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This paper proposes an approach for integrating social networks into intelligent tutoring systems (ITS), especially for predicting student performance (PSP). Firstly, we review the main concepts of the ITS as well as matrix factorization technique and social matrix factorization in Recommender Systems (RS). Next, this work introduces how to map the ITS data into the context of user, item and rating...
This work introduces several methods which can be used for building the course recommendation systems. By using course recommendation system, students can early predict their learning results as well as select appropriate courses so that they can have better studying plans. After introducing the methods, this study also compares and analyzes their performance by using a real educational data set....
This study proposes an approach for building a semi-automatic consultancy (question-answering) system via mobile/Internet networks. This approach is a combination of natural language (text) processing and machine learning method. For building the system, at first, we need to build modules for sending and receiving messages via SMS/Email/Webpage. These modules are used for users to send/receive their...
Detecting abnormal usages in intelligent systems, especially in smart home systems, is an important task. By exploring log data, useful information and patterns can be discovered which may help users/organizations to better understand the usage of their appliances and to distinguish unnecessary usages as well as abnormal problems which can cause waste, damages, or even fire. This work proposes several...
Student Modeling is an important part of an Intelligent Tutoring System. The student model tracks information of individual student (e.g., Time spent on problems, hints requested, correct answers, etc). One of the important tasks in student modeling is predicting student performance, where the system can provide the students early feedbacks to help them improving their study results. In this work,...
This study proposes an approach for building a Semi-Automatic Agricultural Extension Support System based on mobile communication networks and machine learning. This system can be used to link farmers and agricultural experts, thus, it can be considered as an online “farmer-expert bridge”. To build the system, at first, we need to build modules for sending and receiving SMS/MMS messages. These modules...
Recently, researchers have shown that the Area Under the ROC Curve (AUC) has a serious deficiency since it implicitly uses different misclassification cost distributions for different classifiers. Thus, using the AUC can be compared to using different metrics to evaluate different classifiers [1]. To overcome this incoherence, the H measure was proposed, which uses a symmetric Beta distribution to...
This work proposes a novel approach - personalized forecasting - to take into account the sequential effect in predicting student performance (PSP). Instead of using all historical data as other methods in PSP, the proposed methods only use the information of the individual students for forecasting his/her own performance. Moreover, these methods also encode the "student effect" (e.g. how...
Class imbalance is one of the challenging problems for machine learning algorithms. When learning from highly imbalanced data, most classifiers are overwhelmed by the majority class examples, so the false negative rate is always high. Although researchers have introduced many methods to deal with this problem, including resampling techniques and cost-sensitive learning (CSL), most of them focus on...
Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach, we...
This paper introduces and compares some techniques used to predict the student performance at the university. Recently, researchers have focused on applying machine learning in higher education to support both the students and the instructors getting better in their performances. Some previous papers have introduced this problem but the prediction results were unsatisfactory because of the class imbalance...
This paper compares the accuracy of decision tree and Bayesian network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam; and the Asian Institute of Technology (AIT), a small international postgraduate institute in Thailand that draws students from...
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