Many researchers have proposed program visualization tools for memory management. Examples include stateof-the-art tools for C languages such as SeeC and Python Tutor (PT). However, three problems hinder the use of these and other tools: capability (P1), installability (P2), and usability (P3). (P1) Tools do not fully support dynamic memory allocation or File Input / Output (I/O) and Standard Input. (P2) Novice programmers often have difficulty installing SeeC due to its dependence on Clang and setting up an offline environment that uses PT. (P3) Revisualization of the modified source code in SeeC requires several steps. To alleviate these issues, we propose a new visualization tool called PlayVisualizerC.js (PVC.js). PVC.js, which is designed for novice C language programmers to provide solutions (S1-3) for P1-3. S1 offers complete support for dynamic memory allocation, standard I/O, and file I/O. S2 involves installation in a user web browser. This system is composed of JavaScript programs, including C language execution functions. S3 reduces the steps required for revisualization. To evaluate PVC.js, we conducted two experiments. The first experiment found that students using PVC solved a set of four programming tasks on average 1.7-times faster and with 19% more correct answers than those using SeeC. The second experiment found that PVC.js has a visualization performance equivalent to PT, and that PVC.js is more effective than existing general debugging tools for novices to understand programs in cases where the values of important variables change and the control flow is complicated.

In some situations, it is necessary to measure personal programming skills. For example, often students must be divided according to skill level and motivation to learn or companies recruiting employees must rank candidates by evaluating programming skills through programming tests, programming contests, etc. This process is burdensome because teachers and recruiters must prepare, implement, and evaluate a placement examination. This paper tries to predict the placement and ranking results of programming contests via machine learning without such an examination. Explanatory variables used for machine learning are classified into three categories: Psychological Scales, Programming Tasks, and Student-answered Questionnaires. The participants are university students enrolled in a Java programming class. One target variable is the placement result based on an examination by a teacher of a class and the ranking results of the programming contest. Our best classification model with a decision tree has an F-measure of 0.912, while our best ranking model with an SVM-rank has an nDCG of 0.962. In both prediction models, the best explanatory variable is from the Programming Task followed in order by Psychological Sale and Student-answered Questionnaire. Our classification model uses 9 explanatory variables, while our ranking model uses 20 explanatory variables. These include all three types of explanatory variables. The source code complexity, which is a source code metrics from Programming Task, shows best performance when the prediction uses only one explanatory variable. Contribution (1), this method can automate some of the teacher's workload, which may improve educational quality and increase the number of acceptable students in the course. Contribution (2), this paper shows the potential of using difficult-to-formulate information for an evaluation such as a Psychological Scale is demonstrated. These are the contributions and implications of this paper.

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Many researchers have proposed program visualization tools for memory management because this is a challenging concept for novice programmers. For example, SeeC and PythonTutor (PT) are state-of-the-art tools for C languages. However, three problems hinder the use of these and other tools: capability (P1), installability (P2), and usability (P3). (P1) Tools do not fully support dynamic memory allocation or File Input / Output (I/O) and Standard Input. (P2) Novice programmers often have difficulty installing SeeC due to its dependence on Clang and setting up an offline environment that uses PT. (P3) Revisualization of the modified source code in SeeC requires several steps. To alleviate these issues, we propose a new visualization tool called PlayVisualizerC (PVC). PVC, which is designed for novice C language programmers to provide solutions (S1-3) for P1-3. S1 offers complete support for dynamic memory allocation, standard I/O, and file I/O. S2 involves installation in a user web browser and its server program is initiated by executing a jar file. S3 reduces the steps required for revisualization. To evaluate PVC, we conducted an experiment and questionnaire involving 30 students. Students using PVC solved a set of four programming tasks on average 1.7 times faster and with 19% more correct answers than those using a current state-of-the-art visualization tool. CCS CONCEPTS • Human-centered computing → Visualization systems and tools; • Social and professional topics → Computer science education;