Using Technology to Support Progress Monitoring and Data-Based Intervention Decision Making in Early Childhood: Is There an App for That?

Multi-Objective Heuristic Decision Making and Benchmarking for Mobile Applications in English Language Learning

This research proposes to evaluate and analyze the decision matrix for learner's English mobile applications (EMAs) based on multi-objective heuristic decision making with a view to listening, speaking, reading, and writing. Because of the number of criteria, the significance of parameters, and variance in results, EMAs are difficult. Decision making has built on the combination of listening, speaking, reading, and writing and EMA evaluation criteria for students. The requirements are adapted from a framework of pre-school education. Six alternatives and 17 skills as a requirement are included in decision-making results. The six EMA are then assessed, with six English learning experts distributing a review form. The application subsequently is evaluated using the best-worst method and preference-order technique (TOPSIS) using multi-objective heuristic decision making methods. The best-worst method is used to measure requirements, whereas TOPSIS is used to test and assess the applications. In two cases, namely person and group, TOPSIS is used. Internal and external aggregations are used throughout the group context. In effect, the aim of evaluating the proposed study and comparing it to six relative studies with scenarios and benchmarking checklists is to develop an objectives validation framework for e-apps.

Improving Access to Trauma-Informed Adoption Services: Applying a Developmental Trauma Framework

Foster and adopted children often experience multiple traumatic and adverse experiences. A growing body of literature indicates the negative impact of trauma on developmental milestones and brain development, which supports the need to address complex trauma among this vulnerable population of foster and adopted youth. This paper presents an overview of the unique needs of children adopted from the foster care system from the perspective of adverse childhood experiences (ACEs), complex trauma, and developmental trauma disorder (DTD). There is an increasing number of evidence-based trauma-focused services and interventions for children and youth. However, many adoptive parents have limited trauma-informed training and limited access to trauma-informed and adoption-competent professionals, particularly long-term supports across developmental stages, making them ill-prepared to meet the needs of children in their care. This paper contributes to the understanding of how access to these trauma-focused services can be increased through new technologies, to better prepare and empower adoptive parents to deal effectively with difficult adoption issues when they arise and to improve outcomes for children and youth adopted from the public child welfare system. Several innovative approaches toward this end include harnessing technology to: (1) improve access to suitable adoption resources, (2) improve mechanisms to track critical events, behaviors, emotions, functional abilities, strengths, etc., in order to determine timely, on-demand contextual services, and (3) extend professional, supportive environments beyond the adoptive family context by proposing the use of technology to build interdisciplinary, virtual community partners.

An automated approach to measuring child movement and location in the early childhood classroom

Children's movement is an important issue in child development and outcome in early childhood research, intervention, and practice. Digital sensor technologies offer improvements in naturalistic movement measurement and analysis. We conducted validity and feasibility testing of a real-time, indoor mapping and location system (Ubisense, Inc.) within a preschool classroom. Real-time indoor mapping has several implications with respect to efficiently and conveniently: (a) determining the activity areas where children are spending the most and least time per day (e.g., music); and (b) mapping a focal child's atypical real-time movements (e.g., lapping behavior). We calibrated the accuracy of Ubisense point-by-point location estimates (i.e., X and Y coordinates) against laser rangefinder measurements using several stationary points and atypical movement patterns as reference standards. Our results indicate that activity areas occupied and atypical movement patterns could be plotted with an accuracy of 30.48 cm (1 ft) using a Ubisense transponder tag attached to the participating child's shirt. The accuracy parallels findings of other researchers employing Ubisense to study atypical movement patterns in individuals at risk for dementia in an assisted living facility. The feasibility of Ubisense was tested in an approximately 90-min assessment of two children, one typically developing and one with Down syndrome, during natural classroom activities, and the results proved positive. Implications for employing Ubisense in early childhood classrooms as a data-based decision-making tool to support children's development and its potential integration with other wearable sensor technologies are discussed.