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Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem-solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one-to-one personalized and adaptive teaching. In this paper, a novel Flowchart-based Intelligent Tutoring System ( FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem-solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi-agent system using an automatic text-to-flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem-solving skills. In the end, in order to investigate the efficacy of FITS in problem-solving ability acquisition, a quasi-experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem-solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem-solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge. [ABSTRACT FROM AUTHOR]

Lesson planning imposes a significant burden on teachers as they need to prepare different lesson plans for different classes according to various constraints. SmartLP, a case-based lesson planning system, has been implemented as a means of assisting teachers in constructing quality lesson plans more quickly. SmartLP enables teachers to retrieve previous lesson plans and customise them according to their constraints as they should be tailored to accommodate differences in students, teachers and the facilities available. An evaluation study was designed to examine the effectiveness of a Case Based Reasoning ( CBR) approach through its cycle of retrieve, reuse, revise and retain in assisting teachers in constructing lesson plans and to assess the effects, in terms of time and quality, the system had on lesson plan preparation. This paper presents the results of the study. The study revealed that all the lesson plans constructed with SmartLP took significantly less time than lesson plans constructed without. In addition, where the planning tasks had greater similarities to the cases in the case base, they took even less time to be constructed. No significant difference in the quality of the plans, measured by a scoring system, was noticed between the control and experimental groups. [ABSTRACT FROM AUTHOR]