Research ‎on ‎Artificial ‎Intelligence-assisted ‎Design ‎and ‎Dynamic ‎Adaptive ‎Learning ‎Module ‎in ‎Self-study ‎Examination ‎Systems

Abstract

Under ‎the ‎development ‎trend ‎of ‎diversification ‎of ‎education ‎forms, ‎the ‎self-study ‎examination ‎system ‎faces ‎a ‎series ‎of ‎dilemmas ‎such ‎as ‎imperfect ‎support ‎system ‎and ‎slow ‎updating ‎and ‎iteration ‎speed. ‎As ‎a ‎result, ‎this ‎paper ‎designs ‎and ‎proposes ‎a ‎self-study ‎examination ‎system ‎with ‎the ‎addition ‎of ‎intelligent ‎auxiliary ‎module ‎and ‎dynamic ‎adaptive ‎learning ‎module. ‎It ‎explores ‎the ‎construction ‎of ‎the ‎self-study ‎examination ‎management ‎and ‎service ‎system ‎from ‎three ‎aspects: ‎the ‎architecture ‎technology ‎of ‎the ‎self-study ‎examination ‎management ‎and ‎service ‎system, ‎the ‎tripartite ‎operation ‎topology ‎and ‎the ‎module ‎characteristics. ‎Aiming ‎at ‎the ‎problem ‎that ‎the ‎Teaching-Learning ‎Optimization ‎Algorithm ‎(TLBO) ‎is ‎prone ‎to ‎premature ‎maturity ‎and ‎low ‎precision, ‎an ‎adaptive ‎change ‎factor ‎is ‎introduced ‎to ‎adjust ‎the ‎ability ‎of ‎exploring ‎new ‎solutions ‎in ‎the ‎iterative ‎optimization ‎search ‎process. ‎Simulation ‎experiments ‎of ‎the ‎dynamic ‎adaptive ‎learning ‎algorithm ‎show ‎that ‎when ‎the ‎adaptive ‎scaling ‎factor ‎parameter ‎range ‎in ‎the ‎DSLTLBO ‎algorithm ‎is ‎set ‎to ‎λstart‎=0.3, ‎λend=3, ‎the ‎simulation ‎results ‎of ‎the ‎algorithm ‎converge ‎to ‎the ‎optimal ‎value ‎for ‎both ‎unconstrained ‎and ‎constrained ‎functions, ‎and ‎the ‎algorithm ‎has ‎better ‎accuracy ‎and ‎stability. ‎The ‎maximum ‎number ‎of ‎concurrent ‎users ‎that ‎the ‎self-study ‎examination ‎system ‎with ‎intelligent ‎assistant ‎module ‎and ‎dynamic ‎adaptive ‎learning ‎module ‎can ‎successfully ‎respond ‎to ‎is ‎100000, ‎which ‎is ‎in ‎line ‎with ‎the ‎design ‎expectation.