Contribution: The purpose of this study is: 1) to identify students' early misconceptions in problem understanding and the use of variables/sequencing in a first-year computer programming course using Python; 2) to quantify how early misconceptions evolve over time; and 3) to characterize how misconceptions in the use of variables/sequencing affect learning more advanced computer programming concepts. Background: It has not been clearly established how early misconceptions evolve over time and how they affect learning later in the semester. Research Questions: 1) What are the most common misconceptions in the use of variables/sequencing in a first-year Python course? 2) do those early misconceptions persist late in the semester? and 3) does the persistence of misconceptions in variable/sequencing hinder progress in learning more advanced computer programming concepts? Methodology: We assessed student performance ( n =116 ) in multiple choice and short-answer questions that involved reading and/or writing small flowcharts or snippets of Python code. Findings: Problem understanding remains a challenge for similar to 45% of students until the end of the semester. Misconceptions in variable assignment/sequencing persist late in the semester for similar to 20% of students, hindering their progress in learning more advanced computer programming concepts.

Dispersion of repolarization results from a non-homogeneous recovery of excitability in cardiac tissue, and it is an important factor in arrhythmogenesis because it could lead to the initiation and maintenance of a variety of arrhythmias. Antiarrhythmic agents that prolong action potential duration (APD) by selectively blocking specific ion channels (like IKr) often increase dispersion of repolarization, which could result in a pro-arrhythmic risk. In this report, using computer models of the action potential of human epicardial, mid-myocardial, and endocardial myocytes, we have identified strategies to prolong APD without increasing transmural dispersion of repolarization. The first strategy, which involves blocking several depolarizing and repolarizing ion channels (INaL, ICaL, IKr, and INaCa), can prolong APD while decreasing transmural APD dispersion by about 20%-60%, depending on the model. The second strategy, which involves the use of a combination of ion channel blockers and activators, can prolong APD while decreasing transmural APD dispersion by about 70%, a stronger reduction in transmural dispersion of repolarization than using only ion channel blockers. Our results suggest that a multichannel pharmacology strategy (as opposed to a single channel strategy), possibly using ion channel blockers and activators, can be effective at increasing APD while minimizing dispersion of repolarization.

Dispersion of repolarization results from a non-homogeneous recovery of excitability in cardiac tissue, and it is an important factor in arrhythmogenesis because it could lead to the initiation and maintenance of a variety of arrhythmias. Antiarrhythmic agents that prolong APD by selectively blocking specific ion channels (like IKr) often increase dispersion of repolarization, which could result in a pro-arrhythmic risk. In this report, using computer models of the action potential of human epicardial and mid-myocardial myocytes, we have identified two strategies to prolong APD while reducing transmural dispersion of repolarization. The first strategy, which involves blocking several depolarizing and repolarizing ion channels (INaL, ICaL, IKr and INaCa), can reduce the transmural APD dispersion by about 20%. The second strategy, which involves the use of a combination of ion channel blockers and activators, results in a stronger reduction in transmural dispersion of repolarization than using only ion channel blockers. Enhancing IKs and blocking IKr can reduce transmural APD dispersion by about 70%. Our results suggest that a multichannel pharmacology strategy (as opposed to a single channel strategy), possibly using ion channel blockers and activators, can be effective at increasing APD while minimizing dispersion of repolarization. ### Competing Interest Statement The authors have declared no competing interest. Professional Staff Congress, https://ror.org/050dp5763, # 67024-00 55

Prolongation of the action potential duration (APD) could prevent reentrant arrhythmias if prolongation occurs at the fast excitation rates of tachycardia with minimal prolongation at slow excitation rates (i.e., if prolongation is positive rate-dependent). APD prolongation by current anti-arrhythmic agents is either reverse (larger APD prolongation at slow rates than at fast rates) or neutral (similar APD prolongation at slow and fast rates), which may not result in an effective anti-arrhythmic action. In this report we show that, in computer models of the human ventricular action potential, the combined modulation of both depolarizing and repolarizing ion currents results in a stronger positive rate-dependent APD prolongation than modulation of repolarizing potassium currents. A robust positive rate-dependent APD prolongation correlates with an acceleration of phase 2 repolarization and a deceleration of phase 3 repolarization, which leads to a triangulation of the action potential. A positive rate-dependent APD prolongation decreases the repolarization reserve with respect to control, which can be managed by interventions that prolong APD at fast excitation rates and shorten APD at slow excitation rates. For both computer models of the action potential, I-CaL and I-K1 are the most important ion currents to achieve a positive rate-dependent APD prolongation. In conclusion, multichannel modulation of depolarizing and repolarizing ion currents, with ion channel activators and blockers, results in a robust APD prolongation at fast excitation rates, which should be anti-arrhythmic, while minimizing APD prolongation at slow heart rates, which should reduce pro-arrhythmic risks.

The purpose of this research-to-practice work-in-progress paper is to help students formalize the development and documentation of problem-solving strategies for computer programming, before attempting code writing, in a first course on Java programming. This approach could be beneficial: 1) for instructors to gain insight on students' problem-solving mental processes and to improve the evaluation of students' programming skills; 2) for students to develop an awareness of the process of developing problem-solving strategies, so they can reflect on their progress through the process of writing computer programs. We have adapted general problem-solving strategies developed in different areas of engineering and computer science to teaching computer programming. The process of developing and documenting computer programming strategies includes the following steps: 1) understanding the problem; 2) identifying/recalling similar problems; 3) developing strategies to solve the problem; 4) implementing a prototype as a computer program.

The purpose of this research-to-practice work-in-progress paper is to identify students' early misconceptions in the use of variables and assignment operators in a first-year computer programming course using Python. About half of first-year students taking computer programming courses have difficulty understanding the use of variables and the assignment operator. The majority of those students will not make adequate progress to learning advanced control flow structures like selection, repetition or the use of functions. To correct early misconceptions and make adequate progress in learning computer programming students should be able to: 1) translate a word problem into input/output requirements; 2) recognize the correct syntax of statements using variables and the assignment operator; 3) develop mental models of how the assignment operator works; 4) identify and interpret data types; 5) evaluate arithmetic expressions. We expect that early focus on achieving those learning objectives will prevent or correct early misconceptions and facilitate progress in learning computer programming.

Pharmacological agents that prolong action potential duration (APD) to a larger extent at slow rates than at the fast excitation rates typical of ventricular tachycardia exhibit reverse rate dependence. Reverse rate dependence has been linked to the lack of efficacy of class III agents at preventing arrhythmias because the doses required to have an antiarrhythmic effect at fast rates may have pro-arrhythmic effects at slow rates due to an excessive APD prolongation. In this report, we show that, in computer models of the ventricular action potential, APD prolongation by accelerating phase 2 repolarization (by increasing IKs ) and decelerating phase 3 repolarization (by blocking IKr and IK1 ) results in a robust positive rate dependence (i.e., larger APD prolongation at fast rates than at slow rates). In contrast, APD prolongation by blocking a specific potassium channel type results in reverse rate dependence or a moderate positive rate dependence. Interventions that result in a strong positive rate dependence tend to decrease the repolarization reserve because they require substantial IK1 block. However, limiting IK1 block to ~50% results in a strong positive rate dependence with moderate decrease in repolarization reserve. In conclusion, the use of a combination of IKs activators and IKr and IK1 blockers could result in APD prolongation that potentially maximizes antiarrhythmic effects (by maximizing APD prolongation at fast excitation rates) and minimizes pro-arrhythmic effects (by minimizing APD prolongation at slow excitation rates).

In this research to practice full paper we quantified whether student progress in learning computer programming concepts in a Java course is consistent with the Matthew effect, that is, if early success (or failure) in the acquisition of concepts/skills begets later success (or failure) in the acquisition of more concepts/skills. We found that 63% of students had difficulty understanding basic programs involving the assignment operator and a sequence of statements. The inability of students to understand assignment and sequencing proved to be a substantial obstacle to student progress in learning more advanced flow control and data structures concepts like selection, repetition loops and arrays. About 66% of students who succeeded in assignment/sequencing also succeeded in selection structures. On the other hand, about 34% of the students who did not succeed in assignment/sequencing succeeded in selection structures. About 77% of the students who succeeded in assignment/sequencing and selection also succeed in repetition. Students who did not succeed in both assignment/sequencing and selection had lower percentage of success in repetition: 39% when they succeeded only in selection; 61% when they succeeded only in assignment/sequencing; 38% when they failed in both assignment/sequencing and selection. The same trends were observed when analyzing performance in student understanding of arrays. In conclusion: 1) student performance in computer programming concepts taught early in the course affects performance in computer concepts taught later in the semester; 2) The ability of students to understand concepts involving a sequence of statements is a good early predictor of success/failure in understanding more advanced concepts like selection, repetition and arrays; 3) Matthew effects are at play in learning computer programming: early success (or failure) in understanding basic computer programming concepts begets later success (or failure) in understanding more advanced computer programming concepts.

The goal of this research to practice full paper is to identify which computer programming concepts/skills predict students' ability to write viable programs using repetition loops and custom methods in Java. We developed machine learning models (logistic regression and decision trees with Scikit-Learn) to predict student performance in writing computer programs. High scores in feature importance analysis of the concepts/skills used as inputs to the models were considered important for predicting the output (i.e., performance in writing viable programs using loops and methods). We found that: 1) The ability to write programs with repetition and methods relies on an adequate understanding of several previous pre-requisite concepts/skills; 2) The relative importance of the pre-requisite concepts/skills varies, but adequate understanding of selection structures, which is typically taught early in the semester, is critical for students to be able to write viable computer programs using repetition loops and methods later in the semester; 3) Machine learning models can be used as predictors of student ability to write viable computer programs; 4) The transparency and interpretability of white-box models, like logistic regression and decision trees, allows students and teachers to identify which pre-requisite concepts/skills need to be emphasized and reinforced to increase performance on a target concept/skill.

In this research to practice full paper we quantified progress in the ability of first-year students (n=54) to solve problems using computer programming control structures with different levels of complexity like sequencing, selection (if/else) and repetition (for/while). Students used both a flowchart interpreter and Python to write programs. We found that 70% of students could solve problems involving a sequence of statements (i.e. without the use of selection or repetition) using a flowchart interpreter or Python. The majority of the students who could not solve sequencing problems were not successful at solving problems involving selection and repetition (69% using flowcharts and 94% using Python). On the other hand, of the students who could solve sequencing problems 45% (flowchart) and 71% (Python) were able to solve problems involving selection and repetition. Therefore, the ability to solve problems involving a sequence of statements is a good early predictor of success/failure in solving problems with more complicated control structures like selection and repetition. Success in solving computer programming problems depends on the tool used for 37% of students. Therefore, the ability of students to transfer problem solving abilities between tools (from flowcharting to Python) is not automatic.

In this research to practice full paper we quantified student understanding of computer programming problems, and correlated it with their ability to write viable computer programs. To quantify problem understanding, students were asked to generate adequate input/output combinations for the problem. About 45% of students demonstrated a complete understanding of the problem. The remaining students (55%) showed inconsistencies in their understanding of programming problems. After the problem understanding assessment, students were asked to solve the problems by writing computer programs using Python. About 88% of the students with complete understanding of a problem could solve it with a Python program. In contrast, the vast majority of students who did not completely understand the problem (67%) were not able to write working computer programs. Surprisingly, ~ 33% of students with partial understanding of problems were able to write viable computer programs. We conclude that the challenges to write viable Python computer programs start with the failure to understand the problem. Helping students develop strategies to understand a problem correctly may help them writing viable computer programs that solve the problem.

Although game-based learning is becoming prevalent in higher education to promote student motivation and active learning, educators have not reached the potential of this pedagogical strategy. Due to games being so accessible, teachers can use digital and non-digital games not only to teach single disciplines but also in interdisciplinary settings. This chapter describes our use of game-based learning, specifically design-based games, in interdisciplinary contexts in several courses. We argue that this high-impact educational practice is an effective way to help students develop computational thinking and problem-solving skills. We also show how teachers can use design-based games to help students develop their writing skills by helping them to formulate a thesis, identify supporting evidence, and present and argue their points.

This Innovative Practice Full Paper presents the impact of Learning Communities (LC) on student retention, class attendance and performance outcomes in first-year computing courses. LCs are a group of students who enroll in two or more courses, generally in different disciplines that are linked together by a common theme, in an academic semester. Our results show that when first-year students take computing courses as part of a LC, retention rates increase and students perform significantly better. We also found that LCs promote class attendance and that students' academic and social interactions with classmates may play a critical role in the improvement of student performance observed in LC students.

This Research to Practice Full Paper evaluates the effectiveness of flowcharting as a scaffolding tool to learn a programming language like Python in the setting of an urban institution that serves mostly underrepresented minority students. We found that the abilities of students to solve problems using flowcharts is a good predictor of their ability to solve problems with Python (r-squared = 0.68). This means that the majority of students who perform well using flowcharts will perform well in Python. A majority of students found flowcharting easier than Python (63%), and reported that flowcharting helped them understand how to write programs in Python (73%). However, flowcharting is not a magic bullet for learning programming because about 31% of students have difficulty solving problems with a flowcharting tool (and Python). We also found that the ability of students to read code is not highly correlated with their ability to write code in Python. In conclusion: 1) For a majority of students flowcharting is an effective scaffolding tool to learn Python; 2) The ability to read and trace code is not predictive of the ability of students to solve problems and write viable programs in Python.

Interdisciplinary competence should be an integral part of undergraduate education. The development of interdisciplinary skills expands students' perspectives and blurs differences between general education and major courses, preparing them to be better problem-solvers in an increasingly complex and connected world. This chapter describes the design, development, and teaching of an interdisciplinary course linking creative writing and computational thinking for non-computer majors. In this interdisciplinary course, students develop original stories which they then implement as a video game prototype using computer programming. Via interdisciplinary connections between writing stories and writing computer code, even non-computer majors acquire computational thinking concepts and skills.

Strategies to Integrate Writing in Problem-Solving Courses: Promoting Learning Transfer in an Interdisciplinary ContextReflective writings, the contextualization of learning experiences, and the application of learning toreal life all facilitate the transfer of interdisciplinary learning. Such strategies include makingexplicit to students the need for such a transfer, advising them to follow the appropriate coursesequence, emphasizing material they need to transfer to other courses, practicing transfer byinviting guest lecturers, developing of metacognitive skills, and reinforcing concepts by using themin different contexts. As the transfer of learning does not occur automatically, curricular andcourse design should intentionally emphasize the connection between courses.Problem Solving with Computer Programming (PS) is a required course for first-year computersystems majors and offers an ideal opportunity to establish a transfer structure. To make studentsaware of the connections between PS and English Composition that is also required, and tofacilitate the transfer of skills, we developed a learning community (LC) linking these courses.This innovative approach to teaching computing and writing to first-year computer systemsmajors at a college of technology uses programming narratives as its theme. Students write andimplement narratives, using computer programming, to develop a narrative-driven video gameprototype using Alice, a three-dimensional animation software. The LC emphasizes theimportance of connecting courses in the major and those in general education. The LC builds onour previous research, which found that introducing narrative elements into problem-solvingcourses improves overall student performance and computer programming-related problem-solving skills in particular.In this presentation, we will describe best practices and lessons learned from our LC and we willpresent three different strategies to integrate writing in PS courses for majors and non-majors.First, since implementation of LCs is not always feasible, to infuse narrative elements intoproblem-solving we developed a narrative module to help students develop narrative and writingskills that can be incorporated in all sections of the PS course. Second, we developed a series ofstudent-assessed case studies that can be integrated in all sections of the PS course for computersystems majors. Cases studies provide a narrative context in which students learn basic constructsof computer programming such as sequencing, selection and repetition structures. Third, wecreated a general education interdisciplinary course, Programming Narratives: Computer AnimatedStorytelling, open to non-computer majors, which emphasize creative writing and computationalthinking. In this interdisciplinary course, students learn the structure of narrative, concepts ofproblem solving, and the logic of computer programming languages as they develop a narrative-driven video game prototype helping students achieve the college-wide learning goal of makingmeaningful and multiple connections among the liberal arts and between the liberal arts and theareas of study leading to a major or profession.

Concept maps are used to organize and represent information. In the context of creating narratives, concept maps can be used to represent the elements of a plot and/or the relationships between characters. Concept mapping has been shown to be an effective prewriting strategy leading to an improvement in student writing. We have investigated the use of concept maps to help students develop narratives in an introductory undergraduate English composition course. We found that students participating in an interdisciplinary learning community (LC) who implement their narratives collaboratively as a video game using computer programming produce better concept maps than students in a traditional English composition course. We conclude that the synergies that develop between English composition and computer programming in the interdisciplinary context of a LC result in more effective concept maps leading to an improvement in students’ performance in English composition courses.

Myocardial infarction causes remodeling of the tissue structure and the density and kinetics of several ion channels in the cell membrane. Heterogeneities in refractory period (ERP) have been shown to occur in the infarct border zone and have been proposed to lead to initiation of arrhythmias. The purpose of this study is to quantify the window of vulnerability (WV) to block and initiation of reentrant impulses in myocardium with ERP heterogeneities using computer simulations. We found that ERP transitions at the border between normal ventricular cells (NZ) with different ERPs are smooth, whereas ERP transitions between NZ and infarct border zone cells (IZ) are abrupt. The profile of the ERP transitions is a combination of electrotonic interaction between NZ and IZ cells and the characteristic post-repolarization refractoriness (PRR) of IZ cells. ERP heterogeneities between NZ and IZ cells are more vulnerable to block and initiation of reentrant impulses than ERP heterogeneities between NZ cells. The relationship between coupling intervals of premature impulses (V1V2) and coupling intervals between premature and first reentrant impulses (V2T1) at NZ/NZ and NZ/IZ borders is inverse (i.e. the longer the coupling intervals of premature impulses the shorter the coupling interval between the premature and first reentrant impulses); this is in contrast with the reported V1V2/V2T1 relationship measured during initiation of reentrant impulses in canine infarcted hearts which is direct. In conclusion: (1) ERP transitions at the NZ-IZ border are abrupt as a consequence of PRR; (2) PRR increases the vulnerability to block and initiation of reentrant impulses in heterogeneous myocardium; (3) V1V2/V2T1 relationships measured at ERP heterogeneities in the computer model and in experimental canine infarcts are not consistent. Therefore, it is likely that other mechanisms like micro and/or macro structural heterogeneities also contribute to initiation of reentrant impulses in infarcted hearts. (C) 2015 Elsevier Ltd. All rights reserved.