Information processing device and information processing method

Abstract

The present invention provides an information processing device for assisting programming education combined with other class subjects.　This information processing device is provided with: an editing assistance unit that assists editing of a program by an editor; an execution unit that causes the program edited through the editing assistance unit to be executed on a program execution device; and an evaluation unit that evaluates the editor on the basis of the activity of the program execution device. The editing assistance unit assists editing of a program for the program execution device to act on the basis of the editor's knowledge about class subjects other than programming education.

Description

[Technical field]

[0001] The technology disclosed in this specification (hereinafter referred to as "the present disclosure") relates to an information processing device and an information processing method that support programming education. [Background technology]

[0002] With the development of information and communication technology, such as the spread of information terminals such as computers and smartphones, and the sophistication of control technology for various devices, the importance of program development technology is increasing. For this reason, even at the primary and secondary education stages, it is necessary to prepare the necessary environment for utilizing information means such as computers and information and communication networks, and to enhance learning activities that make appropriate use of these.

[0003] For example, by providing an environment that allows instructors to easily identify learners who are not achieving the desired results, it is possible to reduce the burden on instructors of on-site instruction.In addition, an educational support system has been proposed that can detect errors in input or operations made by learners to achieve the desired results and notify or suggest how to resolve the errors, thereby promoting understanding in learners even without the learners asking questions of their own accord (see Patent Document 1).

[0004] In addition, a proposal has been made to enable users to learn programming concepts through experience by providing a block mounting section capable of mounting multiple experiential blocks, and a device that moves according to the type and order of the blocks mounted on the block mounting section (see Patent Document 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2010-231580 A [Patent Document 2] JP 2017-161770 A Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the technology disclosed herein is to provide an information processing device and an information processing method that support programming education. [Means for solving the problem]

[0007] A first aspect of the technology according to the present disclosure is an editing support department that supports editors in editing programs; an execution unit that executes the program edited through the editing support unit on a program execution device; an evaluation unit that evaluates the editor based on the activity of the program execution device; The information processing device includes:

[0008] The editing support unit supports editing of a program for the program execution device to operate based on the editor's knowledge of a course subject other than programming education. The editing support unit may support the editor in editing a program by visual programming.

[0009] In addition, the evaluation unit distinguishes between and evaluates the editor's programming skills and the grades for the course using a learning model that has learned the correlation between information about the course and the activities performed by the program execution device, and the programming skills and the grades for the course.

[0010] In addition, a second aspect of the technology according to the present disclosure is an editing support step of supporting an editor in editing a program; an execution step of executing the program edited through the editing support step on a program execution device; evaluating the editor based on the activity of the program execution device; The information processing method includes the steps of: Effect of the Invention

[0011] According to the technology disclosed herein, it is possible to provide an information processing device and an information processing method that support programming education in combination with other course subjects.

[0012] Note that the effects described in this specification are merely examples, and the effects brought about by the technology according to the present disclosure are not limited thereto. Furthermore, the technology according to the present disclosure may have additional effects in addition to the effects described above.

[0013] Further objects, features, and advantages of the technology according to the present disclosure will become apparent from the following detailed description based on the embodiments and the accompanying drawings. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing an example of the functional configuration of a programming education support system 100. As shown in FIG. [Diagram 2] FIG. 2 is a diagram showing an example of an editing screen 200 of a visual programming language. [Diagram 3] FIG. 3 is a diagram showing an example of a program created on an editing screen 200 of a visual programming language. [Figure 4] Figure 4 shows science questions, correct answers, and examples of responses from the robot. [Diagram 5] FIG. 5 is a diagram showing an example of the operation of the performance evaluation neural network 500. As shown in FIG. [Figure 6] FIG. 6 is a diagram showing a mechanism for learning the grade evaluation neural network 500. As shown in FIG. [Figure 7] FIG. 7 is a diagram showing an example of an operation sequence of the programming education support system 100. As shown in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, embodiments of the technology according to the present disclosure will be described in detail with reference to the drawings.

[0016] With the development of information and communication technology, there is a demand for the addition of programming education as a subject. However, there is a limit to the number of class periods that can be assigned per week, and adding a new subject would put a strain on the schedule of other existing subjects and increase the burden on students. For this reason, educational methods have been devised that combine programming education with subjects such as science and mathematics into a single subject and have students learn it simultaneously.

[0017] However, there are some challenges in implementing an integrated class that combines programming education with other subjects. For example, when a teacher gives a test and a student's performance is poor, it is difficult to distinguish whether the cause is the student's immature programming skills or insufficient knowledge or exploration skills in the original subject (science, mathematics, etc.). In other words, for the same test question, a student may get the wrong answer either because of an error in the algorithm of the program he or she created or because of a lack of knowledge of the original subject. If the cause of poor performance cannot be accurately identified, the teacher will not be able to properly guide the student, such as by providing extra guidance or advice on science or mathematics to a student who is passing in science or mathematics, or by not providing guidance or advice on programming to a student who is lagging behind in acquiring programming skills.

[0018] In the first place, science and math teachers may not have sufficient programming skills. Even if teachers can evaluate students' science and math grades when conducting exams, there is a concern that they may not be able to accurately evaluate their programming skills.

[0019] Therefore, in this specification, the technology disclosed herein is applicable to a course that combines and integrates programming education with other courses, provides students with a programming environment, enables them to execute the programs they have created, and improves the students' programming skills and the quality of the original course based on the results of executing the programs. GradesBelow, we propose a programming education support device that can distinguish and evaluate the above.

[0020] FIG. 1 is a schematic diagram showing an example of a functional configuration of a programming education support system 100 to which the technology according to the present disclosure is applied. The illustrated programming education support system 100 includes a program editing support unit 110, a program execution unit 120, and an evaluation unit 130. For example, the program editing support unit 110, the program execution unit 120, and the evaluation unit 130 are implemented as individual devices such as personal computers (PCs). In this case, communication between the program editing support unit 110, the program execution unit 120, and the evaluation unit 130 is performed, for example, via a wired or wireless LAN (Local Area Network). Alternatively, the program editing support unit 110, the program execution unit 120, and the evaluation unit 130 may be computer programs executed on the same or multiple PCs. In this case, communication between the program editing support unit 110, the program execution unit 120, and the evaluation unit 130 may be inter-process communication.

[0021] The program editing support unit 110 supports the students in creating and editing programs. The program execution unit 120 executes the program created through the program editing support unit 110 on the program execution device 121. The evaluation unit 130 evaluates the student's grade based on the activity of the program execution device 121 when the program created by the student is executed.

[0022] In this embodiment, it is assumed that the programming education support system 100 is applied to a class that combines and integrates programming education with other lesson subjects such as science, arithmetic, etc. For convenience, the following description will be made assuming that the other lesson subjects are all science.

[0023] In the application environment assumed in this embodiment, a student creates and edits a program for the program execution device 121 to carry out an activity based on the student's own science knowledge via the program editing support unit 110. Then, the program editing support unit 110 transfers the completed program to the program execution unit 120.

[0024] The program execution device 121 referred to here is a robot, a character (or avatar) displayed on a display, or the like. It is assumed that the program execution device 121 has a function of interacting with a teacher, but the form of the interaction is not particularly limited to voice, gesture, or text information. If a robot is expensive and cannot be obtained, the program execution device 121 may be a robot simulator. However, for convenience, the program execution device 121 will be uniformly described as a robot below.

[0025] A program for acting based on a student's own science knowledge is specifically a program written to execute actions that respond to a teacher's science questions as intended by the student. For example, a program for realizing an activity that responds "high" to the questions "What's the temperature on a sunny day?" and "What's the temperature in summer?", or "Plants grow big" to the questions "What happens when you water plants?" and "The water in the soil?". Incidentally, a program for acting based on arithmetic knowledge rather than science is, for example, a program for realizing an activity in which a robot correctly draws an equilateral triangle or answers the four arithmetic operations.

[0026] The program execution unit 120 is basically operated by the teacher of the class. The program execution unit 120 installs the program transferred from the program editing support unit 110 into the robot, and the robot executes the program, allowing the student who created the program to act based on his or her own science knowledge.

[0027] The teacher creates questions for the students regarding the creation of such programs and asks them the questions. The teacher may present the content of the questions to the students on a medium such as paper, by writing on a blackboard or whiteboard, or orally. In this programming education support system 100, the teacher inputs the content of the questions to the program execution unit 120, which transfers the content of the questions to the program editing support unit 110. The program editing support unit 110 may provide the students with an editing environment that makes it easy to create and edit a program for the robot to act according to the content of the questions.

[0028] The program editing environment provided to the student by the program editing support unit 110 is not particularly limited. The program editing support unit 110 may be composed of only a console based on a character user interface, and the student may create source code. However, in this embodiment, the program editing support unit 110 prepares an editing environment in which the student focuses on thinking up a program algorithm required for the robot to operate as intended (in other words, in line with the science questions given by the teacher) rather than on coding the program. In other words, it is desirable that the program editing support unit 110 has an editing environment that does not require knowledge of the grammar of a specific programming language and does not require debugging work that relies on knowledge of a programming language.

[0029] For example, the program editing support unit 110 may provide the student with an editing environment in which a visual programming language can be used. Fig. 2 shows an example of an editing screen 200 for a visual programming language provided to the student by the program editing unit 110. In a typical visual programming language, a program creator (a student in this embodiment) can write a program algorithm by placing objects represented by circles and rectangles on the editing screen 200 and connecting the objects with arrows, lines, and arcs.

[0030] In the example shown in FIG. 2, as indicated by reference numbers 201 to 208, a plurality of objects corresponding to subjects, objects, predicates, etc., such as "sunny day", "summer", "temperature", "rain", "watering", "in the soil", "grows big", "doesn't grow much", etc., are arranged on the editing screen 200. These objects correspond to the questions that the teacher asks the students. The teacher may design these objects himself. Alternatively, the teacher may create science test questions in a text format as in the past, and the program execution unit 120 or the program editing support unit 110 may convert the test questions into objects to be arranged on the editing screen 200, or automatically generate the objects.

[0031] As shown in FIG. 3, students create a program algorithm for the robot to act by connecting objects 201 to 208 on the editing screen 200 with arrows, lines, and arcs. Based on their own science knowledge, students can create a program algorithm for the robot to answer the following science questions from the teacher via the editing screen 200 shown in FIG. 2 and FIG. 3. Students can learn science through creating programs. It is also expected that the program created by the student will be a program that enables the robot to act based on the student's own science knowledge.

[0032]

number

[0033] The program editing support unit 110 may output the program algorithm created in the visual programming language format as is to the program execution unit 120, or may convert it into source code in another computer language (e.g., C language) format that expresses the program algorithm and output it to the program execution unit 120, or may even compile the source code and then output it to the program execution unit 120.

[0034] The program execution unit 120 installs the program transferred from the program editing support unit 110 into the robot. The robot then executes the installed program. It is assumed that the robot that executes the program created by the student acts based on the science knowledge of the student who created the program.

[0035] The teacher asks the robot, not the students, questions about science. In response, the robot answers the teacher's questions according to the running program. The exchange of questions and answers between the teacher and the robot may be in the form of a dialogue using voice. The robot may answer by voice, or may answer using gestures, or may answer in any format. If the question is multiple-choice, the robot may output a signal specifying the selected branch. Of course, the teacher may input the science question using a user interface (such as a keyboard) of the program execution unit 120, and the question data may be transferred from the program execution unit 120 to the robot.

[0036] As mentioned above, students create programs that enable the robot to act based on their own science knowledge. Therefore, it is expected that the robot running the program created by the student will answer questions from the teacher based on the science knowledge of the student who created the program.

[0037] The evaluation unit 130 monitors the robot executing a program created by a student. The evaluation unit 130 acquires science questions posed to the robot by the teacher. The evaluation unit 130 also acquires the correct answers to the questions posed to the robot by the teacher from the teacher or inputs them via the program execution unit 120. The evaluation unit 130 can acquire questions and correct answers, as well as the answers given by the robot to the questions, as shown in FIG. 4, for example.

[0038] The evaluation unit 130 evaluates each of the students' programming skills and science knowledge based on the science questions asked by the teacher, the correct answers, and the answers given by the robot. The evaluation unit 130 then notifies the teacher and the students of the evaluation results. The teacher creates report cards for the students based on the evaluation results notified by the evaluation unit 130. Also, the evaluation results may not be directly notified to the students, but may be indirectly notified through the teacher (for example, by a report card).

[0039] In the programming education support system 100, programming education and science are combined and integrated into one class. The robot's answer to the teacher's question about science is a combination of the student's programming skills and the student's knowledge about science. For example, the cause of the robot's incorrect answer may be the program created by the student. M This may be due to an algorithm error or the student's lack of science knowledge. In addition, even if the student himself cannot answer the question correctly, the robot may answer the question correctly due to an algorithm error in the program created by the student.

[0040] If the students directly answered the science test, simply marking the test results as shown in FIG. 4 would improve the students' science knowledge, but would not allow the students' programming skills to be evaluated. On the other hand, in a class format in which students create programs and take science tests on robots that run those programs, students can simultaneously study science and program, but it is difficult to distinguish and evaluate the students' programming skills and science knowledge from the results of the tests on the robot. In addition, even if a teacher is knowledgeable about science, if he or she does not have sufficient programming skills, it is difficult for the teacher to directly look at the programs (created by the students) output from the programming editing support unit 110 and evaluate the students' programming skills.

[0041] Therefore, in this embodiment, the evaluation unit 130 uses artificial intelligence to distinguish between the programming skills and science knowledge of the student and evaluates them based on the robot's answers to science-related questions asked by the teacher.

[0042] Here, artificial intelligence refers to a function that artificially realizes functions performed by the human brain, such as learning, inference, data creation, and planning, using software or hardware. Artificial intelligence generally uses a learning model represented by a neural network that mimics the neural circuits of the human brain. A neural network is a network formed by connections between artificial neurons (hereinafter simply referred to as "neurons") via synapses. Artificial intelligence is equipped with a mechanism for constructing a learning model that estimates the optimal solution (output) for a problem (input) by repeatedly learning using training data and changing the connection weight coefficients between neurons.

[0043] In order to evaluate students' programming skills and science knowledge with a higher degree of accuracy, it is preferable to use a neural network that performs deep learning (DL). When performing deep learning, the number of training data and the number of synapses are both large. Therefore, it is considered appropriate to perform deep learning using huge computer resources such as the cloud.

[0044] FIG. 5 shows a schematic example of the operation of the grade evaluation neural network 500 used as an artificial intelligence in the evaluation unit 130. The grade evaluation neural network 500 is provided with a learning model that represents the correlation between a science problem and its correct answer, a program created by a student, a response result of a robot that executes the program created by the student, a student's programming skill, and a student's science knowledge. Therefore, when a science problem and its correct answer, and a response result of a robot that executes a program created by a student are input, the grade evaluation neural network 500 can output a result of evaluation that distinguishes between the programming skill and science knowledge of the student. Note that, an operation log during creation using the program editing support unit 110 may also be input to the grade evaluation neural network 500 together with the program created by the student.

[0045] Even if the science teacher has poor programming skills and cannot evaluate the programs created by the students, he or she only needs to give science questions to the robot that executes the programs created by the students. The grade evaluation neural network 500 inputs the questions and correct answers given by the science teacher, the programs created by the students and the operation logs at the time of creation, and the answers to the science questions given by the robot that executes the programs created by the students, and outputs the evaluation results by distinguishing between the programming skills and science knowledge of the students based on the results of the advance learning.

[0046] It can be difficult to distinguish between a student's programming skills and science knowledge from the results of a robot's answers to one science question. This is because if a student answers incorrectly, there are two possible causes: an algorithm error in the program the student created, or a lack of science knowledge, and it is difficult to distinguish between these two causes with just one question. Even if the student answers correctly, it is possible that the robot gave an answer that was different from what the student intended due to an algorithm error in the program the student created, in addition to the fact that both the student's programming skills and science knowledge are passing grades, and it is difficult to distinguish between these two causes with just one question.

[0047] In contrast, by using the performance evaluation neural network 500 equipped with a learning model that has learned the correlation between multiple answers given by the robot to multiple science questions and the programming skills and science knowledge of the students, it is possible to distinguish between the programming skills and science knowledge of the students and output more accurate evaluation results. For example, a recurrent neural network (RNN) having a recursive structure may be used for the neural network 500.

[0048] FIG. 6 illustrates the mechanism by which the grade evaluation neural network 500 learns.

[0049] The learning data database 500 accumulates a huge amount of learning data as learning data for the performance evaluation neural network 500, which consists of combinations of science problems and correct answers, and the robot's answers to the science problems as input vectors to the performance evaluation neural network 500, and combinations of students' programming skills and science knowledge as output vectors from the performance evaluation neural network 500.

[0050] When the performance evaluation neural network 500 is learning, the learning data is taken out one by one, and the science questions and correct answers as input vectors, and the robot's answer to the science questions are input to the performance evaluation neural network 500. Each time an input vector is input, the performance evaluation neural network 500 distinguishes and outputs the programming skills and science knowledge of the student who created the program executed by the robot.

[0051] The comparison unit 601 compares the programming skills and science knowledge of the student output from the performance evaluation neural network 500 with the programming skills and science knowledge of the student as an output vector extracted from the learning data database 600. The comparison unit 601 defines a loss function based on the difference between the output vector of the performance evaluation neural network 500 and the output vector extracted from the learning data database 600. Then, the performance evaluation neural network 500 is trained by backpropagation (error backpropagation method) so that this loss function is minimized. Then, the learning model having a set of coupling weight coefficients between neurons in the performance evaluation neural network 500 when the loss function is minimized is set as the final learning result.

[0052] The evaluation unit 130 also notifies the teacher or student of the student's programming skills and science knowledge estimated by the grade evaluation neural network 500 (as described above). The teacher or student returns feedback (FB) indicating whether or not they agree with the evaluation of the programming skills and science knowledge (for example, the distribution of the evaluations of the programming skills and science knowledge is wrong). The comparison unit 601 may define a loss function that further considers the difference between the evaluation result by the grade evaluation neural network 500 and the feedback from the teacher or student, and perform learning of the grade evaluation neural network 500.

[0053] FIG. 7 shows an example of an operation sequence of the programming education support system 100 according to this embodiment.

[0054] The program execution unit 120 transfers the questions on program creation proposed by the teacher to the program editing support unit 110 (SEQ701). Basically, it is assumed here that the question data input by the teacher to the program execution unit 120 is transferred to the program editing support unit 110. However, without data communication between the program execution unit 120 and the program editing support unit 110, the teacher may present the content of the questions to the students on a medium such as paper, by writing on a blackboard or whiteboard, or orally.

[0055] The program editing support unit 110 provides the student with an editing environment, such as visual programming, that does not require knowledge of the grammar of a specific programming language and does not require debugging work that relies on knowledge of a programming language. The student uses the program editing support unit 110 to create and edit a program for the robot to act based on the student's own science knowledge (SEQ702).

[0056] Then, the program editing support unit 110 transfers the completed program to the program execution unit 120 (SEQ703).

[0057] The program execution unit 120 installs the program transferred from the program editing support unit 110 in the robot (SEQ704). By executing the program, the robot can act based on the science knowledge of the student who created the program.

[0058] After that, the teacher asks the robot, not the students, a question about science (SEQ705). In response, the robot answers the question according to the running program (SEQ706).

[0059] Students write programs that allow the robot to act based on their own science knowledge. Therefore, it is expected that the robot running the program will answer questions from the teacher based on the science knowledge of the student who wrote the program.

[0060] The evaluation unit 130 acquires the questions asked by the teacher to the robot, the correct answers, and the answers given by the robot to the questions. The method of acquiring these data is arbitrary. For example, the evaluation unit 130 may acquire these data by performing voice recognition on the dialogue between the teacher and the robot, or may acquire the data from the program execution unit 120 or the robot through data communication.

[0061] Then, the evaluation unit 130 uses the performance evaluation neural network 500 to distinguish between the programming skills and science knowledge of the student and evaluates them based on the answers of the robot to the science questions asked by the teacher (SEQ707). The performance evaluation neural network 500 is equipped with a learning model that has previously learned correlations between science questions and their correct answers, answers of the robot that executes the program created by the student, the programming skills of the student, and the science knowledge of the student (as described above).

[0062] After that, the evaluation unit 130 notifies the teacher and the student of the programming skills and science knowledge of the student, respectively, estimated by the performance evaluation neural network 500 (SEQ708, SEQ709). [Industrial Applicability]

[0063] The technology according to the present disclosure has been described in detail above with reference to specific embodiments. However, it is obvious that a person skilled in the art can modify or substitute the embodiments without departing from the gist of the technology according to the present disclosure.

[0064] The technology disclosed herein can be applied to classes that integrate programming education with science and various other subjects, and can accurately evaluate students' programming skills and their grades in the original subjects. The technology disclosed herein can also be applied to programming education at various stages, including not only primary and secondary education, but also universities, vocational schools, and working adults.

[0065] In short, the technology according to the present disclosure has been described in the form of examples, and the contents of the present specification should not be interpreted in a limiting manner. In order to determine the gist of the technology according to the present disclosure, the claims should be taken into consideration.

[0066] The technology according to the present disclosure can also be configured as follows.

[0067] (1) an editing support unit that supports editors in editing programs; an execution unit that executes the program edited through the editing support unit on a program execution device; an evaluation unit that evaluates the editor based on the activity of the program execution device; An information processing device comprising:

[0068] (2) the editing support unit supports editing of a program for the program execution device to act based on the editor's knowledge of a subject other than programming education; The information processing device according to (1) above.

[0069] (3) The execution unit controls the activity of the robot or character as the program execution device in accordance with the program edited through the editing support unit. An information processing device according to any one of (1) or (2) above.

[0070] (4) the evaluation unit distinguishes and evaluates the programming skills of the editor and the grades of the course based on the activities performed by the program execution device that executes the program on the information related to the course. An information processing device according to any one of (1) to (3) above.

[0071] (5) The evaluation unit evaluates the programming skills of the editor and the grades of the course using a trained learning model. The information processing device according to (4) above.

[0072] (6) The evaluation unit uses the learning model that has learned the correlation between the information about the course and the activity performed by the program execution device, and the programming skills and the grades for the course to distinguish and evaluate the programming skills and the grades for the course of the editor. The information processing device according to (5) above.

[0073] (7) The editing support unit supports an editor in editing a program by visual programming. An information processing device according to any one of (1) to (6) above.

[0074] (8) an editing support step of supporting an editor in editing a program; an execution step of executing the program edited through the editing support step on a program execution device; evaluating the editor based on the activity of the program execution device; An information processing method comprising the steps of:

[0075] (8-1) In the editing support step, the program execution device supports the editor in editing a program for performing an activity based on the editor's knowledge of a course other than programming education. The information processing method according to (8) above.

[0076] (8-2) In the execution step, the activity of a robot or a character as the program execution device is controlled in accordance with the program edited in the editing support step. The information processing method according to any one of (8) or (8-1) above.

[0077] (8-3) in the evaluation step, the program execution device that executes the program evaluates the programming skill of the editor and the grade of the course based on the activity performed on the information on the course. The information processing method according to any one of (8) to (8-2) above.

[0078] (8-4) In the evaluation step, the programming skills of the editor and the grades of the course are evaluated using the trained learning model. The information processing method described in (8-3) above.

[0079] (8-5) In the evaluation step, the editor's programming skills and the grades for the course are distinguished and evaluated using the learning model that has learned correlations between the information about the course and the activity performed by the program execution device, and the programming skills and the grades for the course. The information processing method described in (8-4) above.

[0080] (8-6) In the editing support step, the editor is supported in editing a program by visual programming. The information processing method according to any one of (8) to (8-5) above. [Explanation of symbols]

[0081] 100…Programming education support system 110... program editing support unit, 120... program execution unit 121...Program execution device (robot, character, avatar, ...) 130…Evaluation Department 500…Neural network 600... learning data database, 601... comparison section

Claims

1. an editing support department that supports editors in editing programs; an execution unit that executes the program edited through the editing support unit on a program execution device; an evaluation unit that evaluates the editor based on the activity of the program execution device; Equipped with the editing support unit supports editing of a program for the program execution device to carry out an activity related to the class subject based on the editor's knowledge of a class subject other than programming education, The evaluation unit uses a trained learning model to distinguish and evaluate the programming skills of the editor and the grades of the course based on the activities performed by the program execution device that executes the program on the information on the course. Information processing device.

2. the editing support unit supports editing of a program for the program execution device to answer questions about the lesson subject based on the editor's knowledge of the lesson subject; The information processing device according to claim 1 .

3. The execution unit controls the activity of a robot or a character as the program execution device in accordance with the program edited through the editing support unit. The information processing device according to claim 1 .

4. The evaluation unit uses the learning model that has learned correlations between information about the course and the activity performed by the program execution device, and programming skills and grades for the course to distinguish and evaluate the editor's programming skills and grades for the course. The information processing device according to claim 1 .

5. The editing support unit supports the editor in editing a program by arranging components of sentences that constitute questions and answers and presenting a user interface that allows the editor to create a program algorithm for answering the questions by connecting the components of the sentences. The information processing device according to claim 1 .

6. An editing support step in which an editing support unit provided in the information processing device supports an editor in editing a program; an execution step of causing an execution unit included in the information processing device to execute the program edited through the editing support step on a program execution device; an evaluation step in which an evaluation unit included in the information processing device evaluates the editor based on an activity of the program execution device; having In the editing support step, the program execution device supports editing of a program for performing an activity related to the class subject based on the editor's knowledge of a class subject other than programming education, In the evaluation step, the program execution device that executes the program uses a trained learning model to distinguish and evaluate the programming skills of the editor and the grades of the course based on the activities performed on the information on the course by the program execution device that executes the program. Information processing methods.

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