Detecting Learner’s To-Be-Forgotten Items using Online Handwritten Data
Detecting Learner’s To-Be-Forgotten Items using Online Handwritten Data
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使用在线手写数据检测学习者的遗忘项目
DOI：
10.1145/2808047.2808049
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Hiroki Asai，Hayato Yamana
中科院分区：
文献类型：
--
作者：
Ikuko Sasaba;Sean J. Fitzpatrick;Alison Pope-Rhodius; Haruo Sakuma;Hiroki Asai，Hayato Yamana
对于生命有限的人来说，有效的学习系统是必不可少的。传统的学习系统根据回忆测试的结果和间隔效应等学习理论来安排重复。然而，从记忆水平估计的角度来看，还有改进的余地。在本文中，我们关注的是在线手写数据的获取。在分析传统估计方法存在的问题的同时，利用手写数据作为输入数据，建立了一种新的估计模型。评估发现，我们提出的模型可以输出0到1的连续记忆水平值，而传统方法只能输出二进制决策。此外，实验表明，我们提出的模型达到了最佳性能，f值为0.69。
An effective learning system is indispensable for human beings with a limited life span. Traditional learning systems schedule repetition based on both the results of a recall test and learning theories such as the spacing effect. However, there is room for improvement from the perspective of remembrance-level estimation. In this paper, we focus on on-line handwritten data obtained from handwriting using a computer. We collected handwritten data from remembrance tests to both analyze the problem of traditional estimation methods and to build a new estimation model using handwritten data as the input data. The evaluation found that our proposed model can output a continuous remembrance-level value of zero to 1, whereas traditional methods output a only binary decision. In addition, the experiment showed that our proposed model achieves the best performance with an F-value of 0.69.