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  1. Blikstein, P ; Van_Aalst, J ; Kizito, R ; Brennan, K (Ed.)
    Developing assessment tools for computational thinking (CT) in STEM education is a precursor for science teachers to effectively integrate intervention strategies for CT practices. One problem to assessing CT skills is students’ varying familiarity with different programming languages and platforms. A text-neutral, open-source platform called iFlow, is capable of addressing this issue. Specifically, this innovative technology has been adopted to elicit underrepresented undergraduate students’ debugging skills. We present how the visual-based coding platform can be applied to bypass programming language bias in assessing CT. In this preliminary study, we discuss design principles of a visual-based platform to effectively assess debugging practices – identification, isolation, and iteration – with the use of iFlow assignments. Our findings suggest how the ability of iFlow to test parts of a program independently, dataflow connectivity, and equity in removing biases from students’ various backgrounds are advantageous over text-based platforms. 
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  2. A rubric is presented to assess debugging skills for students particularly in the natural sciences and engineering. The three categories that are assessed for the cognitive processes in debugging skills are identification, isolation, and iteration. These are defined, and the characteristics of each process are listed. We discuss the method used to develop this rubric that was based on intentional errors in a programming assignment given to students in an introductory physics course. The programming in this assignment was in Python and a visual-based programming platform, called iFlow. We believe that visual-based programming will help elicit weaknesses in debugging because it removes students' familiarity with particular programming languages. Our focus on debugging skills came from a survey of students to self-identify barriers in computational work in an introductory physics course that included engineering majors. This skill was the primary self-identified barrier along with abstraction skills, which will be the focus of another work. We also present the results of this survey. The Python assignment (ntext = 9) was used to create the rubric and the iFlow assignment (ngraphic = 11) was used to test the rubric. Scoring was based on a scale of six levels in each category. Although the sample size was too small to establish rigorous scoring reliability, we discussed how the two researchers attained agreement in scoring the assignments after iterative modifications of the rubric and rescoring. For the Python assignment, the average for identification was 2.75/5, for isolation 2.30/5, and for iteration 3.33/5. For the iFlow assignment, the average for identification was 2.63/5, for isolation 2.23/5, and for iterate 3.32/5. A consistent trend from these assignments showed that students' approach to debugging is mainly to identify and iterate without a full understanding of the error (i.e., isolation). The lack of a full understanding of the error implies that students are prone to repeat the error. Thus, the important outcome of debugging is to understand the source of error by systematically investigating different parts of the computational solution. Our preliminary results led to the hypothesis that students with weak debugging skills are mainly due the isolation process. This hypothesis will be tested in a future experiment. Results from such an experiment will be significant to those who are designing intervention strategies to integrate computational thinking in science and engineering curricula. 
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  3. null (Ed.)
    The integration of biology with mathematics and computer science mandates the training of students capable of comfortably navigating among these fields. We address this formidable pedagogical challenge with the creation of transdisciplinary modules that guide students toward solving realistic problems with methods from different disciplines. Knowledge is gradually integrated as the same topic is revisited in biology, mathematics, and computer science courses. We illustrate this process with a module on the homeostasis and dynamic regulation of red blood cell production, which was first implemented in an introductory biology course and will be revisited in the mathematics and computer science curricula. 
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