Intelligent E-Learning System with Personalized Misconception Diagnose and Learning Path Guidance

In recent years, to advances network technology, IT-enabled learning and support learning are important in on-line education. More learners obtain knowledge by the Web-Learning Instruction (WBI). Learners usually induce the problem of misconception and cognitive overload when they use Web-based learning system. At present, most of the studies in the on-line education either concentrate on the technological aspect (e.g. personalization technology development) or focus on adapting learner’s interests or browsing behaviors, while, learner’s ability and level of knowledge is neglected. Therefore, it is important to consider learner’s ability while designing web-based learning system. This study developed an On-line Knowledge Diagnose System (OKDS) to diagnose learner’s misconception and provide personalized remedial guidance that based on a graphic organizer technology - concept map. The results indicate that the OKDS can effectiveness enhance learners learner’s learning performance and learner also has positive perception of OKDS

Domain Adaptation for Roasted Coffee Bean Quality Inspection

Current research in machine learning primarily focuses on raw coffee bean quality, hampered by limited labeled datasets for roasted beans. This study proposes a domain adaptation approach to transfer knowledge acquired from raw coffee beans to the task of inspecting roasted beans. The method maps the source and target data, originating from different distributions, into a shared feature space while minimizing distribution discrepancies with domain adversarial training. Experimental results demonstrate that the proposed approach effectively uses annotated raw bean datasets to achieve a high-performance quality inspection system tailored specifically to roasted coffee beans