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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES)
Prakash, Chetana
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Computer Science & IT Economics, Econometrics & Finance Engineering

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Machine learning based education data mining through student session streams Hanumanthappa, Shashirekha; Prakash, Chetana
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp383-394

Abstract

Recently, significant growth in using online-based learning stream (i.e., elearning systems) have been seen due to pandemic such as COVID-19. Forecasting student performance has become a major task as an institution is focusing on improving the quality of education and students' performance. Data mining (DM) employing machine learning (ML) techniques have been employed in the e-learning platform for analyzing student session streams and predicting academic performance with good effects. A recent, study shows ML-based methodologies exhibit when data is imbalanced. In addressing ensemble learning by combining multiple ML algorithms for choosing the best model according to data. However, the existing ensemblebased model does not incorporate feature importance into the student performance prediction model. Thus, exhibits poor performance, especially for multi-label classification. In addressing this, this paper presents an improved ensemble learning mechanism by modifying the XGBoost algorithm, namely modified XGBoost (MXGB). The MXGB incorporates an effective cross-validation scheme that learns correlation among features more efficiently. The experiment outcome shows the proposed MXGBabased student performance prediction model achieves much better prediction accuracy contrary to the state-of-art ensemble-based student performance prediction model.
Multi-label feature aware XGBoost model for student performance assessment using behavior data in online learning environment Hanumanthappa, Shashirekha; Prakash, Chetana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4537-4543

Abstract

In light of recent outbreaks like COVID19, the use of online-based learning streams (i.e., e-Learning systems) has increased significantly. Institutional efforts to boost student achievement have made precise predictions of academic success a priority. To analyze student sessions-streams and anticipate academic success, e-learning platforms are starting to combine data mining (DM) with machine-learning (ML) techniques. Recent research highlights the difficulties that ML-based methods have while dealing with unbalanced data. In tackling ensemble-learning, we combine several ML algorithms to select the most appropriate approach for the given data. Current ensemble-based approaches for predicting student achievement, nevertheless, don't do exceptionally well, particularly when it comes to multi-label classification, because they don't factor the relevance of features into their approaches. This study presents multi-label feature aware XGBoost (MLFA-XGB) method that improves upon the previously used ensemble-learning technique. The MLFA-XGB makes use of a robust cross-validation approach for gaining a deeper understanding of feature relationships. The experimental results demonstrate that in comparison with the state-of-the-art ensemble-based student achievement predictive approach, this suggested MLFA-XGB based approach provides much higher accuracy for prediction. 
A web-based learning platform to assess student performance using online session activity engagement Hanumanthappa, Shashirekha; Prakash, Chetana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5240-5250

Abstract

Predicting students' performance and engagement is crucial for academic eLearning partners in colleges and universities as well as students themselves considering post-COVID-19 pandemic and university grant commission (UGC) dual degree regulation era. An educational system's data on students’ engagement in taking courses that are a significant component of an institution of higher learning with a cogent vertical syllabus can be used to make predictions. By examining how closely a student's course-taking actions correspond with the requirements of the syllabus, one can utilize the student's conduct in the classroom and online eLearning web tool as a predictor of future achievement. This paper presents a study that uses an eLearning web-based dataset to predict students' success throughout a series of online interactive sessions. The dataset records how students engage with each other during online lab work, including how many keystrokes they make, how long they spend on each task, and how well they perform on exams overall. The current methods lacks accuracy to assess student performance and engagement with high precision. In addressing this paper introduces novel multi-label ensemble learning (MLEL) using XGBoost (XGB) and K-fold cross validation. Experiment outcome shows the proposed (MLEL-XGB) achieves much improved outcome than other existing models.
Co-Authors Hanumanthappa, Shashirekha