Crafting Engaging Science Environments is a high school chemistry and physics project-based learning intervention that meets Next Generation Science Standards performance expectations. It was administered to a diverse group of over 4,000 students in a randomized control trial in California and Michigan. Results show that treatment students, on average, performed 0.20 standard deviations higher than control students on an independently developed summative science assessment. Mediation analyses show an indirect path between teacher- and student-reported participation in modeling practices and science achievement. Exploratory analyses indicate positive treatment effects for enhancing college ambitions. Overall, results show that improving secondary school science learning is achievable with a coherent system comprising teacher and student learning experiences, professional learning, and formative unit assessments that support students in “doing” science.
This investigation studied the effects of the Multiple Literacies in Project-Based Learning science intervention on third graders’ academic, social, and emotional learning. This intervention includes four science units and materials, professional learning, and post-unit assessments; features of project-based learning; three-dimensional learning (National Research Council, 2012); and the performance expectations from the Next Generation of Science Standards (NGSS Lead States, 2013). The intervention was evaluated with a cluster randomized control trial in 46 Michigan schools with 2,371 students. Results show that students who received the intervention had higher scores on a standardized science test (0.277 standard deviation) and reported higher levels of self-reflection and collaboration when involved in science activities.
For nearly a decade, two science interventions anchored in project-based learning (PBL) principles have been shown to increase student science learning in 3rd grade and high school physical science classes. Both interventions employed a randomized control trial of several thousand students (N = 3,271 in 3rd grade and N = 4,238 in 10th, 11th, and 12th grades). Incorporating a rich background of research studies and reports, the two interventions are based on the ideas of PBL as well as the National Academies of Science’s publications, including how children learn; how science learning and instruction can be transformed; and the performance expectations for science learning articulated in the Next Generation of Science Standards. Results show significant positive increases in student academic, social, and emotional learning in both elementary and secondary school. These findings can be traced, in part, to carefully crafted experiential participatory activities and high-quality instructional materials which act as strong facilitators for knowledge acquisition and use. Reviewing the innovations undertaken by these two interventions, this article describes the importance of studying social and emotional factors ‘in situ’, using the Experience Sampling Method (ESM), that can motivate and engage students in science learning in both elementary and secondary school. Using these ‘in situ’ data collection (N = 596 students in 3rd and N = 1412 students in 10th, 11th, and 12th grades) along with case studies and repeated measures analysis gave deep insights into emotional and social development for young children and adolescents. These methods should continue to be considered when trying to understand key factors of improving engagement in science.
As AI and machine learning permeates every area of life, its use to ameliorate educational inequities becomes of great interest. One important application of machine learning within education is to help students increase their alignment of career choice, educational attainment, and projected salary. Alignment theory has shown that having alignment yields higher educational attainment for students. Using the app, Init2Winit, which has students play a game which gives them points for correct alignment, this chapter explores how machine learning, in particular using a decision tree, can give insights into game use and its relation to educational expectations. This model builds a basis for the improvement of Init2Winit to increase student educational expectations through counselor interventions and how other educational applications could use machine learning for insights to improve educational outcomes. The model can decrease educational inequities by increasing educational attainment for those in underrepresented minorities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.