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We propose new ensemble models for multivariate functional dataclassification as combinations of semi-metric-based weak learners. Our modelsextend current semi-metric-type methods from the univariate to the multivariatecase, propose new semi-metrics to compute distances between functions, andconsider more flexible options for combining weak learners using stackedgeneralisation methods. We apply these ensemble models to identify respondents'difficulty with survey questions, with the aim to improve survey data quality.As predictors of difficulty, we use mouse movement trajectories from therespondents' interaction with a web survey, in which several questions weremanipulated to create two scenarios with different levels of difficulty.

Classification ensembles for multivariate functional data with application to mouse movements in web surveys