Satoru Kogure

Code-reading support environment visualizing three fields and educational practice to understand nested loops

In this paper, we describe a code-reading support environment and practical classroom applications using this environment to understand nested loops. Previously, we developed a code-reading support system based on visualization of the relationships among the program code, target domain world, and operations. We implemented the proposed system in exercises with nested loops. The evaluation results suggested that students could frequently fulfill learning objectives using the proposed system. However, we also discovered that some students experienced a learning impasse in the classroom. We attempted to address these students with two supporting approaches: bridging the gap between the generalization structures in the program code and their corresponding operations and enabling learners to predict the behavior of the nested loops. In this paper, we extend our previous system with new functions based on our two supporting approaches. Further, we implement the system in another classroom for nested loops. We describe a correlation between the proposed system and an understanding of nested loops using pre-/post-test comparisons. We discuss how code reading using the proposed system allows learners to cultivate a superior understanding of the program code.

Practices of algorithm education based on discovery learning using a program visualization system

In this paper, we describe three practical exercises relating to algorithm education. The exercises are based on a learning support system that offers visualization of program behavior. Systems with the ability to visualize program behavior are effective to promote the understanding of algorithm behavior. The introduction of these systems into an algorithm course is expected to allow learners to cultivate a more thorough understanding. However, almost all existing systems cannot incorporate the teacher’s intent of instruction that may be necessary to accommodate learners with different abilities by using a different instructional approach. Based on these considerations, we conducted classroom practice sessions as part of an algorithm course by incorporating the visualization system we developed in our previous work. Our system visualizes the target domain world according to the visualization policy defined by the teacher. Our aim with the practical classes is to enable learners to understand the properties of algorithms, such as the number of comparisons and data exchanges. The contents of the course are structured such that the properties of an algorithm can be understood by discovery learning in the practical work. In this paper, we provide an overview of our educational practices and learners’ responses and show that the framework we use in our practices can be established in algorithm classes. Furthermore, we summarize the requirements for the inclusion of discovery learning in the algorithm classes as the knowledge obtained from our practices.

Monitoring system for the effective instruction based on the semi-automatic evaluation of programs during programming classroom lectures

In this study, we developed a programming practice monitoring system to facilitate teachers to give appropriate instructions to students at the appropriate time during classroom lectures. To help teachers to provide appropriate instruction to learners, we identified parameters that would be useful for teachers during programming exercise in classroom lecture. We constructed a monitoring system with five functions. The system automatically acquired the programs written by students to evaluate their performance, and the teacher can obtain their performance using the five functions. We asked four subjects to test our proposed monitoring system during a simulation of a classroom lecture. The evaluation revealed that the system had a high accuracy in evaluating student programs.

Classroom practice for understanding pointers using learning support system for visualizing memory image and target domain world

Pointers are difficult learning targets for novice learners of C programming. For such difficult targets, introducing a system visualizing program behaviors is generally expected to support learners to understand the targets. However, visualization in existing systems often conceals the concrete value of variables such as pointers; the way in which each visualized object is located on the memory is not made explicit. In order to address this issue, we focused on a program visualization system called TEDViT. It visualizes simultaneously and synchronously the memory image that is the field that presents the concrete value of variables and the target domain world that is the field that presents logically the data structures processed by the program. We consider that observing and comparing program code, memory image, and target domain world with TEDViT could work for understanding pointers. TEDViT visualizes the status of the target domain world according to the visualization policy defined by the teacher in order to allow teachers to set their instruction content based on the growing variety of learner background knowledge. We also consider that this feature could support teachers’ instructions and class managements appropriately, and improving teachers’ performance by TEDViT’s support would bring improvement of learners’ understanding. We conducted classroom practice for understanding pointers in connection with a memory model, thus introducing TEDViT to a real class. Analysis of answered scores in a questionnaire conducted after the practice suggests that our practice using TEDViT provided useful supports for participants to understand pointers. It also suggests our practice had a certain effect to reduce uneven levels of understanding among participants. Based on these results, we describe that classroom practices in our framework could support learners to understand pointers and support teachers to manage the class.

Practice supporting system with related problem set generator based on targeted educational effects

Exercises with well-designed similar problem sets are effective in classrooms. In this case, teachers design similar problem sets related to the educational effects they have targeted. However, to design these “related problem sets (RPSs)” is not so easy for teachers, especially for students who are studying the problems. To support them, an intelligent tutoring system is expected to generate RPSs for teachers’ and learners’ targeting educational effects and support exercises for learners using these RPSs. It is useful for teachers who provide RPSs to learners with their educational effects and/or learners who want to study by themselves to get rid of their own weakness. This paper suggested the RPS generation and exercises supporting functions by an intelligent tutoring system for high school chemistry named Intelligent Practice Supporting System (IPSS). Some experiments confirmed that the performance of RPS generation and the exercises with IPSS had better educational effects than the ones without RPSs.