Department of Information Systems and Technology, IE Business School, IE University, Madrid, Spain.
Khoury College of Computer Sciences, Northeastern University at Silicon Valley, San Jose, CA, United States of America.
PLoS One. 2020 Dec 2;15(12):e0241687. doi: 10.1371/journal.pone.0241687. eCollection 2020.
Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and a predictive machine learning model. In a study with more than 3,600 participants, our model correctly detected over 80% of the participants with dyslexia. To check the robustness of the method we tested our method using a new data set with over 1,300 participants with age customized tests in a different environment -a tablet instead of a desktop computer- reaching a recall of over 78% for the class with dyslexia for children 12 years old or older. Our work shows that dyslexia can be screened using a machine learning approach. An online screening tool in Spanish based on our methods has already been used by more than 200,000 people.
阅读障碍是一种与学业失败相关的特定学习障碍。检测既关键又具有挑战性，尤其是在像西班牙语这样的拼写规则透明的语言中。为了更轻松地检测阅读障碍，我们设计了一个在线游戏化测试和一个预测机器学习模型。在一项涉及 3600 多名参与者的研究中，我们的模型正确检测出了 80%以上的阅读障碍者。为了检查该方法的稳健性，我们使用了一个新的数据集进行了测试，该数据集包含 1300 多名参与者，他们在不同的环境中使用年龄定制的测试（平板电脑而非台式计算机），对于 12 岁或以上的儿童阅读障碍组，召回率超过 78%。我们的工作表明，可以使用机器学习方法来筛查阅读障碍。一个基于我们方法的西班牙语在线筛查工具已经被 20 多万人使用。
