Student Modeling within a Computer Tutor for Mathematics: Using Bayesian Networks and Tabling Methods

Intelligent tutoring systems rely on student modeling to understand student behavior. The result of student modeling can provide assessment for student knowledge, estimation of student¡¯s current affective states (ie boredom, confusion, concentration, frustration, etc), prediction of student performance, and suggestion of the next tutoring steps. There are three focuses of this dissertation. The first focus is on better predicting student performance by adding more information, such as student identity and information about how many assistance students needed. The second focus is to analyze different performance and feature set for modeling student short-term knowledge and longer-term knowledge. The third focus is on improving the affect detectors by adding more features. In this dissertation I make contributions to the field of data mining as well as educational research. I demonstrate novel Bayesian networks for student modeling, and also compared them with each other. This work contributes to educational research by broadening the task of analyzing student knowledge to student knowledge retention, which is a much more important and interesting question for researchers to look at. Additionally, I showed a set of new useful features as well as how to effectively use these features in real models. For instance, in Chapter 5, I showed that the feature of the number of different days a students has worked on a skill is a more predictive feature for knowledge retention. These features themselves are not a contribution to data mining so much as they are to education research more broadly, which can used by other educational researchers or tutoring systems

Social Welfare Maximization Auction in Edge Computing Resource Allocation for Mobile Blockchain

Blockchain, an emerging decentralized security system, has been applied in many applications, such as bitcoin, smart grid, and Internet-of-Things. However, running the mining process may cost too much energy consumption and computing resource usage on handheld devices, which restricts the use of blockchain in mobile environments. In this paper, we consider deploying edge computing service to support the mobile blockchain. We propose an auction-based edge computing resource market of the edge computing service provider. Since there is competition among miners, the allocative externalities (positive and negative) are taken into account in the model. In our auction mechanism, we maximize the social welfare while guaranteeing the truthfulness, individual rationality and computational efficiency. Based on blockchain mining experiment results, we define a hash power function that characterizes the probability of successfully mining a block. Through extensive simulations, we evaluate the performance of our auction mechanism which shows that our edge computing resources market model can efficiently solve the social welfare maximization problem for the edge computing service provider

Profit Maximization Auction and Data Management in Big Data Markets

A big data service is any data-originated resource that is offered over the Internet. The performance of a big data service depends on the data bought from the data collectors. However, the problem of optimal pricing and data allocation in big data services is not well-studied. In this paper, we propose an auction-based big data market model. We first define the data cost and utility based on the impact of data size on the performance of big data analytics, e.g., machine learning algorithms. The big data services are considered as digital goods and uniquely characterized with "unlimited supply" compared to conventional goods which are limited. We therefore propose a Bayesian profit maximization auction which is truthful, rational, and computationally efficient. The optimal service price and data size are obtained by solving the profit maximization auction. Finally, experimental results on a real-world taxi trip dataset show that our big data market model and auction mechanism effectively solve the profit maximization problem of the service provider.Comment: 6 pages, 9 figures. This paper was accepted by IEEE WCNC conference in Dec. 201