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This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE) Journal of Information Systems Engineering and Business Intelligence
Maresha Caroline Wijanto
Maranatha Christian University
Computer Science & IT Education

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Thesis Supervisor Recommendation with Representative Content and Information Retrieval Maresha Caroline Wijanto; Rachmi Rachmadiany; Oscar Karnalim
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 2 (2020): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.2.143-150

Abstract

Background: In higher education in Indonesia, students are often required to complete a thesis under the supervision of one or more lecturers. Allocating a supervisor is not an easy task as the thesis topic should match a prospective supervisor’s field of expertise.Objective: This study aims to develop a thesis supervisor recommender system with representative content and information retrieval. The system accepts a student thesis proposal and replies with a list of potential supervisors in a descending order based on the relevancy between the prospective supervisor’s academic publications and the proposal.Methods: Unique to this, supervisor profiles are taken from previous academic publications. For scalability, the current research uses the information retrieval concept with a cosine similarity and a vector space model.Results: According to the accuracy and mean average precision (MAP), grouping supervisor candidates based on their broad expertise is effective in matching a potential supervisor with a student. Lowercasing is effective in improving the accuracy. Considering only top ten most frequent words for each lecturer’s profile is useful for the MAP.Conclusion:An arguably effective thesis supervisor recommender system with representative content and information retrieval is proposed. 
Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums Hapnes Toba; Yolanda Trixie Hernita; Mewati Ayub; Maresha Caroline Wijanto
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i1.26024

Abstract

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom’s epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom’s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.
Co-Authors Hapnes Toba Mewati Ayub Oscar Karnalim, Oscar Rachmi Rachmadiany Yolanda Trixie Hernita