Osman Yasar is an endowed professor and director of the CMST Institute at The College at Brockport, SUNY. He established the first undergraduate degree program in computational science in the United States and developed a computational pedagogical content knowledge (CPACK) framework for teacher professional development. His research interests include engineering and science education, computational pedagogy, computational theory of mind, fluid and particle dynamics, engine ignition modeling, and parallel computing. Yasar has a PhD in engineering physics and MS degrees in computer science and nuclear engineering from the University of Wisconsin-Madison. He also has BS and MS degrees in physics from Hacettepe University-Ankara. He co-founded a national supercomputer center and a doctoral program in computational science and engineering at Istanbul Technical University. Ibrahim Halil Yeter, Texas Tech UniversityIbrahim H. Yeter is currently a PhD candidate in the Curriculum and Instruction program at the College of Education, and at the same time, he is pursuing his Master's degree in Petroleum Engineering at Texas Tech University. He is highly interested in conducting research within the Engineering Education framework. Mr. Yeter plans to graduate in December 2016 with both degrees and is looking forward to securing a teaching position within a research university and continuing his in-depth research on Engineering Education.He is one of two scholarships awarded by NARST (National Association for Research in Science Teaching) to attend the ESERA (European Science Education Research Association) summer research conference inČeské Budějovice, Czech Republic in August 2016. In addition, he has been named as one of 14 Jhumki Basu Scholars by the NARST's Equity and Ethics Committee in 2014. He is the first and only individual from his native country and Texas Tech University to have received this prestigious award. Furthermore, he was a recipient of the Texas Tech University President's Excellence in Diversity & Equity award in 2014 and was the only graduate student to have received the award, which was granted based on outstanding activities and projects that contribute to a better understanding of equity and diversity issues within Engineering Education. Computational Pedagogy: Fostering a New Method of Teaching AbstractTeaching with technology still remains as a challenge. Making judicious choices of when, what and how specific tools and pedagogies to use in the teaching of a topic can be improved with the help of curriculum inventories, training, and practices but as new and more capable technologies arrive, such resources and experience do not often transfer to new circumstances. This article presents a case study in which computational modeling and simulation technology (CMST) is used to improve technological pedagogical content knowledge (TPACK) of teachers. We report findings of a summer training program for both preservice and in-service teachers in the Northeastern United States. CMST has shown to be effect...

Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for the performance-based assessment of the learning of concepts and practices regarding image classification with artificial neural networks in K-12. The assessment is based on the examination of student-created artifacts as a part of open-ended applications on the use stage of the Use-Modify-Create cycle. An initial evaluation of the scoring rubric through an expert panel demonstrates its internal consistency as well as its correctness and relevance. Providing a first step for the assessment of concepts on image recognition, the results may support the progress of learning ML by providing feedback to students and teachers.

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. This qualitative case study of a homeschool family is comprised of a mother and her six-year-old daughter. They engaged in two STEM+C activities designed by our research team. The parent first utilized the integrated STEM+C+literacy curriculum at home, and then visited a local science center. During their visit, both the parent and child interacted with an exhibit designed to promote engineering and computational thinking among children. Their engagement in both activities was video-and audio-recorded. Interviews regarding their experience were also conducted at the end of each activity (curriculum and exhibit participation). In this study, we employed a video analysis approach to examine childparent interactions and utilized a thematic analysis approach to analyze the interviews. Our findings suggest that homeschool parents are integral to supporting children's understanding of CT.

that given the tasks that children were given, the level of CT competencies they engaged in was different.What evidence of computational thinking is observed when first-grade children are engaged in an engineering design task in a science center? Do children make any connections across those learning environments? Theoretical Framework: Computational Thinking Competencies Computational thinking (CT) is a multifaceted construct that includes several cognitive processes. These processes have been defined and described by a variety of frameworks and models. These frameworks have some differences and similarities in the ways CT cognitive processes are called, categorized and defined.

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