ارائه يك سكوي كم كد با هدف توسعه سفارشي پيش خوان هاي تجزيه و تحليل عملكرد مدرسان براي سيستم مديريت يادگيري مودل

Nowadays, analyzing the performance of instructors plays a significant role in improving the quality of teaching. eva‎luating instructors can be effective in designing and delivering a course, managing the classroom, managing time, and supporting learners. Continuous eva‎luation of instructors can prevent the recruitment of underqualified instructors, which would otherwise lead to weaknesses in education. In the current situation, where Learning Management Systems (LMS) provide an environment for interaction and enhancing the quality of education, offering dashboards aimed at analyzing the performance of instructors in each educational institution can greatly assist in extracting monitoring reports on the quality of courses and improving instructor performance. Additionally, various educational criteria for eva‎luating instructor performance can help institutions. These criteria may vary for different institutions. However, researches conducted in the field of providing performance analysis dashboards for instructors does not meet the existing need for creating various dashboards, and there is a need for a new solution to address this requirement. In this research, we proposed a low-code development platform for developing performance analysis dashboards for instructors in the Moodle LMS, which can be customized based on instructor eva‎luation criteria. In the provided platform, using the feature model, the performance eva‎luation indicators of instructors have been categorized. Using this platform helps reduce the time and cost of producing and designing dashboards. The present study was eva‎luated in two parts. In the first part, it was examined using three case studies with different topics. Finally, in the second part, the usability of the developed platform was eva‎luated by conducting an online workshop and completing a questionnaire by school administrators and individuals familiar with the LMS. The eva‎luation results indicate an increase in the speed of developing performance analysis dashboards and user satisfaction.

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