Automated programming assessment systems are useful tools to track the learning progress of students automatically and thereby reduce the workload of educators. They can also be used to gain insights into how students learn, making it easier to formulate strategies aimed at enhancing learning performance. Rather than functional code which is always inspected, code quality remains an essential aspect to which not many educators consider when designing an automated programming assessment system. In this study, we applied data mining techniques to analyze the results of an automated assessment system to reveal unexpressed patterns in code quality improvement that are predictive of final achievements in the course. Cluster analysis is first utilized to categorize students according to their learning behavior and outcomes. Cluster profile analysis is then leveraged to highlight actionable factors that could affect their final grades. Finally, the same factors are employed to construct a classification model by which to make early predictions of the students' final results. Our empirical results demonstrate the efficacy of the proposed scheme in providing valuable insights into the learning behaviors of students in novice programming courses, especially in code quality assurance, which could be used to enhance programming performance at the university level. INDEX TERMS automated programming assessment system, code quality, educational data mining, early learning achievement detection, programming education

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An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with much needed resources. In such systems, ontology construction is one of the most important phases. Since there are abundant documents on the Internet, useful learning materials can be acquired openly with the use of an ontology. However, due to the lack of system support for ontology construction, it is difficult to construct self-instructional materials for Vietnamese people. In general, the cost of manual acquisition of ontologies from domain documents and expert knowledge is too high. Therefore, we present a support system for Vietnamese ontology construction using pattern-based mechanisms to discover Vietnamese concepts and conceptual relations from Vietnamese text documents. In this system, we use the combination of statistics-based, data mining, and Vietnamese natural language processing methods to develop concept and conceptual relation extraction algorithms to discover knowledge from Vietnamese text documents. From the experiments, we show that our approach provides a feasible solution to build Vietnamese ontologies used for supporting systems in education.

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