Recommender system for predicting student performance

AbstractRecommender 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 compare recommender system techniques with traditional regression methods such as logistic/linear regression by using educational data for intelligent tutoring systems. Experimental results show that the proposed approach can improve prediction results

Data-driven curation, learning and analysis for inferring evolving IoT botnets in the wild

© 2019 Association for Computing Machinery. The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructure realms. Several challenges impede addressing IoT security at large, including, the lack of IoT-centric data that can be collected, analyzed and correlated, due to the highly heterogeneous nature of such devices and their widespread deployments in Internet-wide environments. To this end, this paper explores macroscopic, passive empirical data to shed light on this evolving threat phenomena. This not only aims at classifying and inferring Internet-scale compromised IoT devices by solely observing such one-way network traffic, but also endeavors to uncover, track and report on orchestrated “in the wild” IoT botnets. Initially, to prepare the effective utilization of such data, a novel probabilistic model is designed and developed to cleanse such traffic from noise samples (i.e., misconfiguration traffic). Subsequently, several shallow and deep learning models are evaluated to ultimately design and develop a multi-window convolution neural network trained on active and passive measurements to accurately identify compromised IoT devices. Consequently, to infer orchestrated and unsolicited activities that have been generated by well-coordinated IoT botnets, hierarchical agglomerative clustering is deployed by scrutinizing a set of innovative and efficient network feature sets. By analyzing 3.6 TB of recent darknet traffic, the proposed approach uncovers a momentous 440,000 compromised IoT devices and generates evidence-based artifacts related to 350 IoT botnets. While some of these detected botnets refer to previously documented campaigns such as the Hide and Seek, Hajime and Fbot, other events illustrate evolving threats such as those with cryptojacking capabilities and those that are targeting industrial control system communication and control services

PERSPECTIVES ON PERCEPTIONS AND PRACTICE THROUGH LEARNING CULTURES OF ENGLISH-SPEAKING COUNTRIES OF HIGH-QUALITY ENGLISH STUDIES PROGRAM STUDENTS, SCHOOL OF FOREIGN LANGUAGES, CAN THO UNIVERSITY, VIETNAM

The overall goal of the project is to understand the awareness or perceptions and practice of students of the High-quality English Studies program, School of Foreign Languages (SFL), Can Tho University (CTU), Vietnam when studying cultures of English-speaking countries (CESCs) to improve intercultural competence. The research on the perspectives or opinions of 200 High-quality English Studies students, 12 of them joining the semi-structured interview, about their awareness and practice through learning CESCs. The analysis would help the researcher understand the difficulties of students when studying cultural modules from English-speaking countries at SFL, CTU. The research results would suggest solutions to overcome the difficulties that students encounter, and at the same time provide factors that contribute to improving the intercultural competence of language students. Also, through the research results, despite many obstacles in the process of absorbing culture from cultural modules, students still retain their interest and love for the course-CESCs. However, it can be seen that the difficulty that many students often encounter is still cultural differences, thereby raising awareness of the need to learn cultures for students. Next is to design teaching materials to become more attractive and attractive, proactively find opportunities to communicate with foreigners.  Article visualizations

AN INTRODUCTION TO FACTORIZATION TECHNIQUE FOR BUILDING RECOMMENDATION SYSTEMS

Recommender System (RS) is successfully applied in predicting user preferences. For instance, RS has been used in many areas such as in e-commerce (for online shopping), in entertainments (music/movie/video clip... recommendation), and in education (learning resource recommendation). In Vietnam, e-commerce is initially growing, thus, RS may be an interesting and potential research topic in the next years. In this work, we shortly introduce about the RS and thoroughly describe one of the prominent techniques in RS which is Matrix Factorization (MF). We describe the MF in details so that the new reader can understand and implement it easily. In the experiments, we set up and compare the MF with other techniques using three data sets from two different areas which are entertainment and education. Experimental results show that the MF can work well in both entertainment (e-commerce) and education domain

Predicting Student Performance in an Intelligent Tutoring System

-Predicting student performance (PSP) is an important task in Student Modeling where we would like to know whether the students solve the given problems (tasks) correctly, so that we can understand how the students learn, provide them early feedbacks, and help them getting better in studying. This thesis introduces several approaches, which mainly base on state-ofthe- art techniques in Recommender Systems (RS), for student modeling, especially for PSP. First, we formulate the PSP problem and show how to map this problem to rating prediction task in RS and to forecasting problem. Second, we propose using latent factor models, e.g., matrix factorization, for student modeling. These models could implicitly take into account the student and task latent factors (e.g., slip and guess) as well as student effect/bias and task effect/bias. Moreover, there is a fact that similar students may have similar performances, we suggest using k-nearest neighbors collaborative filtering to take into account the correlations between the students and the tasks. Third, in student's problem solving, each student performs several tasks, and each task requires one or many skills, while the students are also required to master the skills that they have learned. We propose to exploit such multiple relationships by using multi-relational matrix factorization approach. Fourth, as the student performance (student knowledge) cumulates and improves over time, a trend line could be observed in his/her performance. Similar to time series, for solving this problem, forecasting techniques would be reasonable choices. Furthermore, it is well-know that student (human) knowledge is diverse, thus, thought and performance of one student may differ from another one. To cope with these aspects, we propose personalized forecasting methods which use the past performances of individual student to forecast his/her own future performance. Fifth, since student knowledge changes over time, temporal/sequential information would be an important factor in PSP. We propose tensor factorization methods to model both the student/task latent factors and the sequential/temporal effects. Sixth, we open an issue for recommendation in e-learning, that is, recommending the tasks to the students. This approach can tackle existing issues in the literature since we can recommend the tasks to the students using their performance instead of their preference. Based on student performance, we can recommend suitable tasks to the students by filtering out the tasks that are too easy or too hard, or both, depending on the system goal. Furthermore, we propose using context-aware factorization approach to utilize multiple interactions between the students and the tasks. Seventh, we discover a characteristic in student performance data, namely class imbalance problem, i.e., the number of correct solutions are higher than the number of incorrect solutions, which may hinder classifiers' performance. To tackle this problem, we introduce several methods as well as introducing a new evaluation measure for learning from imbalanced data. Finally, we validate the proposed methods by many experiments. We compare them with other state-of-the-art methods and empirically show that, in most of the cases, the proposed methods can improve the prediction results. We therefore conclude that our approaches would be reasonable choices for student modeling, especially for predicting student performance. Last but not least, we raise some open issues for the future research in this area

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