August 21, 2019
Name
Wei Yan
LTEC Role
Graduate Research Assistant @ the University of Florida.
Professional Background
A 2nd-year Ph.D. student in Educational Technology at the University of Florida; I got my BA in Elementary Education (Mathematics and Science) from China and MA in curriculum and instruction (Elementary Education) from UF. My research interest is integrating technology into the elementary curriculum, especially in math and science.
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August 14, 2019
Name
Feiya Luo
LTEC Role
Doctoral Research Assistant @ the University of Florida.
Professional Background
I am currently a 4th-year doctoral candidate in Educational Technology at the University of Florida. I received my master’s degree in Translation and Interpretation from the Middlebury Institute of International Studies at Monterey in California. I also completed a certificate program in computer programming at Mission College, Santa Clara. A teacher (with many years of experience in K-12 and undergraduate classrooms both in the U.S. and China), translator and interpreter, and website developer, I have gained from various roles experience that has empowered me to pursue my interests in educational technology. Prior to joining LTEC, I built an educational technology that aimed to support children’s interests in botany as well as to provide college students with a hub for information and learning activities in botany. I also ran a research study that involved elementary students in computational thinking-integrated botany using robotics. My current research interests are computational thinking assessment and understanding students’ CT/CS learning progression K-8.
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August 07, 2019
Name: John Hampton
LTEC Role: Doctoral student (3rd) year in Educational Technology @ the University of Florida; graduate assistant.
Professional Background: I taught high school English for 10 years, from remedial reading to college-level literature and linguistics. I also taught photojournalism for several years and was in charge of the yearbook program.
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July 17, 2019
Author: Feiya Luo
Contributors (names in alphabetical order): John Hampton, Maya Israel, Todd Lash, Ruohan Liu, Michael McKelvey, Wei Yan.
This Spring semester, we piloted a series of Action Fractions lessons with 4th-grade students. During the lessons, students built projects using computational thinking (CT) skills such as decomposition, sequencing, and repetition to solve math projects focused on fractions. As part of the effort to understand students’ thinking during problem solving, we conducted “cognitive interviews,” also known as the think-aloud protocol (TAP), to solicit students’ immediate thoughts and reactions during problem solving. In our case, the purposes were to a) have students verbalize their thinking as they solved the CT+fractions problems, b) identify learning gaps and pitfalls in regard to CT understanding, and c) help us validate the assessment items–i.e. to see if there is any misalignment between students’ actual problem-solving and the item developers’ intention.
The assessment items used in the cognitive interviews were selected from the CT assessments designed to assess student learning after they go through the Action Fractions lessons. To ensure meaningful data collection, the items included multiple-choice, open-ended, and fill-in-the-blank questions that covered all important CT concepts present in the lessons. From a data collection perspective, no True or False questions were selected, since they would not provide as much rich data compared to the other question formats used.
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