No abstract

Education always plays an important role in building up every country around the world. Hence, educational decision making support is significant to students, educators, and educational organizations. The support will be more valuable if a lot of relevant data and knowledge mined from data are available for educational managers in their decision making process. Nevertheless, educational decision support system development is non-trivial and different from organization to organization due to the peculiar features of each educational organization. Besides, an academic credit system is nowadays very widely-used in many educational organizations. Due to the flexibility of a credit system of education, applying data mining techniques to educational data for knowledge discovery is challenging. In this paper, we propose a knowledge-driven educational decision support system for education with a semester credit system by taking advantage of educational data mining. During system development, we have figured out that knowledge discovery from educational data in a semester credit system is full of many problems remaining unsolved with a semester credit system. Tackling those problems, our resulting system can provide educational managers with actionable knowledge discovered from educational data. Such knowledge-driven decision support is helpful for educational managers to make more appropriate and reasonable decisions about student's study and further give support to students for their graduation. Above all, a waste of effort, time, and money can be avoided accordingly for both students and educators.Keywords-educational decision support system; knowledgedriven decision support system; data-driven decision support system; educational data mining; actionable knowledge I.

This paper deals with a single machine scheduling problem with availability constraints. The jobs are splitable and lower bound on the size of each sub-job is imposed. The objective is to find a feasible schedule that minimizes the makespan. The proposed scheduling problem is proved to be NP-hard in the strong sense. Some effective heuristic algorithms are then proposed. Additionally, computational results show that the proposed heuristic performs well.

Nowadays, knowledge discovered from educational data sets plays an important role in educational decision making support. One kind of such knowledge that enables us to get insights into our students' characteristics is cluster models generated by a clustering task. Each cluster model presents the groups of similar students by several aspects such as study performance, behavior, skill, etc. Many recent educational data clustering works used the existing algorithms like k-means, expectation-maximization, spectral clustering, etc. Nevertheless, none of them considered the incompleteness of the educational data gathered in an academic credit system although incomplete data handling was figured out well with several different general-purpose solutions. Unfortunately, early in-trouble student detection normally faces data incompleteness as we have collected and processed the study results of the second-, third-, and fourth-year students who have not yet accomplished the program as of that moment. In this situation, the clustering task becomes an inevitable incomplete educational data clustering task. Hence, our work focuses on an incomplete educational data clustering approach to the aforementioned task. Following kernel-based vector quantization, we define a robust effective simple solution, named VQ_fk_nps, which is able to not only handle ubiquitous data incompleteness in an iterative manner using the nearest prototype strategy but also optimize the clusters in the feature space to reach the resulting clusters with arbitrary shapes in the data space. As shown through the experimental results on real educational data sets, the clusters from our solution have better cluster quality as compared to some existing approaches.