In recent years there has been a resurgence of descriptive research that seeks to document, describe, and analyze the conditions under which infants and children learn in a variety of natural settings. Descriptive developmental research enhances fundamental understanding and facilitates the generation and refinement of comprehensive theories of early learning and development. Incorporating open science practices into descriptive research increases the transparency, reliability, and replicability of this work. However, many existing open-science practices have been oriented toward experimental rather than descriptive studies and thus translate less directly to work that is more descriptive in nature. We discuss a number of unique considerations for incorporating open-science practices into descriptive developmental research, providing examples from existing and ongoing studies and suggestions for how descriptive research and open science can coexist productively. Our overarching goal is to provide a resource for anyone who intends to do open, descriptive, developmental research.
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
Latest posts by Ryan Watkins (see all)
- Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents - December 1, 2023
- AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction [imputation] - November 29, 2023
- Enhancing Human Persuasion With Large Language Models - November 29, 2023