Latino Parents\u27 Motivations for Involvement in Their Children\u27s Schooling: An Exploratory Study

This study examines the ability of a theoretical model of the parental involvement process to predict Latino parents\u27 involvement in their children\u27s schooling. A sample of Latino parents (N = 147) of grade 1 through 6 children in a large urban public school district in the southeastern United States responded to surveys assessing model-based predictors of involvement (personal psychological beliefs, contextual motivators of involvement, perceived life-context variables), as well as levels of home- and school-based involvement. Home-based involvement was predicted by partnership-focused role construction (a personal psychological belief) and by specific invitations from the student (a contextual motivator of involvement). School-based involvement was predicted by specific invitations from the teacher (a contextual motivator) and by perceptions of time and energy for involvement (a life-context variable). Results are discussed with reference to research on Latino parents\u27 involvemen

Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset

Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing. Traditional approaches frequently require hand-crafted speech features and segmentation of speaker turns. In this work, we design end-to-end deep learning methods to recognize each person's affective expression in an audio stream with two speakers, automatically discovering features and time regions relevant to the target speaker's affect. We integrate a local attention mechanism into the end-to-end architecture and compare the performance of three attention implementations -- one mean pooling and two weighted pooling methods. Our results show that the proposed weighted-pooling attention solutions are able to learn to focus on the regions containing target speaker's affective information and successfully extract the individual's valence and arousal intensity. Here we introduce and use a "dyadic affect in multimodal interaction - parent to child" (DAMI-P2C) dataset collected in a study of 34 families, where a parent and a child (3-7 years old) engage in reading storybooks together. In contrast to existing public datasets for affect recognition, each instance for both speakers in the DAMI-P2C dataset is annotated for the perceived affect by three labelers. To encourage more research on the challenging task of multi-speaker affect sensing, we make the annotated DAMI-P2C dataset publicly available, including acoustic features of the dyads' raw audios, affect annotations, and a diverse set of developmental, social, and demographic profiles of each dyad.Comment: Accepted by the 2020 International Conference on Multimodal Interaction (ICMI'20