offering insights into generational behaviors and interests.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Leveraging Race Prediction Algorithms to Enhance Team Composition in Big Data Science Teams
    (2024-01-01)
    Chumthong, Thanathip
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Kraishan, Obada
    ;
    Kee, Kerk F.
    ;
    Narabin, Akan
    As big data science projects scale in complexity, optimizing team composition has become vital for improving creativity, productivity, and project success. We explore the possibility of incorporating race prediction algorithms for enhancing racial diversity in team composition in big data science projects. This paper evaluates five race prediction algorithms - wru, ethnicolr, ethnicolr2, pyethnicity, and rethnicity - and then discuss their potential in supporting racially diverse team assembly in big data projects. Utilizing three datasets, we assess algorithm performance and applicability, emphasizing their role in building balanced teams that enhance agility, inclusivity, and bias mitigation. We present an actionable methodology for integrating demographic insights into team management. In addition, we propose ethical safeguards to ensure responsible race prediction use, recommending data privacy measures, aggregate-only data handling, and transparency in communication. We argue that when used within ethical constraints, race prediction can support robust team processes, reduce reliance on less diverse teams, and ultimately facilitate more creative and equitable big data project outcomes.