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首页> 外文期刊>Frontiers in Psychology >Proof of Concept of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children
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Proof of Concept of a Gamified DEvelopmental Assessment on an E-Platform (DEEP) Tool to Measure Cognitive Development in Rural Indian Preschool Children

机译：关于电子平台（深）工具的娱乐发展评估概念证明，以衡量农村幼儿园的认知发展

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Over 250 million children in developing countries are at risk of not achieving their developmental potential, and unlikely to receive timely interventions because existing developmental assessments that help identify children who are faltering are prohibitive for use in low resource contexts. To bridge this “detection gap,” we developed a tablet-based, gamified cognitive assessment tool named DEvelopmental assessment on an E-Platform (DEEP), which is feasible for delivery by non-specialists in rural Indian households and acceptable to all end-users. Here we provide proof-of-concept of using a supervised machine learning (ML) approach benchmarked to the Bayley’s Scale of Infant and Toddler Development, 3rd Edition (BSID-III) cognitive scale, to predict a child’s cognitive development using metrics derived from gameplay on DEEP. Two-hundred children aged 34–40 months recruited from rural Haryana, India were concurrently assessed using DEEP and BSID-III. Seventy percent of the sample was used for training the ML algorithms using a 10-fold cross validation approach and ensemble modeling, while 30% was assigned to the “test” dataset to evaluate the algorithm’s accuracy on novel data. Of the 522 features that computationally described children’s performance on DEEP, 31 features which together represented all nine games of DEEP were selected in the final model. The predicted DEEP scores were in good agreement (ICC [2,1] 0.6) and positively correlated (Pearson’s r = 0.67) with BSID-cognitive scores, and model performance metrics were highly comparable between the training and test datasets. Importantly, the mean absolute prediction error was less than three points (10% error) on a possible range of 31 points on the BSID-cognitive scale in both the training and test datasets. Leveraging the power of ML which allows iterative improvements as more diverse data become available for training, DEEP, pending further validation, holds promise to serve as an acceptable and feasible cognitive assessment tool to bridge the detection gap and support optimum child development.
机译：超过2.5亿个发展中国家的儿童有可能没有实现其发展潜力的风险，并且不太可能及时接受干预措施，因为有助于识别在低资源上下文中持久使用的儿童的现有发展评估是禁止使用的。为了弥合这种“检测差距”，我们开发了一种基于片剂的游戏认知评估工具，名为E-Platform（深）的发育评估，这是由农村印度家庭的非专家提供的可行的，并且所有最终都可以接受用户。在这里，我们提供使用监督机器学习（ML）方法的概念证明，该方法基准测试到拜访婴儿和幼儿的发展，第三版（BSID-III）认知规模，使用来自游戏的指标预测儿童的认知发展深。从农村哈里亚纳邦招募了34-40个月的24-40个月，并使用深入和BSID-III同时评估。使用10倍的交叉验证方法和集合建模，将百分之七十分样本用于训练ML算法，而集合建模则分配给“测试”数据集以评估算法对新型数据的准确性。在522个功能中，在最终模型中选择了31个在深度的深度，31个功能中所选择的31个功能。预测的深度分数良好（ICC [2,1]> 0.6），并带有BSID认知评分的正相关（Pearson的r = 0.67），训练和测试数据集之间的模型性能指标非常可比。重要的是，平均绝对预测误差小于训练和测试数据集中的BSID认知量表的可能范围为31个点（误差）。利用ML的强大力量，这使得迭代改进随着越来越多的数据可供培训，深入，等待进一步验证，承担承诺作为弥合检测差距并支持最佳儿童发展的可接受和可行的认知评估工具。

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