Data_Sheet_1_Evaluating transfer prediction using machine learning for skill acquisition study under various practice conditions.PDF

Recent research highlighted the interest in 1) investigating the effect of variable practice on the dynamics of learning and 2) modeling the dynamics of motor skill learning to enhance understanding of individual pathways learners. Such modeling has not been suitable for predicting future performance, both in terms of retention and transfer to new tasks. The present study attempted to quantify, by means of a machine learning algorithm, the prediction of skill transfer for three practice conditions in a climbing task: constant practice (without any modifications applied during learning), imposed variable practice (with graded contextual modifications, i.e., the variants of the climbing route), and self-controlled variable practice (participants were given some control over their variant practice schedule). The proposed pipeline allowed us to measure the fitness of the test to the dataset, i.e., the ability of the dataset to be predictive of the skill transfer test. Behavioral data are difficult to model with statistical learning and tend to be 1) scarce (too modest data sample in comparison with the machine learning standards) and 2) flawed (data tend to contain voids in measurements). Despite these adversities, we were nevertheless able to develop a machine learning pipeline for behavioral data. The main findings demonstrate that the level of learning transfer varies, according to the type of practice that the dynamics pertain: we found that the self-controlled condition is more predictive of generalization ability in learners than the constant condition.

近期的研究凸显了学界的两大研究热点：一是探究变量练习（variable practice）对学习动态的影响，二是构建运动技能学习的动态模型，以深化对学习者个体学习路径的理解。此类模型此前无法适用于预测未来运动表现，涵盖记忆保持表现与向新任务的技能迁移表现。本研究借助机器学习（machine learning）算法，针对攀岩任务中的三种练习模式量化其技能迁移的预测效果，三种练习模式分别为：固定练习（constant practice，即学习过程中无任何调整的练习）、强制变量练习（imposed variable practice，即带有分级情境调整的练习，也就是攀岩路线的变体方案）以及自主控制变量练习（self-controlled variable practice，即参与者可自主把控变量练习计划的练习模式）。本研究提出的机器学习流水线（machine learning pipeline）可用于衡量测试与数据集的拟合度，即数据集对技能迁移测试的预测能力。行为数据难以通过统计学习（statistical learning）进行建模，且往往存在两类问题：一是数据稀缺，相较于机器学习领域的通用标准，其样本规模过小；二是数据存在缺陷，测量数据往往存在缺失值。尽管面临这些挑战，本研究仍成功构建了适用于行为数据的机器学习流水线。主要研究结果表明，学习迁移水平随其所关联的练习类型而异：相较于固定练习模式，自主控制变量练习模式更能有效预测学习者的泛化能力。