The Research on Soldier’s Personalized Learning Based on improved Ant Colony Algorithm
Dong Li, Huqiang Wang · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · 2015
Confronting the situation of uneven educational background, knowledge comprehension and master speed of soldiers, stereotype education system of soldier's occupational skill no longer adapts the demand of network era development and informational military construction.The paper puts forward to the tactic of personalized learning of soldiers after analyzing the existing problems in soldier's occupational skill education and the shortcomings of traditional ant colony algorithm, the tactic which makes use of Max-Min ant system just up-dates the pheromones of the most optimal path the ant has went through, and the pheromones of each path are strictly limited.The algorithm will build related mathematical model and optimal strategy to find a personalized learning scheme or path that adapts to themselves according to the cognitive ability, self-learning style and knowledge level of soldiers.Then it will be provided to the soldiers so as to reach the purpose of improving their learning efficiency.The experiment result shows that the improved ant colony algorithm owns higher convergence, and the degree of coincidence between personalized learning path and the soldier's demand is greatly improved.