JP7057566B2 - Analytical system and analytical method - Google Patents

Description

The present invention relates to an analysis system and an analysis method.

In recent years, learning models have diversified and range from e-learning to blended learning that combines classroom lessons and e-learning. In such an educational environment, LMS (Learning Management System) is used.

The LMS has various functions such as distribution of learning contents, test implementation, and scoring. According to this LMS, it is possible to collect various histories such as operation logs related to the learner's learning activities via the LMS, and records such as reports and evaluation results.

For example, a technique (see Non-Patent Document 1) that allows a learner to refer to an editing history at the time of learning is disclosed. Further, a technique of acquiring brain waves as biological information and determining the difficulty level of a teaching material is also disclosed (see Patent Document 1).

In addition, a technique for aggregating error messages during execution of a programming learner's program and analyzing the learner's weaknesses (see Non-Patent Document 2), and parsing the final completed program to form a correct answer program. A technique for clarifying a difference (see Non-Patent Document 3) is disclosed. In addition, although we have built an environment to acquire the history of the program creation process, there are reports that do not lead to analysis (see Non-Patent Document 4) and techniques for manual analysis (see Non-Patent Document 5). It has been disclosed.

U.S. Patent Application Publication No. 2006/0147007

Michitaka Aramoto, Manabu Kobayashi, Makoto Nakazawa, Michiko Nakano, Masayuki Goto, Shigeichi Hirasawa, "Learning Analytics Using Editing History Visualization System-System Configuration and Implementation", IPSJ 78th National Convention Proceedings, pp.4- 527-4-528, Yokohama, (2016.3). Toshiyasu Kato, Yasushi Kambayashi and Yasushi Kodama, “Data Mining of Students ’Behaviors in Programming Exercises”, Smart Education and e-Learning 2016, pp.121-133, 2016 Yoshiki Majima, Kenji Oga, Kazuhiro Takeuchi, "Development of Similarity for Function-Based Program Structure Comparison", IPSJ Research Report, CE 138 No.12, pp.1-6, 2017 Kazuki Mori, Takafumi Tanaka, Hiroaki Hashiura, Atsuo Hashiyama, Seiichi Komiya, "Development of Fine Particle History Collection Environment to Support Programming Exercises", IPSJ Research Report, SE 179 No.16, pp.1-6, 2013 Shinya Naga, Kazuhei Nagashima, Hiroki Manabe, Susumu Kanemune, Mitaro Namiki, "Activity Log Evaluation Support Function in Programming Classes for Beginners", IPSJ Research Report, CE 138 No.4, pp.1-8, 2017

However, the conventional technique is not efficient because it requires comparison with the correct answer program and a large amount of backtracking during analysis. Moreover, the conventional technique has not yet automatically analyzed the weaknesses of the learner such as the learner's stumbling block and the cause of lack of understanding in the process of creating the program.

The present invention has been made in view of the above, and an object of the present invention is to provide an analysis system and an analysis method capable of analyzing a learner's weaknesses during programming learning.

In order to solve the above-mentioned problems and achieve the object, the analysis system according to the present invention is an acquisition unit that acquires edit history information in the process of creating a program by a learner and brain wave information indicating the state of the learner's brain wave. It has an analysis unit that analyzes the weaknesses of the learner for each component of the program based on the editing history information and the brain wave information, and an output unit that outputs the analysis result by the analysis unit as the composition information analysis result. It is characterized by.

Further, the analysis system according to the present invention is based on the edit history information in the process of creating a program by the learner, the acquisition unit for acquiring the brain wave information indicating the state of the learner's brain wave, and the edit history information and the brain wave information. It is characterized by having an analysis unit that analyzes the weaknesses of the learner regarding the block structure of the program, and an output unit that outputs the analysis result by the analysis unit as the block structure trial and error analysis result.

In addition, the analysis system according to the present invention has an acquisition unit that acquires edit history information in the process of creating a program by the learner, and based on the edit history information, the learner's weaknesses regarding program errors are determined for each error type. It is characterized by having an analysis unit for analysis and an output unit for outputting the analysis result by the analysis unit as an error analysis result.

According to the present invention, the weaknesses of the learner during programming learning can be analyzed.

FIG. 1 is a diagram showing an example of a configuration of an analysis system according to an embodiment. FIG. 2 is a diagram showing an example of a data structure of edit history information. FIG. 3 is a diagram showing an example of a data structure of electroencephalogram information. FIG. 4 is a diagram showing an example of the data structure of the individual component analysis result. FIG. 5 is a diagram showing an example of the data structure of the component analysis result. FIG. 6 is a diagram showing an example of the data structure of the block structure trial and error analysis result. FIG. 7 is a diagram showing an example of the data structure of the error analysis result. FIG. 8 is a sequence diagram showing an example of the processing procedure of the edit history acquisition process. FIG. 9 is a sequence diagram showing a processing procedure of brain wave information acquisition processing. FIG. 10 is a sequence diagram showing a processing procedure of the component analysis process. FIG. 11 is a sequence diagram showing a processing procedure of the block structure trial and error analysis process. FIG. 12 is a sequence diagram showing a processing procedure of error analysis processing. FIG. 13 is a sequence diagram showing a processing procedure of analysis result output processing. FIG. 14 is a flowchart showing a processing procedure of the component analysis process shown in FIG. FIG. 15 is a flowchart showing a processing procedure of the component type analysis process shown in FIG. FIG. 16 is a flowchart showing a processing procedure of the block structure determination process shown in FIG. FIG. 17 is a flowchart showing a processing procedure of the conditional branch conditional element determination process shown in FIG. FIG. 18 is a flowchart showing a processing procedure of the conditional branch block determination process shown in FIG. FIG. 19 is a flowchart showing a processing procedure of the arithmetic element determination process shown in FIG. FIG. 20 is a flowchart showing a processing procedure of the identifier element determination process shown in FIG. FIG. 21 is a flowchart showing a processing procedure of the emotional state analysis process shown in FIG. FIG. 22 is a flowchart showing a processing procedure of the block structure trial and error analysis process shown in FIG. FIG. 23 is a flowchart showing a processing procedure of the error analysis process shown in FIG. FIG. 24 is a diagram showing an example of a computer in which a learning device and an analysis device are realized by executing a program.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. The present invention is not limited to this embodiment. Further, in the description of the drawings, the same parts are indicated by the same reference numerals.

[Embodiment]
[Analysis system configuration]
An embodiment of the present invention will be described. FIG. 1 is a diagram showing an example of a configuration of an analysis system according to an embodiment. As shown in FIG. 1, the analysis system 1 according to the embodiment has a configuration in which the learning device 20 and the analysis device 30 are connected via a network 90.

The learning device 20 acquires the editing history information regarding the editing history of the program creation process by the learner at the time of programming learning. Further, the learning device 20 acquires brain wave information indicating the state of the learner's brain wave at the time of programming learning.

The analyzer 30 acquires edit history information and brain wave information from the learning device 20. Based on these acquired information, the analyzer 30 performs component analysis, block structure trial and error analysis, and error analysis, and analyzes the weaknesses of the programming learner. Then, the analyzer 30 supports the learner's efficient learning by providing the analysis result to the learner or the like.

[Configuration of learning device]
Next, the configuration of the learning device 20 will be described. In the learning device 20, for example, a predetermined program is read into a computer or the like including a ROM (Read Only Memory), a RAM (Random Access Memory), a CPU (Central Processing Unit), and the CPU executes the predetermined program. It is realized by. As shown in FIG. 1, the learning device 20 has an edit history information acquisition unit 21 and an electroencephalogram information acquisition unit 22.

The edit history information acquisition unit 21 acquires edit history information in the process of creating a program by the learner. The edit history information is a history of input status to the program by the learner.

The electroencephalogram information acquisition unit 22 acquires electroencephalogram information indicating the state of the learner's electroencephalogram. The electroencephalogram information includes, for example, the intensity of α wave, β wave, and the like. In addition, the electroencephalogram information includes the electroencephalogram concentration degree and the electroencephalogram contemplation degree derived from the electroencephalogram pattern and the like.

[Analyzer configuration]
Next, the configuration of the analyzer 30 will be described. The analyzer 30 is realized by reading a predetermined program into, for example, a computer including a ROM, RAM, a CPU, etc., and the CPU executing the predetermined program. As shown in FIG. 1, the analyzer 30 includes an acquisition unit 301, an edit history information storage unit 33, a brain wave information storage unit 34, an analysis unit 302, a component analysis result storage unit 38, and a block structure trial and error analysis result storage unit 39. It has an error analysis result storage unit 40 and an analysis result output unit 41.

The acquisition unit 301 acquires edit history information and brain wave information from the learning device 20. The acquisition unit 301 has an edit history information acquisition unit 31 and an electroencephalogram information acquisition unit 32. The edit history information acquisition unit 31 acquires edit history information from the edit history information acquisition unit 21 of the learning device 20 and stores it in the edit history information storage unit 33. The electroencephalogram information acquisition unit 32 acquires the electroencephalogram information 341 from the electroencephalogram information acquisition unit 22 of the learning device 20 and stores it in the electroencephalogram information storage unit 34.

The edit history information storage unit 33 stores the edit history information. FIG. 2 is a diagram showing an example of a data structure of edit history information. As shown in FIG. 2, the edit history information 331 has items of user ID, date and time, and edit status information. The user ID is identification information that identifies the learner. The editing status is information indicating what kind of character was input or deleted at which position in the program. The editing history information 331 may include all the information at each time point during the editing work. Further, the edit history information 331 may be difference information. Further, the edit history information 331 also includes error type information at the time of building and at the time of execution of the program in the editing status.

Here, in FIG. 2, for example, the learner with the user ID “001” made an edit in which “f” was added to the position 10 at 10:05:41 on October 5, 2016. Is illustrated.

The electroencephalogram information storage unit 34 stores the electroencephalogram information. FIG. 3 is a diagram showing an example of a data structure of electroencephalogram information. As shown in FIG. 3, the brain wave information 341 includes a user ID, a date and time, an α wave value, a β wave value, a γ wave value, a δ wave value, a θ wave value, a brain wave concentration degree, and a brain wave contemplation degree. Has an item. Here, the date and time represent the day and time when the electroencephalogram information was collected. The α wave, β wave, γ wave, δ wave, and θ wave represent the intensity of each component of the brain wave. The electroencephalogram concentration and the electroencephalogram pondering degree represent the concentration and the pondering degree of the learner's teaching materials derived from the electroencephalogram pattern and the like. FIG. 3 illustrates, for example, that the learner with the user ID “ID001” had an electroencephalogram concentration level of 80 and an electroencephalogram contemplation level of 25 at 10:05:41 on October 5, 2016. ..

The analysis unit 302 performs component analysis, block structure trial and error analysis, and error analysis based on the edit history information and the brain wave information, and analyzes the weaknesses of the programming learner. The analysis unit 302 has a component analysis unit 35, a block structure trial and error analysis unit 36, and an error analysis unit 37.

The component analysis unit 35 analyzes the learner's weaknesses with respect to each component of the program based on the edit history information and the brain wave information, and stores the analysis result as the component information analysis result in the component analysis result storage unit 38. .. The component analysis unit 35 analyzes the weaknesses of the learner for each component of the programming language.

First, the component analysis unit 35 analyzes and specifies the component type for all the records of the edit history information using the edit history information. Then, the component analysis unit 35 calculates the editing time from the start time and the end time of the specified component type. Subsequently, the component analysis unit 35 analyzes the emotional state during the start time and the end time by using the brain wave information.

Then, the component analysis unit 35 calculates the total editing time and the average emotional state for each component type based on the component type, the editing time, and the emotional state in all the records of the editing history information. The component analysis unit 35 determines whether or not the total editing time is longer than the predetermined time for each component type. This predetermined time is a preset time.

The component analysis unit 35 determines that the component type determined that the total editing time is longer than the predetermined time is a weak point for the learner. The component analysis unit 35 determines whether or not the average emotional state is "difficult" for the component types that are determined to have a total editing time of a predetermined time or less. The component analysis unit 35 determines that the component type determined that the average emotional state is "difficult" is a weak point for the learner. The component analysis unit 35 determines that the component type determined that the average emotional state is not "difficult" is not a weak point for the learner. In this way, the component analysis unit 35 analyzes whether or not the learner is a weak point for each component type.

The block structure trial / error analysis unit 36 analyzes the weaknesses of the learner regarding the block structure of the program based on the edit history information and the brain wave information, and stores the analysis result as the block structure trial / error analysis result. Store in unit 39. The block structure trial and error analysis unit 36 analyzes the frequency of generation and deletion of the block structure of the programming language.

Specifically, first, the block structure trial and error analysis unit 36 determines whether or not the edit history to be determined is input or deletion of curly braces for all the records of the edit history information based on the edit history information. Is determined. When the block structure trial and error analysis unit 36 determines that the editing history of the determination target is the input or deletion of the curly braces, the block structure trial and error analysis unit 36 calculates the number of the curly braces from the beginning of the program.

Then, the block structure trial and error analysis unit 36 determines whether or not the editing history of the determination target is the deletion of the curly braces. When the block structure trial and error analysis unit 36 determines that the editing history of the determination target is the deletion of the curly braces, the block structure trial and error analysis unit 36 calculates the time from the input of the curly braces to the deletion of the curly braces.

Then, the block structure trial and error analysis unit 36 analyzes the emotional state of all the records of the editing history information using the brain wave information.

Subsequently, the block structure trial and error analysis unit 36 determines whether or not the edit history of the determination target is input or deletion of the curly braces for all the records of the edit history information, and the edit history is the input of the curly braces. Alternatively, the total editing time from the creation of all block structures to the deletion is calculated after determining whether or not the block structure is deleted and performing the emotional state analysis process. Then, the block structure trial and error analysis unit 36 calculates the average emotional state from the creation to the deletion of all the block structures. Subsequently, the block structure trial and error analysis unit 36 determines whether or not the total editing time of the block structure is longer than the predetermined time.

When the block structure trial and error analysis unit 36 determines that the total editing time of the block structure is longer than a predetermined time, the learner determines that the block structure cannot be understood. Further, when the block structure trial and error analysis unit 36 determines that the total editing time of the block structure is equal to or less than a predetermined time, the block structure trial and error analysis unit 36 determines whether or not the average emotional state is “difficult”.

When the block structure trial and error analysis unit 36 determines that the average emotional state is "difficult", the learner determines that the block structure cannot be understood. On the other hand, when the block structure trial and error analysis unit 36 determines that the average emotional state is not "difficult", the learner determines that the block structure can be understood. In this way, the block structure trial and error analysis unit 36 analyzes the learner's weaknesses regarding the block structure of the program.

The error analysis unit 37 analyzes the weaknesses of the learner regarding the error of the program for each type of error based on the edit history information, and stores the analysis result as the error analysis result in the error analysis result storage unit 40. The error analysis unit 37 analyzes errors at the time of creating and executing a programming language.

Specifically, first, the error analysis unit 37 uses the edit history information to count the errors in the edit history information for each error type. Then, the error analysis unit 37 determines whether or not the same error is repeated within a certain period of time for each error type.

The error analysis unit 37 determines that the learner does not understand the meaning of the error with respect to the error type determined that the same error is repeated. Further, the error analysis unit 37 determines whether or not the frequency of a specific error exceeds a preset threshold value for the error type determined that the same error is not repeated. The error analysis unit 37 determines that the learner has many careless mistakes for the error type for which it is determined that the frequency of a specific error exceeds a preset threshold value. Further, the error analysis unit 37 determines that the error type for which the frequency of a specific error does not exceed a preset threshold value is a normal understanding level for the error of this error type. ..

The component analysis result storage unit 38 stores the analysis result by the component analysis unit 35. Specifically, the component analysis result storage unit 38 stores the individual component analysis result and the component analysis result.

FIG. 4 is a diagram showing an example of the data structure of the individual component analysis result. As shown in FIG. 4, the individual component analysis result 380 has items of user ID, component type, editing time, and emotional state. The component type is the type of the component of the edit history information analyzed by the component analysis unit 35 based on the edit history information. The editing time is the editing time calculated by the component analysis unit 35 based on the start time and the end time of the specified component type. The emotional state is an emotional state about the emotion during the start time and the end time analyzed by the component analysis unit 35 based on the brain wave information.

FIG. 5 is a diagram showing an example of the data structure of the component analysis result. As shown in FIG. 5, the component analysis result 381 has items of a user ID, a component type, and a determination result. The determination result is a determination result regarding whether or not the learner understands each type of component, which is determined by the component analysis unit 35 based on the editing time and the emotional state.

The block structure trial and error analysis result storage unit 39 stores the block structure trial and error analysis result. FIG. 6 is a diagram showing an example of the data structure of the block structure trial and error analysis result. As shown in FIG. 6, the block structure trial and error analysis result 391 has a user ID and a determination result item. The determination result is a determination result regarding whether or not the learner understands the block structure.

The error analysis result storage unit 40 stores the error analysis result. FIG. 7 is a diagram showing an example of the data structure of the error analysis result. As shown in FIG. 7, the error analysis result 401 has items of a user ID, an error type, and a determination result. The determination result is a determination result regarding whether or not the learner understands the error of the program.

The analysis result output unit 41 outputs the analysis result by the analysis unit 302. When the analysis result output unit 41 receives a request for providing the analysis result from the outside such as a learner or a teacher, the analysis result output unit 41 outputs the analysis result to the request source. The analysis result output unit 41 includes a component analysis result 381 stored in the component analysis result storage unit 38, a block structure trial / error analysis result 391 stored in the block structure trial / error analysis result storage unit 39, and an error analysis result storage unit. The error analysis result 401 stored in 40 is read and output as analysis information regarding the weakness of the user.

[Analysis processing]
The analysis process by the analyzer 30 according to the present embodiment will be described with reference to FIGS. 8 to 23.

FIG. 8 is a sequence diagram showing an example of the processing procedure of the edit history acquisition process. In the analysis device 30, the edit history information acquisition unit 31 acquires the edit history information from the edit history information acquisition unit 21 of the learning device 20 (step S1), and stores the edit history information in the edit history information storage unit 33 (step S1). Step S2).

FIG. 9 is a sequence diagram showing a processing procedure of brain wave information acquisition processing. In the analyzer 30, the electroencephalogram information acquisition unit 32 acquires the electroencephalogram information from the electroencephalogram information acquisition unit 22 of the learning device 20 (step S11), and stores the electroencephalogram information in the electroencephalogram information storage unit 34 (step S12).

FIG. 10 is a sequence diagram showing a processing procedure of the component analysis process. In the analyzer 30, the component analysis unit 35 reads the edit history information 331 stored in the edit history information storage unit 33 and the electroencephalogram information 341 stored in the brain wave information storage unit 34 (steps S21 and S22). ). Then, the component analysis unit 35 executes a component analysis process for analyzing the weaknesses of the learner for each component of the program (step S350), and stores the component analysis result 381 in the component analysis result storage unit 38. (Step S24).

FIG. 11 is a sequence diagram showing a processing procedure of the block structure trial and error analysis process. In the analyzer 30, the block structure trial and error analysis unit 36 reads the edit history information 331 stored in the edit history information storage unit 33 and the electroencephalogram information 341 stored in the brain wave information storage unit 34 (step S31). , S32). Then, the block structure trial and error analysis unit 36 performs a block structure trial and error analysis process for analyzing the frequency of creating and deleting the block structure of the program (step S360). Subsequently, the block structure trial and error analysis unit 36 stores the block structure trial and error analysis result 391 in the block structure trial and error analysis result storage unit 39 (step S34).

FIG. 12 is a sequence diagram showing a processing procedure of error analysis processing. In the analyzer 30, the error analysis unit 37 reads the edit history information 331 stored in the edit history information storage unit 33 (step S41). Then, the error analysis unit 37 executes an error analysis process for analyzing the error frequency of the program (step S370), and stores the error analysis result 401 in the error analysis result storage unit 40 of the auxiliary storage device 13 (step S42). ).

FIG. 13 is a sequence diagram showing a processing procedure of analysis result output processing. When the analysis result output unit 41 receives a request for providing the analysis result from the outside such as a learner or a teacher (step S51), the analysis result output unit 41 has a block structure and a component analysis result 381 stored in the component analysis result storage unit 38. The block structure trial and error analysis result 391 stored in the trial and error analysis result storage unit 39 and the error analysis result 401 stored in the error analysis result storage unit 40 are read (steps S52, S53, S54) and analyzed. Information 411 is output to the requester of the analysis result (step S55).

[Component analysis process]
Next, the processing procedure of the component analysis process (step S350) shown in FIG. 10 will be described. FIG. 14 is a flowchart showing a processing procedure of the component analysis process shown in FIG.

As shown in FIG. 14, the component analysis unit 35 performs the component type analysis process (step S3502) for each record of the edit history information 331 using the read edit history information 331, and performs the component type analysis process. The editing time is calculated from the start time and the end time of the component type specified in (step S3502) (step S3503).

The component analysis unit 35 uses the read electroencephalogram information storage unit 34 to perform an emotional state analysis process (step S3504) for the emotion between the start time and the end time of the component type, and obtains the component type and edits. Record the time and emotional state (step S3505). The processes of steps S3502 to S3505 are repeatedly executed for all the records of the edit history information 331 (steps S3501 and S3506).

Then, the component analysis unit 35 calculates the total editing time for each component type based on the information recorded in step S3505 (step S3507). Then, the component analysis unit 35 calculates the average emotional state for each component type based on the information recorded in step S3505 (step S3508).

Subsequently, the component analysis unit 35 determines whether or not the total editing time is longer than the predetermined time for each component type (step S3509). When the component analysis unit 35 determines that the total editing time is longer than the predetermined time (step S3509: Yes), the component analysis unit 35 determines that the component type is a weak point for the learner (step S3510). On the other hand, when the component analysis unit 35 determines that the total editing time is equal to or less than a predetermined time (step S3509: No), the component analysis unit 35 determines whether or not the average emotional state is “difficult” (step S3511).

When the component analysis unit 35 determines that the average emotional state is "difficult" (step S3511: Yes), the component analysis unit 35 determines that the component type is a weak point for the learner (step S3512). On the other hand, when the component analysis unit 35 determines that the average emotional state is not "difficult" (step S3511: No), the component analysis unit 35 determines that the component type is not a weak point for the learner (step S3513).

[Component type analysis processing]
Next, the processing procedure of the component type analysis processing (step S3502) shown in FIG. 14 will be described. FIG. 15 is a flowchart showing a processing procedure of the component type analysis process shown in FIG.

First, the component analysis unit 35 performs a block structure determination process (step S350201) for determining whether or not the component to be determined corresponds to the block structure. Then, the component analysis unit 35 performs the conditional branch conditional expression element determination process (step S350202) as to whether or not it corresponds to the conditional expression of the conditional branch.

Subsequently, the component analysis unit 35 performs a conditional branch block determination process (step S350203) for determining whether or not the block corresponds to the conditional branch. Then, the component analysis unit 35 performs a repeat conditional expression element determination (step S350204) as to whether or not the repeat conditional expression is applicable. Then, the component analysis unit 35 makes a repeat block determination (step S350205) as to whether or not the block corresponds to the repeat block. The component analysis unit 35 performs a calculation element determination process (step S350206) for determining whether or not the component corresponds to a calculation element. Then, the component element analysis unit 35 performs an identifier element determination process (step S350207) as to whether or not it corresponds to the identifier element.

By performing the process shown in FIG. 15, the component analysis unit 35 analyzes the components of the edit history to be determined. Subsequently, each process of the component type analysis process will be described in detail.

[Block structure judgment processing]
First, the processing procedure of the block structure determination processing (step S350201) shown in FIG. 15 will be described. FIG. 16 is a flowchart showing a processing procedure of the block structure determination process shown in FIG.

The component analysis unit 35 determines whether or not "{" has been added to the edit history of the determination target (step S35020101). When the component analysis unit 35 determines that "{" has been added to the edit history of the determination target (step S35020101: Yes), the component analysis unit 35 determines as a block structure (step S35020104). On the other hand, when the component analysis unit 35 determines that "{" is not added to the edit history of the determination target (step S35020101: No), then whether or not "}" is added to the edit history. (Step S35020102).

When the component analysis unit 35 determines that "}" has been added to the editing history (step S35020102: Yes), it determines that it is a block structure (step S35020104). If the component analysis unit 35 determines that "}" has not been added to the editing history (step S35020102: No), then the characters added or deleted from the editing history are on the right side of "{". It is determined whether or not there is, or whether or not it is inside "{" and "}" (step S35020103).

When the component analysis unit 35 determines that the character added or deleted from the editing history is on the right side of "{" or inside "{" and "}" (step S35020103: Yes). , Determined as a block structure (step S35020104). On the other hand, when the component analysis unit 35 determines that the character added or deleted from the editing history is not on the right side of "{" and is not inside "{" and "}" (step S35020103: No). The process ends as it is.

[Conditional branch conditional expression element judgment processing]
Next, the processing procedure of the conditional branch conditional element determination process (step S350202) shown in FIG. 15 will be described. FIG. 17 is a flowchart showing a processing procedure of the conditional branch conditional element determination process shown in FIG.

First, the component analysis unit 35 determines whether or not "(" is added to the edit history of the determination target and immediately before that is "if" (step S35020201). The component analysis unit 35 determines the determination target. When "(" is added to the editing history of and it is determined that "if" immediately before that (step S35020201: Yes), the determination target is determined as a conditional branch conditional expression element (step S35020204).

Further, when the component analysis unit 35 determines that "(" is not added to the edit history of the determination target or "(" immediately before the added "(" is not "if"), the determination is made (step S35020201: No). ")" Is added to the edit history of the target, and it is determined whether or not the corresponding parenthesis is "if (" (step S35020220). The component analysis unit 35 has ")" in the edit history of the determination target. When it is added and it is determined that the corresponding parenthesis is "if (" (step S3502022: Yes), the determination target is determined as a conditional branch conditional expression element (step S35020204).

When the component analysis unit 35 determines that ")" is not added to the edit history of the determination target, or the parentheses corresponding to the added ")" are not "if (" (step S3502022: No). It is determined whether the character added or deleted from the edit history is on the right side of "if (" or inside "if (" and ")" (step S35020203).

When the component analysis unit 35 determines that the character added or deleted from the edit history is on the right side of "if (" or inside "if (" and ")" (step S3502203: Yes). , The determination target is determined as a conditional branch conditional expression element (step S35020204). Further, in the component analysis unit 35, the character added or deleted from the edit history is not on the right side of "if (", and "if (" If it is determined that it is not inside "" and ")" (step S35020203: No), the process ends.

[Conditional branch block judgment processing]
Next, the processing procedure of the conditional branch block determination processing (step S350203) shown in FIG. 15 will be described. FIG. 18 is a flowchart showing a processing procedure of the conditional branch block determination process shown in FIG.

As shown in FIG. 18, the component analysis unit 35 determines that the editing history of the determination target has a block structure in the above-mentioned block structure determination process (step S350201), and the left side is a conditional branch conditional expression element. Whether or not it is determined (step S35020301). When the component analysis unit 35 determines that the edit history of the determination target has a block structure in the above-mentioned block structure determination process and determines that the left side is a conditional branch conditional expression element (step S350203001: Yes). The determination target is determined as a conditional branch block (step S35020303).

Further, when the component analysis unit 35 determines that the edit history of the determination target is not a block structure in the above-mentioned block structure determination process, or determines that the left side is not a conditional branch conditional expression element (step S350203001). : No), it is determined in the above-mentioned block structure determination process that the edit history of the determination target has a block structure, and it is determined whether or not the left side is “else” (step S35020302).

When the component analysis unit 35 determines that the edit history of the determination target has a block structure in the above-mentioned block structure determination process and determines that the left side is “else” (step S35020302: Yes), the determination target Is determined as a conditional branch block (step S35020303). When the component analysis unit 35 determines that the edit history of the determination target is not a block structure in the above-mentioned block structure determination process, or determines that the left side is not "else" (step S35020302: No). End the process.

Here, the details of the iterative conditional expression element determination process (step S350204) and the iterative block determination process (step S350205) shown in FIG. 15 are described in the above-mentioned conditional branch conditional expression element determination process (step S350202) and the conditional branch block determination process. This is the same process as that of (step S350203).

[Operation element judgment processing]
Next, the processing procedure of the arithmetic element determination processing (step S350206) shown in FIG. 15 will be described. FIG. 19 is a flowchart showing a processing procedure of the arithmetic element determination process shown in FIG.

As shown in FIG. 19, the component analysis unit 35 determines whether or not any symbol of the operator has been added to the edit history of the determination target (step S35020601). When the component analysis unit 35 determines that any symbol of the operator has been added to the editing history of the determination target (step S35020601: Yes), the component analysis unit 35 determines the determination target as an arithmetic element (step S35020603).

When the component analysis unit 35 determines that any symbol of the operator has not been added to the edit history of the determination target (step S35020601: No), parentheses are added to the edit history of the determination target, and It is determined whether the parentheses are the parentheses corresponding to the reserved word or the function (step S35020602).

When the component analysis unit 35 determines that the parentheses are added to the edit history of the determination target and the parentheses do not correspond to the reserved word or the function (step S35020602: Yes), the component analysis unit 35 determines the determination target as an arithmetic element. (Step S35020603). Further, when the component analysis unit 35 determines that the parentheses are not added to the editing history of the determination target, or the added parentheses are the parentheses corresponding to the reserved word or the function (step S35020602: No). Finish the process.

[Identifier element determination process]
Next, the processing procedure of the identifier element determination processing (step S350207) shown in FIG. 15 will be described. FIG. 20 is a flowchart showing a processing procedure of the identifier element determination process shown in FIG.

First, the component analysis unit 35 determines whether or not any symbol of the operator is added to the edit history of the determination target and immediately before that is an identifier (step S35020701). When the component analysis unit 35 determines that any symbol of the operator is added to the editing history of the determination target and immediately before that is an identifier (step S35020701: Yes), the component analysis unit 35 determines the determination target as an identifier element (step S35020701: Yes). Step S35020704).

When the component analysis unit 35 determines that any symbol of the operator is not added to the edit history of the determination target, or that the symbol immediately before the added symbol is not an identifier (step S35020701: No), the determination target is determined. Right parentheses are added to the edit history, and it is determined whether or not the identifier is immediately before the right parenthesis (step S35020702).

When the right parenthesis is added to the editing history of the determination target and the component analysis unit 35 determines that the identifier is immediately preceding it (step S35020702: Yes), the component analysis unit 35 determines the determination target as an identifier element (step S35020704).

When the component analysis unit 35 determines that the right parenthesis is not added to the edit history of the determination target, or that the part immediately before the added parenthesis is not an identifier (step S35020702: No), the semicolon is added to the edit history of the determination target. And a colon are added, and it is determined whether or not immediately before that is an identifier (step S35020703). When the component analysis unit 35 determines that a semicolon or a colon is added to the editing history of the determination target and immediately before that is an identifier (step S35020703: Yes), the component analysis unit 35 determines the determination target as an identifier element (step S35020704). When the component analysis unit 35 determines that the semicolon or colon is not added to the editing history of the determination target, or that the semicolon or colon immediately before the added semicolon or colon is not an identifier (step S35020703: No), the component analysis unit 35 ends the process.

[Affect state analysis process]
Next, the processing procedure of the emotional state analysis processing (step S3504) shown in FIG. 14 will be described. FIG. 21 is a flowchart showing a processing procedure of the emotional state analysis process shown in FIG.

First, the component analysis unit 35 determines whether or not the electroencephalogram information at the time to be analyzed is larger than a predetermined first threshold value (step S350401). When the component analysis unit 35 determines that the electroencephalogram information at the time to be analyzed is larger than the first threshold value (step S350401: Yes), the component analysis unit 35 determines that the emotional state is "difficult" (step S350403).

When the component analysis unit 35 determines that the electroencephalogram information at the time to be analyzed is equal to or less than the first threshold value (step S350401: No), the electroencephalogram information at the time to be analyzed is from a predetermined second threshold value. It is determined whether or not it is small (step S350402).

When the component analysis unit 35 determines that the brain wave information at the time to be analyzed is smaller than the second threshold value (step S350402: Yes), the component analysis unit 35 determines that the emotional state is "easy" (step S350404). Further, when the component analysis unit 35 determines that the electroencephalogram information at the time to be analyzed is equal to or higher than a predetermined second threshold value (step S350402: No), the component analysis unit 35 determines that the emotional state is "normal". (Step S350405).

The component analysis unit 35 obtains the individual component analysis result 380 exemplified in FIG. 4 by executing each process shown in FIGS. 14 to 21, and then obtains the component analysis result 381 exemplified in FIG. obtain.

[Block structure trial and error analysis processing]
Next, the processing procedure of the block structure trial and error analysis processing (step S360) shown in FIG. 11 will be described. FIG. 22 is a flowchart showing a processing procedure of the block structure trial and error analysis process shown in FIG.

The block structure trial / error analysis unit 36 reads the edit history information 331 stored in the edit history information storage unit 33, and the edit history to be determined is input or deleted in curly braces for each record of the edit history information 331. It is determined whether or not (step S3602). When the block structure trial and error analysis unit 36 determines that the edit history of the determination target is the input or deletion of the curly braces (step S3602: Yes), the curly braces in the edit history is the number of curly braces from the beginning of the program. Is calculated (step S3603).

When the block structure trial and error analysis unit 36 determines that the edit history of the determination target is not the input or deletion of the curly braces (step S3602: No), or after the processing of step S3603, the edit history of the determination target is the curly braces. It is determined whether or not the deletion is performed (step S3604). When the block structure trial and error analysis unit 36 determines that the editing history of the determination target is the deletion of the curly braces (step S3604: Yes), the block structure trial and error analysis unit 36 calculates the time from the input of the curly braces to the deletion (step S3604: Yes). Step S3605).

When the block structure trial and error analysis unit 36 determines that the editing history of the determination target is not the deletion of the curly braces (step S3604: No), or after the processing of step S3605, the emotional state analysis processing (step S3606) for the target record. I do. The block structure trial and error analysis unit 36 performs the emotional state analysis process by executing the same processing procedure as the emotional state analysis process (step S3504) in the component analysis process (step S350). The processes of steps S3602 to S3606 are repeatedly executed for all the records of the edit history information 331 (steps S3601 and S3607).

Then, the block structure trial and error analysis unit 36 calculates the total editing time from the creation to the deletion of all the block structures (step S3608). Next, the block structure trial and error analysis unit 36 calculates the average emotional state from the creation to the deletion of all the block structures (step S3609).

Subsequently, the block structure trial and error analysis unit 36 determines whether or not the total editing time of the block structure is longer than the predetermined time (step S3610). When the block structure trial and error analysis unit 36 determines that the total editing time of the block structure is longer than a predetermined time (step S3610: Yes), it determines that the learner does not understand the block structure (step S3611).

Further, when the block structure trial and error analysis unit 36 determines that the total editing time of the block structure is equal to or less than a predetermined time (step S3610: No), the block structure trial and error analysis unit 36 determines whether or not the average emotional state is “difficult” (step S3610: No). Step S3612). When the block structure trial and error analysis unit 36 determines that the average emotional state is "difficult" (step S3612: Yes), it determines that the learner does not understand the block structure (step S3611). On the other hand, when the block structure trial and error analysis unit 36 determines whether the average emotional state is “difficult” (step S3612: No), it determines that the learner understands the block structure (step S3613).

The block structure trial and error analysis unit 36 executes the block structure trial and error analysis process shown in FIG. 22 to obtain the block structure trial and error analysis result 391 illustrated in FIG.

[Error analysis processing]
Next, the processing procedure of the error analysis processing (step S370) shown in FIG. 12 will be described. FIG. 23 is a flowchart showing a processing procedure of the error analysis process shown in FIG.

The error analysis unit 37 reads the edit history information 331 stored in the edit history information storage unit 33, and counts the errors in the edit history information 331 for each error type (step S3702). The process of step S3702 is repeatedly executed for all the records of the edit history information 331 (steps S3701 and S3703).

The error analysis unit 37 determines whether or not the same error is repeated within a fixed time for each error type for the counted error (step S3704). When the error analysis unit 37 determines that the same error is repeated within a certain period of time (step S3704: Yes), the learner determines that the meaning of the type of error is not understood (step S3706). .. Further, when the error analysis unit 37 determines that the same error is not repeated within a certain period of time (step S3704: No), whether or not the frequency of a specific error exceeds a preset threshold value for the error type. (Step S3705).

When the error analysis unit 37 determines that the frequency of a specific error exceeds a preset threshold value (step S3705: Yes), the learner determines that there are many careless mistakes of the error (step S3707). Further, when the error analysis unit 37 determines that the frequency of a specific error does not exceed a preset threshold value (step S3705: No), the learner has a normal understanding of the error of this error type. It is determined that there is (step S3708).

[Effect of embodiment]
As described above, in the analysis system according to the embodiment, the analysis device 30 analyzes the learner's weaknesses with respect to each component of the program based on the editing history information and the brain wave information. Further, the analyzer 30 analyzes the learner's weaknesses regarding the block structure of the program based on the editing history information and the brain wave information. Then, the analyzer 30 analyzes the weaknesses of the learner regarding the error of the program for each type of error based on the editing history information.

Therefore, according to the present embodiment, by performing the analysis process combining the edit history information and the brain wave information, it is not necessary to compare with the correct answer program, and a large amount of backtracking is not performed at the time of analysis. Therefore, the analysis process can be executed efficiently. Then, according to the present embodiment, by executing the processes of FIGS. 8 to 22, the learner's weaknesses such as the learner's stumbling block and the cause of lack of understanding in the programming learning process are automatically analyzed. , Can be provided to learners, etc.

[Modification example]
For example, the component analysis process (step S350), the block structure trial and error analysis process (step S360), and the error analysis process (step S370) do not have to be performed sequentially, and may be performed independently.

Further, the component analysis process (step S350), the block structure trial and error analysis process (step S360), and the error analysis process (step S370) may be performed sequentially at the time of learning after the learning is completed.

That is, in the present embodiment, the component analysis process (step S350), the block structure trial and error analysis process (step S360), and the error analysis process (step S370) are the edit history information after the learning is completed once. An example of analysis based on the data stored in the storage unit 33 and the brain wave information storage unit 34 has been described. Not limited to this, the component analysis process (step S350), the block structure trial and error analysis process (step S360), and the error analysis process (step S370) are performed from the edit history information acquisition unit 31 and the brain wave information acquisition unit 32. Data may be received directly and analyzed in real time during learning. In that case, the edit history information storage unit 33 and the brain wave information storage unit 34 become unnecessary.

Further, in the present embodiment, the component analysis process (step S350), the block structure trial and error analysis process (step S360), and the error analysis process (step S370) are described in parallel. Not limited to this, the process to be executed in the present embodiment may be only the component analysis process (step S350), the block structure trial and error analysis process (step S360), or the error analysis process (step S370). It may be only, or it may be only an arbitrary combination of processes that combines any of these three processes.

Further, in the present embodiment, as the component type analysis process (step S3502), the block structure determination process (step S350201), the conditional branch conditional element determination process (step S350202), and the conditional branch block determination process (step S350203) are performed. ), Repeated conditional expression element determination process (step S350204), iterative block determination process (step S350205), arithmetic element determination process (step S350206), and identifier element determination process (step S350207). The process (step S350) and the block structure trial / error analysis process (step S360) are executed, but the function block may be determined, or the assignment or reference of a variable or array may be determined. The analyzer 30 may add or delete the types of components according to the request of the user who uses the analysis result.

Further, in the present embodiment, in each analysis of the component type analysis process (step S3502), symbols such as "{" and "}" are used for determination as block boundaries, but "{" and " In the case of a programming language that does not use} ”, it can be applied by determining the boundaries peculiar to that language (for example, indentation in the Python language).

Specifically, the component analysis unit 35 determines the start parenthesis by checking whether or not it is the beginning of a line when the space key or the tab key is pressed. Then, in the detection of the closing parenthesis, the component analysis unit 35 needs to determine whether or not the beginning of the line following the return key is indented, so that the return key is pressed and the next line is indented. Judgment is made by checking two logs so that the logs are not spaces or indents.

Further, in the present embodiment, the component analysis unit 35 makes a three-step determination such as "difficult", "easy", and "normal" in the emotional state analysis process (step S3504), but it is "slightly difficult". You may make a judgment in more categories as needed, such as "" or "somewhat easy".

Further, in the determination (step S3509) of whether or not the total editing time of the component analysis process (step S350) is longer than the predetermined time, the component analysis unit 35 exceeds the preset predetermined time as described above. In addition to determining whether or not it is possible to make a statistical determination, such as when it takes more than twice the standard deviation with respect to the average editing time of the entire learner.

Further, the component analysis unit 35 determines whether or not the emotional state of the component analysis process (step S350) is difficult (step S3511), and the total editing time of a certain component is below a certain threshold value. Moreover, when it is emotionally determined to be "difficult", it may be determined to be "difficult". Further, in step S3511, the component analysis unit 35 has an editing time of the target learner less than 1 times the standard deviation with respect to the average editing time of the entire learner, and is emotionally “difficult”. If it is determined to be "difficult", it may be determined to be "difficult".

Further, in the determination (step S3610) of whether or not the block structure mechanism error analysis (step S360) is longer than the predetermined time, the block structure trial and error analysis unit 36 determines whether or not the preset value is exceeded as described above. In addition to making a judgment, it can also be statistically determined, such as when it takes more than twice the standard deviation for the average editing time of the entire learner.

Further, the block structure trial and error analysis unit 36 sets a certain threshold value for the total editing time of a certain component in the determination (step S3612) of whether or not the emotional state of the block structure mechanism error analysis process (step S360) is difficult. It may be the case that the value is lower than the above and it is emotionally judged to be "difficult". In addition, when the editing time of the target learner is less than 1 times the standard deviation with respect to the average editing time of the entire block structure mechanism error analysis learner, and it is emotionally judged to be "difficult". It may be determined to be "difficult".

Further, in the present embodiment, the threshold value may be set in advance in the determination (step S3707) of whether or not the frequency of a specific error in the error analysis process (step S370) exceeds the threshold value, or the learner as a whole may set the threshold value in advance. It may be dynamically determined by the deviation from the average number of times.

Further, in the present embodiment, in the component analysis process (step S350), the component is determined for each record of the edit history information 331, but a plurality of components may be determined for each record. be. For example, when the component analysis unit 35 has a conditional branch block in the repeating block, the record is determined as a component of both the repeating block and the conditional branch block. In this case, the component analysis result 381 will be recorded as a plurality of records.

[System configuration, etc.]
Each component of each of the illustrated devices is functional and conceptual, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of them may be functionally or physically distributed / physically in arbitrary units according to various loads and usage conditions. Can be integrated and configured. Further, each processing function performed by each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by wired logic. The learning device 20 and the analysis device 30 according to the present embodiment can also be realized by a computer and a program, and the program can be recorded on a recording medium or provided through a network.

Further, among the processes described in the present embodiment, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or part of it can be done automatically by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified.

[program]
FIG. 24 is a diagram showing an example of a computer in which the learning device 20 and the analysis device 30 are realized by executing the program. The computer 1000 has, for example, a memory 1010 and a CPU 1020. The computer 1000 also has a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. Each of these parts is connected by a bus 1080.

Memory 1010 includes ROM 1011 and RAM 1012. The ROM 1011 stores, for example, a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1090. The disk drive interface 1040 is connected to the disk drive 1100. For example, a removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to, for example, a mouse 1110 and a keyboard 1120. The video adapter 1060 is connected to, for example, the display 1130. For example, these storage media are divided into a main storage device and an auxiliary storage device. The main memory stores the program. In addition, the auxiliary storage device can write and read information, and stores edit history information, electroencephalogram information, component analysis information, block structure trial and error analysis results, and error analysis results acquired by executing the process.

The hard disk drive 1090 stores, for example, the OS 1091, the application program 1092, the program module 1093, and the program data 1094. That is, the program that defines each process of the learning device 20 and the analysis device 30 is implemented as a program module 1093 in which a code that can be executed by the computer 1000 is described. The program module 1093 is stored in, for example, the hard disk drive 1090. For example, the program module 1093 for executing the same processing as the functional configuration in the learning device 20 and the analysis device 30 is stored in the hard disk drive 1090. The hard disk drive 1090 may be replaced by an SSD (step Solid State Drive).

Further, the setting data used in the processing of the above-described embodiment is stored as program data 1094 in, for example, a memory 1010 or a hard disk drive 1090. Then, the CPU 1020 reads the program module 1093 and the program data 1094 stored in the memory 1010 and the hard disk drive 1090 into the RAM 1012 and executes them as needed.

The program module 1093 and the program data 1094 are not limited to those stored in the hard disk drive 1090, and may be stored in, for example, a removable storage medium and read out by the CPU 1020 via the disk drive 1100 or the like. Alternatively, the program module 1093 and the program data 1094 may be stored in another computer connected via a network (LAN, WAN (Wide Area Network), etc.). Then, the program module 1093 and the program data 1094 may be read from another computer by the CPU 1020 via the network interface 1070.

Although the embodiment to which the invention made by the present inventor is applied has been described above, the present invention is not limited by the description and the drawings which form a part of the disclosure of the present invention according to the present embodiment. That is, all other embodiments, examples, operational techniques, and the like made by those skilled in the art based on the present embodiment are included in the scope of the present invention.

1 Analysis system 20 Learning device 21 Editing history information acquisition unit 22 Brain wave information acquisition unit 30 Analysis device 31 Editing history information acquisition unit 32 Brain wave information acquisition unit 33 Editing history information storage unit 34 Brain wave information storage unit 35 Component analysis unit 36 Block structure Trial and error analysis unit 37 Error analysis unit 38 Component analysis result storage unit 39 Block structure Trial and error analysis result storage unit 40 Error analysis result storage unit 41 Analysis result output unit

Claims (11)

An acquisition unit that acquires edit history information in the process of creating a program by a learner and brain wave information indicating the state of the learner's brain wave.
An analysis unit that analyzes the learner's weaknesses with respect to each component of the program based on the editing history information and the brain wave information.
An output unit that outputs the analysis result by the analysis unit as a configuration information analysis result,
An analytical system characterized by having. An acquisition unit that acquires edit history information in the process of creating a program by a learner and brain wave information indicating the state of the learner's brain wave.
An analysis unit that analyzes the learner's weaknesses regarding the block structure of the program based on the editing history information and the brain wave information.
An output unit that outputs the analysis result by the analysis unit as a block structure trial and error analysis result,
An analytical system characterized by having. The acquisition unit acquires edit history information in the process of creating a program by the learner.
Based on the editing history information, the analysis unit analyzes the weaknesses of the learner regarding the error of the program for each type of error.
The analysis system according to claim 1 or 2, wherein the output unit outputs an analysis result by the analysis unit as an error analysis result. An edit history information acquisition unit that acquires the edit history information and outputs the edit history information to the acquisition unit during learning by the learner.
At the time of learning of the learner, the brain wave information acquisition unit that acquires the brain wave information of the learner and provides the brain wave information to the acquisition unit, and
The analysis system according to any one of claims 1 to 3, further comprising a learning device having the above. The acquisition unit includes an edit history information storage unit that stores the edit history information acquired from the edit history information acquisition unit in the learning device.
The acquisition unit includes an electroencephalogram information storage unit that stores the electroencephalogram information acquired from the electroencephalogram information acquisition unit in the learning device.
4. The analysis system according to claim 4. An analytical method performed by an analyzer,
An acquisition process for acquiring edit history information in the process of creating a program by a learner and brain wave information indicating the state of the learner's brain waves.
An analysis process for analyzing the learner's weaknesses with respect to each component of the program based on the editing history information and the brain wave information.
An output process that outputs the analysis result as a configuration information analysis result,
An analysis method characterized by including. The analysis step is
A first analysis step of analyzing component types for all records of the edit history information using the edit history information, and
The first calculation step of calculating the editing time from the start time and the end time of the component type specified in the step of analyzing the component type, and
A second analysis step of analyzing an emotional state showing the state of the brain wave information during the start time and the end time by a step evaluation using the brain wave information, and a second analysis step.
The total editing time for each component type is calculated based on the component type, the editing time, and the emotional state in all the records of the editing history information, and the same component type is calculated for each component type. The second calculation step of acquiring the average emotional state, which is the average of the graded evaluations for the emotional state in the record , and
A first determination step for determining whether or not the total editing time is longer than a predetermined time for each component type, and
In the first determination step, the component type determined that the total editing time is longer than the predetermined time is the weak point for the learner, and the second determination step.
A third determination step for determining whether or not the average emotional state is "difficult" for a component type determined to have a total editing time of a predetermined time or less in the first determination step.
The component type in which the average emotional state is determined to be "difficult" in the third determination step is the fourth determination step in which the learner is determined to be a weak point.
In the third determination step, the component type determined that the average emotional state is not "difficult" is not a weak point for the learner, and the fifth determination step.
Including
In the third determination step, when the brain wave information at the time of the analysis target is larger than the predetermined first threshold value, the emotional state is determined to be "difficult", and the brain wave information at the time of the analysis target is obtained. When it is equal to or less than the first threshold value and smaller than the predetermined second threshold value, the emotional state is determined to be "easy", and the brain wave information at the time of the analysis target is the first threshold value. The analysis method according to claim 6, wherein the analysis method is as follows, and when it is determined that the threshold value is equal to or higher than the second threshold value, the emotional state is determined to be "normal" . An analytical method performed by an analyzer,
An acquisition process for acquiring edit history information in the process of creating a program by a learner and brain wave information indicating the state of the learner's brain waves.
An analysis step for analyzing the learner's weaknesses regarding the block structure of the program based on the editing history information and the brain wave information.
An output process that outputs the analysis results as block structure trial and error analysis results,
An analysis method characterized by including. The analysis step is
Based on the edit history information, the first determination step of determining whether or not the edit history to be determined is the input or deletion of curly braces for all the records of the edit history information.
When it is determined in the first determination step that the editing history of the determination target is the input or deletion of the curly braces, the first is to calculate the number of the curly braces from the beginning of the program. Calculation process and
A second determination step of determining whether or not the editing history of the determination target is the deletion of curly braces, and
When it is determined in the second determination step that the editing history of the determination target is the deletion of the curly braces, the second calculation step of calculating the time from the input of the curly braces to the deletion of the curly braces,
Using the electroencephalogram information, a first analysis step of analyzing an emotional state showing the state of the electroencephalogram information at the time of analysis for all records of the edit history information in a stepwise evaluation, and
After the first determination step, the second determination step, and the first analysis step are completed for all the records of the edit history information, the total edit time from the creation to the deletion of all the block structures is set. The third calculation process to obtain the average emotional state, which is the average of the stage evaluation of the emotional state for each record from the creation to the deletion of all the block structures,
A third determination step of determining whether or not the total editing time of the block structure is longer than a predetermined time, and
When it is determined in the third determination step that the total editing time of the block structure is longer than a predetermined time, the learner determines that the block structure is not understood, and the fourth determination step.
When it is determined in the third determination step that the total editing time of the block structure is not more than a predetermined time, the fifth determination step of determining whether or not the average emotional state is "difficult" is used.
When the average emotional state is determined to be "difficult" in the fifth determination step, the sixth determination step of determining that the learner does not understand the block structure, and
When the average emotional state is not determined to be "difficult" in the fifth determination step, the seventh determination step of determining that the learner understands the block structure, and
Including
In the fifth determination step, when the brain wave information at the time of the analysis target is larger than the predetermined first threshold value, the emotional state is determined to be "difficult", and the brain wave information at the time of the analysis target is obtained. When it is equal to or less than the first threshold value and smaller than the predetermined second threshold value, the emotional state is determined to be "easy", and the brain wave information at the time of the analysis target is the first threshold value. The analysis method according to claim 8, wherein the analysis method is as follows, and when it is determined that the threshold value is equal to or higher than the second threshold value, the emotional state is determined to be "normal" . The acquisition process includes an acquisition process for acquiring edit history information in the process of creating a program by the learner.
The analysis step analyzes the weaknesses of the learner regarding the error of the program for each type of error based on the editing history information.
The output process includes an output process that outputs the analysis result as an error analysis result.
The analysis method according to claim 6 or 8, wherein the analysis method comprises. The analysis step is
A process of counting errors in the edit history information for each error type using the edit history information, and
With respect to the counted errors, the first determination step of determining whether or not the same error is repeated within a certain period of time for each error type, and
Regarding the error type in which the same error is determined to be repeated in the first determination step, the second determination step in which the learner determines that the meaning of the error is not understood, and the second determination step.
A third determination step of determining whether or not the frequency of a specific error exceeds a preset threshold value for an error type determined that the same error is not repeated in the first determination step.
With respect to the error type in which the frequency of the specific error is determined to exceed a preset threshold value in the third determination step, the learner determines that there are many careless mistakes of the error, and the fourth determination step.
Regarding the error type in which the frequency of the specific error is determined not to exceed the preset threshold value in the third determination step, the learner has a normal understanding of the error of the error type. The fifth determination step for determination and
The analysis method according to claim 10, wherein the analysis method is included.

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