Efficiency of Multi-instance Learning in Educational Data Mining
Educational data mining (EDM) is one of the emerging technologies in recent years. The various changes in the process of teaching and learning have brought in a lot of challenges to the stakeholders to understand the learners toward the different methods of teaching and the way they perform in various teaching environments. This chapter is an application of Baker’s taxonomy in an educational dataset to predict course outcome of the learners during the middle of the course. The experiment is conducted using different single and multi-instance-based learning algorithms. The efficiency of the single and multi-instance learning algorithms was measured using the accuracy rates and the time taken to build the model. In single instance algorithm, decision stump tree was found very effective and in multi-instance learning, the Simple MI method was found very effective. The precision of the instance-based learning algorithms is calculated using Wilcoxon rank method, and multi-instance learning algorithm is found to be more accurate than the single instance learning techniques.
KeywordsPrediction Single instance learning Multi-instance learning Accuracy ROC PRC MCC Rank
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