In recent years, many contents have been created and released as Open Educational Resources (OER). Developed OER are collected and registered on repositories such as OER commons and learners can use OER for self-directed learning, or educators can select OER and use them in their own educational activities. However, finding the most appropriate OER relies mainly on keyword searches and requires skills of instructional design and knowledge of the skills required in specific occupations and objectives, making it difficult for learners and teachers to select the most suitable OER.

To solve problems, OER recommending functions have been developed in recent research. OER recommending functions proposes OER based on semantic ontology utilizing metadata and the other attributes. OER recommending functions are useful as a function to propose the best OER for a learner from a repository containing various OER. However, recommendations based on semantic ontology and metadata are insufficient to classify occupations and skills, and it is difficult to directly link the acquisition of specific occupations and skills to the use of OER.

To address these challenges, we propose a two-step approach: first, required skills for each occupation or learning objective are organized into structured learning paths associated with relevant OER (learning path of OER). Based on these paths, learners are then presented with a personalized selection of OER tailored to their existing knowledge and professional background (personalization of OER). This enables more efficient and targeted learning aligned with individual needs. Regarding the learning path of OER, we developed a function based on the OSS OpenSALT to visualize a learning path that bundles the skills required for a specific occupation or purpose, and to display OER as information for acquiring each skill. For each skill included in the learning path, information on OERs that can be used to acquire that skill is presented, allowing the user to select OERs from the perspective of occupation and purpose. Regarding the personalization of OER, in the past research, we developed a function that presents learning content in a structured manner when the learner selects an occupation or objective, so that the learner can learn only those materials containing OER that are necessary for his or her own occupation or objective. However, in the past function, novice learners and experienced learners should learn the same learning contents when the same occupation or objective was selected. Therefore, in the present study, we developed an additional function to customize learning contents after checking the learner's knowledge.

The functions developed in this study will be used in research management education for university employees in Japan. There are various occupations such as research data management personnel, library staff, and IT technologist and OER and skills for each occupation have been developed by some leading universities and are being released as OER. These functions will be applied in real educational settings, and data collected through actual use will be analyzed to refine and enhance the system.

Keywords: Micro-contents, adult learning, OER, skill.