This paper presents synthesized research on where XR is most effective within a museum setting and what impact XR might have on the visitor experience.
The data collection procedure and process is one of the most critical components in a research study that affects the findings. Problems in data collection may directly influence the findings, and consequently, may lead to questionable inferences. Despite the challenges in data collection, this study provides insights for STEM education researchers and practitioners on effective data collection, in order to ensure that the data is useful for answering questions posed by research. Our engineering education research study was a part of a three-year, NSF funded project implemented in the Midwest
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TEAM MEMBERS:
Ibrahim YeterAnastasia Marie RynearsonHoda EhsanAnnwesa DasguptaBarbara FagundesMuhsin MeneskeMonica Cardella
Integrating science, technology, engineering, and mathematics (STEM) subjects in pre-college settings is seen as critical in providing opportunities for children to develop knowledge, skills, and interests in these subjects and the associated critical thinking skills. More recently computational thinking (CT) has been called out as an equally important topic to emphasize among pre-college students. The authors of this paper began an integrated STEM+CT project three years ago to explore integrating these subjects through a science center exhibit and a curriculum for 5-8 year old students. We
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TEAM MEMBERS:
Morgan HynesMonica CardellaTamara MooreSean BrophySenay PurzerKristina TankMuhsin MeneskeIbrahim YeterHoda Ehsan
Computational Thinking (CT) is an often overlooked, but important, aspect of engineering thinking. This connection can be seen in Wing’s definition of CT, which includes a combination of mathematical and engineering thinking required to solve problems. While previous studies have shown that children are capable of engaging in multiple CT competencies, research has yet to explore the role that parents play in promoting these competencies in their children. In this study, we are taking a unique approach by investigating the role that a homeschool mother played in her child’s engagement in CT
Given the growth of technology in the 21st century and the growing demands for computer science skills, computational thinking has been increasingly included in K-12 STEM (Science, Technology, Engineering and Mathematics) education. Computational thinking (CT) is relevant to integrated STEM and has many common practices with other STEM disciplines. Previous studies have shown synergies between CT and engineering learning. In addition, many researchers believe that the more children are exposed to CT learning experiences, the stronger their programming abilities will be. As programming is a
For the past two decades, researchers and educators have been interested in integrating engineering into K-12 learning experiences. More recently, computational thinking (CT) has gained increased attention in K-12 engineering education. Computational thinking is broader than programming and coding. Some describe computational thinking as crucial to engineering problem solving and critical to engineering habits of mind like systems thinking. However, few studies have explored how computational thinking is exhibited by children, and CT competencies for children have not been consistently defined
Informal learning environments such as science centers and museums are instrumental in the promotion of science, technology, engineering, and mathematics (STEM) education. These settings provide children with the chance to engage in self-directed activities that can create a of lifelong interest and persistence in STEM. On the other hand, the presence of parents in these settings allows children the opportunity to work together and engage in conversations that can boost understanding and enhance learning of STEM topics. To date, a considerable amount of research has focused on adult-child
This guide describes what took place during NYSCI’s Big Data for Little Kids workshop series, Museum Makers: Designing With Data. In addition to detailed outlines of the activities implemented during the program, this guide includes a glossary of recurrent terms and resources used throughout the workshops.
In 2017, as part of a National Science Foundation funded project, the New York Hall of Science (NYSCI) set out to teach Big Data concepts to children ages 4 – 8 years old. NYSCI developed and piloted an after-school program for families to utilize the data cycle as a method of informed
Computational social science represents an interdisciplinary approach to the study of reality based on advanced computer tools. From economics to political science, from journalism to sociology, digital approaches and techniques for the analysis and management of large quantities of data have now been adopted in several disciplines. The papers in this JCOM commentary focus on the use of such approaches and techniques in the research on science communication. As the papers point out, the most significant advantages of a computational approach in this sector include the chance to open up a range
In 2013 and 2014, the Museum of Science (MOS) partnered with Dr. Rob Wood’s lab at Harvard University’s School of Engineering and Applied Sciences (SEAS) to create an exhibition about Wood’s Robotic Bees (RoboBees) project. The Microrobotics Takes Flight exhibition (referred to in the original grant as the RoboBees exhibition) consists of three interactive components and an introductory section. The three interactive components are modeled on the three different engineering teams working on the RoboBees project: the Brain, the Body, and the Colony teams. The purpose of the evaluation was
To better help museum visitors make sense of large data sets, also called “Big Data”, this study focused on the types of visual representations visitors recognize, and how they make meaning (or not) of various visuals. Individual adults and youths were shown five different data visualizations (one from each of five categories), one at a time, and asked if the visualization looked familiar and how it was read. This study found that Context and previous experience matters. Participants of all ages are familiar with a wide variety of visual displays of data. If a participant encounters a visual
To better help museum visitors make sense of large data sets, also called “big data”, this study investigated if there were generalizable ways in which visitors engage with and then make meaning of such data sets. This front-end study was designed to explore if there were different, distinct, and repeatable patterns intuited by individuals as they work with large data sets. This was a descriptive, process method using a complex card sort with an interview. Each card had the name of one food item written on it. Food items were diverse, including eggs, crackers, lasagna, apples, tofu and almonds