Learners always have their learning style preferences according to their different cognitive processes. Automatically modeling the learners' learning style can get the more accurate information compared with the questionnaires which is free from the problem of inaccurate self-conceptions of the learners. There are many problems in the current LS detecting methods,like only can detect the LS dimensions in a specific model, can not adaptively adjust the LS preferences in a different learning environment. We provided a new method to forecast the learners' LSs which is called LS-Pre. In LS-Pre,non-linear dynamic programming is used to construct the mathematic model while simulated annealing algorithm is used to optimize the goal function. We illustrated the effectiveness of LS-Pre in part 4 of this paper.%学习者根据其不同的认知过程通常可以分为不同类型的学习风格,而自动获取学习者学习风格的方式相较问卷来说可以得到更为准确的信息.现有的学习风格自动识别手段都有无法跨学习风格模型进行预测以及当学习环境发生改变时无法自适应动态调整等问题.提出了一种利用学习者学习行为表象来预测学习者学习风格的方法——LS-Pre.LS-Pre使用非线性动态规划法构建预测学习风格的数学模型并使用模拟退火算法优化目标函数.通过LSPre预测的学习风格不仅包括在环境中可以通过具体行为观测到的维度,还包括那些无法观测到的以及跨模型的学习风格维度.实验验证了该方法的有效性.
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