Study on customized knowledge graph of student pilot learning in FITS training

Abstract

Knowledge graphs are useful sources for various AI applications, however the basic paradigm to support pilot training is still unclear. In the paper, It is proposed to generate the customized knowledge graph of flight trainings using machine learning method for the flight training program. In order to provide the successful key to the further understanding of the learning problems between the students and the instructors. In this research, we collected data from an aeronautical academic in Taiwan that students were trained for Recreation Pilot License Program. We performed a test on 24 students at the first of each training course, 16 data of collected been used on building the module, 8 of them used to exam the module. There are 12 courses in the training program, and 30 hours total time were suggested by academic. The score which we applied on test were based on LCG method which is the sum of Maneuver and SRM Grades. For the indicators of course component in Learner Centered Grading, namely (a) CCS1: Operation & Effect of Controls; (b) CCS2: Straight & Level; (c) CCS3: Climbing & Descending; (d) CCS4: Turning; (e) CCS5: Stalling; (f) CCS6: Revision; (g) CCS7: Circuits; (h) CCS8: Cross-Wind Training; (i) CCS9: Circuit Emergency; (j) CCS10: Solo Circuit; (k) CCS11: Forced Landing; and (l) CCS12: Precautionary & Searching Landing. Through the method of Knowledge Graph, we deduct and predict the number of hours that need to be added for each student’s learning. Using the dynamic knowledge graph to display the key issues of the course learning continuously, and make follow-up decisions for the students, instructors and airliners.

Intelligent Recommendation Algorithm Combining RNN and Knowledge Graph

“…For the problem of matching radiological imaging with medical reports, Hou et al applied knowledge graph to improve the original method and proved that the improvement possessed better perfor-mance [14]. Huang C L. and Huang C C. applied knowledge graph to customize the flight training of student pilots, and the results showed that it could effectively demonstrate the problems in course learning as well as provide a basis for subsequent [15]. Fan et al applied knowledge mapping to the analysis of sepsis-related knowledge literature to sort out its historical evolution, research content, etc., so as to achieve the prediction of its development trend [16].…”