Information processing device, information processing method, and program

Abstract

To provide an information processing apparatus, or the like, configured to check validity of a comment of an instructor.SOLUTION: An information processing apparatus 1 includes: a receiving unit which receives time-series learning status data indicating learning status of a learner, and a comment of an instructor for the learning status data; and an output unit which inputs, when time-series learning status data for each predetermined period and comments for the learning status data are input, the learning status data and the comment received by the receiving unit to a first learning model trained to estimate validity of the comments, and outputs an estimation result obtained by estimating the validity of the comments.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] In recent years, systems for providing learning support to learners have been developed. For example, Patent Document 1 discloses a group education method using a computer system that allows a teacher or a corrector to monitor the answering status of students' assignments and provide comments on the answers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2015-018096 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the invention of Patent Document 1 cannot check the validity of the comments of an instructor (for example, a teacher or a corrector).

[0005] In one aspect, an object of the present invention is to provide an information processing device or the like capable of checking the validity of an instructor's comments. [Means for solving the problem]

[0006] An information processing device according to one aspect includes a reception unit that receives time-series learning status data indicating a learner's learning status and an instructor's comments on the learning status data, and an output unit that inputs the learning status data and comments received by the reception unit into a first learning model that has been trained to estimate the validity of the comments when time-series learning status data for a specified period and comments on the learning status data are input, and outputs an estimation result that estimates the validity of the comments. Effect of the Invention

[0007] In one aspect, it allows a check of the validity of the instructor's comments. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram showing an overview of a comment check system. [Diagram 2] FIG. 2 is a block diagram showing an example of the configuration of a server. [Diagram 3] FIG. 11 is an explanatory diagram showing an example of a record layout of a training data DB. [Figure 4] 1 is an explanatory diagram showing an example of a record layout of a learning model management DB and a learner DB. [Diagram 5] FIG. 13 is an explanatory diagram illustrating an example of a record layout of a comment DB. [Figure 6] FIG. 2 is a block diagram showing an example of the configuration of a learner terminal. [Figure 7] 3 is a block diagram showing a configuration example of an instructor terminal; FIG. [Figure 8] FIG. 13 is an explanatory diagram of a validity estimation model. [Figure 9] 13 is a flowchart showing a procedure for generating a validity estimation model. [Figure 10] FIG. 13 is an explanatory diagram showing an example of a display screen of an estimation result of the validity of a comment. [Figure 11] 13 is a flowchart showing a processing procedure for performing a process of estimating the validity of a comment. [Figure 12] 13 is a flowchart showing a processing procedure for performing a process of estimating the validity of a comment. [Figure 13] FIG. 11 is a block diagram showing an example of the configuration of a server according to the second embodiment. [Figure 14] FIG. 11 is an explanatory diagram showing an example of a record layout of a training data DB in the second embodiment. [Figure 15] FIG. 1 is an explanatory diagram regarding a comment estimation model. [Figure 16]FIG. 13 is an explanatory diagram showing an example of a display screen of a comment estimation result. [Figure 17] 13 is a flowchart showing a processing procedure for performing a comment generation process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the present invention will be described in detail with reference to the drawings showing embodiments thereof.

[0010] (Embodiment 1) The first embodiment relates to a form in which the validity of an instructor's comment is estimated using artificial intelligence (AI) based on learning situation data indicating a learner's learning situation and the instructor's comment on the learning situation data. Fig. 1 is an explanatory diagram showing an overview of a comment check system. The system of this embodiment includes an information processing device 1, an information processing terminal 2, and an information processing terminal 3, and each device transmits and receives information via a network N such as the Internet.

[0011] The information processing device 1 is an information processing device that processes, stores, and transmits / receives various types of information. The information processing device 1 is, for example, a server device, a personal computer, or a general-purpose tablet PC (personal computer). In this embodiment, the information processing device 1 is a server device, and will be referred to as server 1 below for simplicity.

[0012] The information processing terminal 2 is a terminal device that receives and transmits learning situation data of learners, and receives and displays comments from instructors. The information processing terminal 2 is an information processing device such as a smartphone, a mobile phone, a wearable device such as Apple Watch (registered trademark), a tablet, a personal computer terminal, etc. For the sake of simplicity, the information processing terminal 2 will be read as learner terminal 2 below.

[0013] The information processing terminal 3 is a terminal device that receives and transmits comments from an instructor on the learning situation data of a learner, and receives and displays the estimation result of the validity of the comments. The information processing terminal 3 is an information processing device such as a smartphone, a mobile phone, a wearable device such as an Apple Watch, a tablet, a personal computer terminal, etc. For the sake of simplicity, the information processing terminal 3 will be read as an instructor terminal 3 below.

[0014] The server 1 according to the present embodiment acquires time-series learning status data indicating the learning status of a learner and the instructor's comments on the learning status data. The server 1 inputs the acquired learning status data and the instructor's comments into a learning model that has been trained to estimate the validity of the comments when time-series learning status data for a predetermined period and comments on the learning status data are input, and outputs an estimation result of the validity of the comments. The learning model will be described later.

[0015] 2 is a block diagram showing an example of the configuration of the server 1. The server 1 includes a control unit 11, a storage unit 12, a communication unit 13, an input unit 14, a display unit 15, a reading unit 16, and a large-capacity storage unit 17. Each component is connected by a bus B.

[0016] The control unit 11 includes an arithmetic processing device such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), and a GPU (Graphics Processing Unit), and performs various information processing, control processing, and the like related to the server 1 by reading and executing a control program 1P stored in the storage unit 12. The control program 1P can be deployed so as to be executed on a single computer or at one site, or distributed across multiple sites and on multiple computers interconnected by a communication network. Although the control unit 11 is described in FIG. 2 as being a single processor, it may be a multiprocessor.

[0017] The storage unit 12 includes memory elements such as RAM (Random Access Memory) and ROM (Read Only Memory), and stores the control program 1P or data required for the control unit 11 to execute processing. The storage unit 12 also temporarily stores data required for the control unit 11 to execute arithmetic processing. The communication unit 13 is a communication module for performing processing related to communication, and transmits and receives information between the learner terminal 2 and the instructor terminal 3 via the network N.

[0018] The input unit 14 is an input device such as a mouse, a keyboard, a touch panel, or a button, and outputs received operation information to the control unit 11. The display unit 15 is a liquid crystal display, an organic EL (electroluminescence) display, or the like, and displays various information according to instructions from the control unit 11.

[0019] The reading unit 16 reads the portable storage medium 1a including a CD (Compact Disc)-ROM or a DVD (Digital Versatile Disc)-ROM. The control unit 11 may read the control program 1P from the portable storage medium 1a via the reading unit 16 and store it in the mass storage unit 17. The control unit 11 may also download the control program 1P from another computer via a network N or the like and store it in the mass storage unit 17. Furthermore, the control unit 11 may read the control program 1P from the semiconductor memory 1b.

[0020] The mass storage unit 17 includes a recording medium such as a hard disk drive (HDD) or a solid state drive (SSD). The mass storage unit 17 includes a validity estimation model (first learning model) 171, a training data DB (database) 172, a learning model management DB 173, a learner DB 174, and a comment DB 175.

[0021] The validity estimation model 171 is an estimator that estimates the validity of a comment based on the learning situation data of a learner and a comment by an instructor on the learning situation data, and is a trained learning model generated by machine learning. The training data DB 172 stores training data for constructing (creating) the validity estimation model 171. The learning model management DB 173 stores information related to the trained validity estimation model 171. The learner DB 174 stores information related to learners. The comment DB 175 stores comments that can be used by an instructor.

[0022] In this embodiment, the storage unit 12 and the large-capacity storage unit 17 may be configured as an integrated storage device. The large-capacity storage unit 17 may be configured with a plurality of storage devices. Furthermore, the large-capacity storage unit 17 may be an external storage device connected to the server 1.

[0023] The server 1 may be executed by a single computer, or may be executed in a distributed manner on a plurality of computers, or may be executed in a distributed manner on virtual machines.

[0024] 3 is an explanatory diagram showing an example of a record layout of the training data DB 172. The training data DB 172 includes a training ID column, an input column, an output column, and a personality type column.

[0025] The training ID column stores a unique ID of the training data to identify each training data. The input column includes a learning status column and a comment column. The learning status column stores time-series learning status data indicating the learning status of the learner. The learning status data is a numerical value quantified by the learner's learning amount, academic ability deviation value, score, or a combination of these. The learning amount is, for example, the number of learning assignments within a specified period (e.g., one month), or the learning time within a specified period. For example, when using a learning amount that combines the number of assignments and the learning time, the learning amount for 10 questions is set to one hour of learning time, and the learning status data becomes 10 (10 x 1). The learning status data may be quantified based on any calculation formula.

[0026] The comment column stores the instructor's comments on the learning situation data. The output column stores information indicating the validity of the comments. The personality type column stores the personality type of the learner. Personality types will be described later.

[0027] FIG. 4 is an explanatory diagram showing an example of the record layout of the learning model management DB 173 and the learner DB 174. As shown in FIG. The learning model management DB173 includes a model ID column, a learning model column, a type column, and a generation date and time column. The model ID column stores a uniquely specified ID of the learning model to identify each trained learning model. The learning model column stores a file of the trained learning model. The type column stores the type of learning model. The type column stores, for example, the type of learning model generated based on the learner's personality type. For example, the type column stores "personality type A model" or "personality type B model", etc. The generation date and time column stores date and time information when the learning model was generated.

[0028] The learner DB 174 includes a learner ID column and a personality type column. The learner ID column stores a unique learner ID to identify each learner. The personality type column stores a personality type indicating the personality of the learner. Regarding the personality type, for example, a personality type diagnosis is performed on each learner to obtain the personality type of each learner, and the obtained personality type is registered in the learner DB 174 in association with the learner ID.

[0029] Regarding the diagnosis of the personality type of the learners, for example, a personality diagnosis method called Ecogram may be used to calculate scores of personality traits such as "strictness", "kindness", "calmness", "free-spiritedness", and "obedience", and based on the calculated scores, it may be diagnosed which personality trait applies most to each learner. Alternatively, a personality diagnosis method called OK-gram may be used to calculate scores of personality traits such as "negativity towards others", "positivity towards others", "self-negativity", and "self-affirmation", and based on the calculated scores, it may be diagnosed which personality trait applies most to each learner. Furthermore, for example, several questions may be asked of each learner in advance, and information obtained based on the answers to the questions may be analyzed to diagnose which personality trait each learner has. For example, personality types include "type that grows when praised" and "type that grows when strict", etc.

[0030] 5 is an explanatory diagram showing an example of a record layout of the comment DB 175. The comment DB 175 includes a comment ID column and a comment column. The comment ID column stores a unique comment ID for identifying each comment. The comment column stores the content of the instructor's comment.

[0031] The above-mentioned storage formats of each DB are merely examples, and other storage formats may be used as long as the relationships between the data are maintained.

[0032] 6 is a block diagram showing an example of the configuration of the learner terminal 2. The learner terminal 2 includes a control unit 21, a storage unit 22, a communication unit 23, an input unit 24, and a display unit 25. Each component is connected by a bus B.

[0033] The control unit 21 includes an arithmetic processing unit such as a CPU, MPU, etc., and performs various information processing, control processing, etc. related to the learner terminal 2 by reading and executing the control program 2P stored in the storage unit 22. Note that, although the control unit 21 is described as a single processor in Fig. 6, it may be a multiprocessor. The storage unit 22 includes memory elements such as RAM, ROM, etc., and stores the control program 2P or data, etc., required for the control unit 21 to execute processing. The storage unit 22 also temporarily stores data, etc., required for the control unit 21 to execute arithmetic processing.

[0034] The communication unit 23 is a communication module for performing processing related to communication, and transmits and receives information to and from the server 1 via the network N. The input unit 24 may be a keyboard, a mouse, or a touch panel integrated with the display unit 25. The display unit 25 is a liquid crystal display, an organic EL display, or the like, and displays various information according to instructions from the control unit 21.

[0035] 7 is a block diagram showing an example of the configuration of the instructor terminal 3. The instructor terminal 3 includes a control unit 31, a storage unit 32, a communication unit 33, an input unit 34, and a display unit 35. Each component is connected by a bus B.

[0036] The control unit 31 includes an arithmetic processing unit such as a CPU, MPU, etc., and performs various information processing, control processing, etc. related to the instructor terminal 3 by reading and executing the control program 3P stored in the storage unit 32. Note that, although the control unit 31 is described as a single processor in FIG. 7, it may be a multiprocessor. The storage unit 32 includes memory elements such as RAM, ROM, etc., and stores the control program 3P or data, etc., required for the control unit 31 to execute processing. The storage unit 32 also temporarily stores data, etc., required for the control unit 31 to execute arithmetic processing.

[0037] The communication unit 33 is a communication module for performing processing related to communication, and transmits and receives information to and from the server 1 via the network N. The input unit 34 may be a keyboard, a mouse, or a touch panel integrated with the display unit 35. The display unit 35 is a liquid crystal display, an organic EL display, or the like, and displays various information according to instructions from the control unit 31.

[0038] Next, a process of estimating the validity of the instructor's comment based on the time-series learning situation data of the learner and the instructor's comment on the learning situation data will be described. In this embodiment, the validity estimation model 171 is used to estimate the validity of the comment and output the estimation result.

[0039] 8 is an explanatory diagram of the validity estimation model 171. In this embodiment, the server 1 performs machine learning to learn the relationship between the time-series learning situation data of the learner, the instructor's comments on the learning situation data, and the validity of the comments, and generates (creates) the validity estimation model 171.

[0040] For example, the validity estimation model 171 is a neural network generated by deep learning, and is an attention model, which is a type of recurrent neural network (RNN).

[0041] The attention model-related validity estimation model 171 is classified into a network that functions as an Encoder and a network that functions as a Decoder, as shown in FIG.<EOS(End of String)> is a reserved word that indicates the end of data during training or estimation processing.

[0042] The encoder is composed of multiple neurons called LSTM (Long-Short Term Memory) blocks. The encoder accepts input of learning situation data for a predetermined period (e.g., one month or one week) and comments on the learning situation data, and performs calculations in the LSTM block. The LSTM block performs calculations while referring to the calculation results in the previous LSTM block (neuron), i.e., the calculation results based on the learning situation data and comments that appear immediately before. In this way, the validity estimation model 171 estimates the validity of the comment corresponding to the learning situation data based on the chronological order.

[0043] The instructor's comments may be single or multiple. A single comment is, for example, "Try harder." Multiple comments are, for example, "You did well." and "You worked really hard." The Encoder passes the hidden states associated with the input data at each time to the Attention layer.

[0044] The Attention layer receives the hidden state vector for each time passed from the Encoder. The Attention layer calculates the score of the hidden state vector for each time input from the Encoder. For example, the score may be calculated using a softmax function. By multiplying each hidden state vector by the score passed through the softmax, it is possible to amplify high-scoring hidden states and eliminate low-scoring hidden states.

[0045] The Attention layer passes the converted hidden state vector to the Decoder. The Decoder is composed of neurons called LSTM blocks. The Decoder adds up the hidden state vectors input from the Attention layer at each time, combines them with the Decoder's hidden state vector, converts them, and obtains the Decoder's final hidden state vector. The Decoder outputs an estimation result that estimates the validity of the comment based on the final hidden state vector. The estimation result output from the Decoder is, for example, a continuous probability value indicating validity (for example, a value in the range from "0" to "1").

[0046] As shown in the figure, the Encoder accepts input of time-series learning status data (e.g., learning status data for April, May, and June) and comments on the learning status data (e.g., try harder), and performs calculations in the middle layer. Specifically, the server 1 divides the comment into multiple segments. A segment is a component of a sentence, and is the smallest unit obtained when dividing a sentence to an extent that does not sound unnatural as actual words. For example, if the comment is "try harder," the server 1 divides "try harder" into three segments including "more," "try harder," and "ne." The server 1 combines each segment with the time-series learning status data. For example, combine the "more" segment with the learning status data for April, combine the "try harder" segment with the learning status data for May, and combine the "ne" segment with the learning status data for June. The server 1 sequentially inputs multiple data combining segments and learning status data to each LSTM block. Although the learning status data on a monthly basis has been described in FIG. 8, the learning status data is not limited to this, and may be, for example, learning status data on a weekly basis.

[0047] The Encoder passes the hidden state vector associated with the input data at each time to the Attention layer. The Attention layer converts the hidden state vector at each time and passes the converted hidden state vector to the Decoder. The Decoder combines and converts the hidden state vector at each time input from the Attention layer and the hidden state vector of the Decoder, and outputs an estimation result that estimates the validity of the comment.

[0048] Note that the attention model is an example of the validity estimation model 171, and for example, the server 1 may generate other neural networks such as Seq2Seq (Sequence to Sequence) as the validity estimation model 171. In addition, the validity estimation model 171 is not limited to a neural network, and may be a model based on other learning algorithms such as a decision tree, a random forest, or a support vector machine (SVM).

[0049] For example, the server 1 generates the validity estimation model 171 by using the training data stored in the training data DB 172. The training data is a combination of time-series learning situation data of a learner, comments on the learning situation data, and information indicating the validity of the comments, which are associated with each other. The training data is generated based on a large amount of learning situation data collected from learners and comments from instructors corresponding to the learning situation data. The training data may be data manually created separately.

[0050] The server 1 inputs the time-series learning situation data for training and the instructor's comments as input data to the validity estimation model 171, and obtains the estimation result (output data) of the validity of the comments from the output layer. The server 1 compares the correct output data included in the training data with the output data output from the validity estimation model 171, and optimizes various parameters such as weights used for calculations in the intermediate layer so that the two are close to each other.

[0051] As a result, the server 1 generates (constructs) the validity estimation model 171. The server 1 stores the generated validity estimation model 171 in the learning model management DB 173. In this embodiment, the server 1 estimates the validity of the comment on the learning situation data of the learner using the validity estimation model 171, and outputs the estimation result of the validity of the comment on the learning situation data and the instructor's comment.

[0052] As shown in the figure, for a comment corresponding to the learning status data, an inference result is output in which the probability value of "valid" is "0.90" and the probability value of "inappropriate" is "0.10". In addition, the inference result may be output using a predetermined threshold. For example, when the server 1 determines that the probability value of "valid" (0.90) is equal to or greater than a predetermined threshold (e.g., 0.85), it outputs an inference result that the comment is valid. Alternatively, when the server 1 determines that the probability value of "inappropriate" (0.87) is equal to or greater than a predetermined threshold (e.g., 0.85), it outputs an inference result that the comment is inappropriate.

[0053] Furthermore, when the instructor's comments include multiple comments, the server 1 outputs the estimation result of estimating the validity of each comment using the validity estimation model 171. For example, when the instructor's comments include "good job" and "you worked really hard", the server 1 uses the validity estimation model 171 to output both the estimation result of the validity of "good job" and the estimation result of the validity of "you worked really hard".

[0054] For example, for the comment "Good job," the server 1 outputs an inference result in which the probability value of "appropriate" is "0.90" and the probability value of "inappropriate" is "0.10." In addition, for the comment "You worked really hard," the server 1 outputs an inference result in which the probability value of "appropriate" is "0.15" and the probability value of "inappropriate" is "0.85." In this case, the server 1 outputs the inappropriate comment "You worked really hard" as a comment to be corrected based on the inference result of the instructor's comment.

[0055] Fig. 9 is a flowchart showing the procedure of the process of generating the validity estimation model 171. The contents of the process of generating the validity estimation model 171 by performing machine learning will be described with reference to Fig. 9.

[0056] The control unit 11 of the server 1 acquires multiple pieces of training data in which the time-series learning situation data of the learner and comments on the learning situation data are associated with information indicating the validity of the comments from the training data DB 172 of the mass storage unit 17 (step S101). Based on the acquired time-series learning situation data and comments on the learning situation data and information indicating the validity of the comments, the control unit 11 generates a validity estimation model 171 that receives the time-series learning situation data and the learning situation data as inputs and outputs an estimation result of the validity of the comments based on the acquired time-series learning situation data and comments on the learning situation data and information indicating the validity of the comments (step S102).

[0057] For example, the validity estimation model 171 is a neural network related to an attention model, which receives learning situation data and comments on the learning situation data as input and outputs an estimation result of estimating the validity of the comments. The control unit 11 inputs the time-series learning situation data and comments to the validity estimation model 171, and outputs an estimation result of estimating the validity of the comments. The control unit 11 compares the output validity estimation result with correct answer information indicating validity contained in the training data, and optimizes various parameters of the intermediate layer so that the two are close to each other, thereby generating the validity estimation model 171.

[0058] The control unit 11 stores the generated validity estimation model 171 in the learning model management DB 173 of the mass storage unit 17 (step S103), and ends a series of processes. Specifically, the control unit 11 assigns a model ID to the generated validity estimation model 171, and stores the file and generation date and time of the validity estimation model 171 as one record in the learning model management DB 173 in association with the assigned model ID.

[0059] Also, the validity estimation model 171 can be generated for each preset personality type indicating the personality of the learner. Specifically, the server 1 acquires training data for each personality type of the learner from the training data DB 172. For example, when generating the validity estimation model 171 for personality type A, the server 1 extracts multiple pieces of training data corresponding to the personality type that is "personality type A" from the training data DB 172. The server 1 generates the validity estimation model 171 for personality type A using the extracted training data.

[0060] Next, a method for using the generated validity estimation model 171 will be described.

[0061] The learner terminal 2 accepts input of time-series learning status data (e.g., the learner's score, academic ability deviation value, or amount of learning) indicating the learning status of the learner. For example, the learner terminal 2 accepts the amount of learning of the learner for three months (e.g., April, May, and June). If the learner uses, for example, an E-learning system that provides assignments, the learner terminal 2 may acquire the learning status data of the learner stored in the E-learning system. The learner terminal 2 transmits the acquired learning status data of the learner to the server 1. The server 1 receives the time-series learning status data of the learner transmitted from the learner terminal 2, and transmits the received learning status data to the instructor terminal 3. The learning status data may be transmitted directly from the learner terminal 2 to the instructor terminal 3.

[0062] The instructor terminal 3 receives the learning situation data of the learner transmitted from the server 1, and acquires the instructor's comments corresponding to the received learning situation data. For example, the instructor terminal 3 acquires a plurality of pre-registered comments from the comment DB 175, and displays the acquired plurality of comments on the screen. The instructor terminal 3 may accept a selection of a comment from the plurality of comments displayed on the screen. The instructor terminal 3 may directly accept input of the instructor's comments. The instructor terminal 3 transmits the acquired instructor's comments to the server 1 together with the learning situation data.

[0063] The server 1 estimates the validity of the comment based on the learning situation data of the learner transmitted from the instructor terminal 3 and the comment corresponding to the learning situation data. Specifically, the server 1 inputs the time-series learning situation data and the comment corresponding to the learning situation data to the validity estimation model 171, and outputs the estimation result of the validity of the comment.

[0064] If the server 1 determines that the comment is valid based on the output estimation result, it transmits the estimation result of the comment to the instructor terminal 3. The instructor terminal 3 receives the estimation result of the validity of the comment transmitted from the server 1, and displays the received estimation result on a screen. The server 1 transmits the comment to the learner terminal 2. The learner terminal 2 displays the comment transmitted from the server 1 on a screen together with the learning situation data.

[0065] Furthermore, the validity estimation model 171 can be used to estimate the validity of a plurality of comments on the learning situation data. Specifically, the server 1 inputs the learning situation data and a plurality of comments on the learning situation data to the validity estimation model 171, and outputs an estimation result estimating the validity of each comment. Based on the output validity estimation result, the server 1 acquires inappropriate comments as comments to be corrected. The server 1 transmits the validity estimation result and the comments to be corrected to the instructor terminal 3. The instructor terminal 3 receives the validity estimation result and the comments to be corrected transmitted from the server 1, and displays the received estimation result and the comments to be corrected on the screen.

[0066] 10 is an explanatory diagram showing an example of a display screen of the estimation result of the validity of a comment. The screen includes a learning situation data display field 11a, a comment selection combo box 11b, a selection button 11c, a comment display field 11d, a check button 11e, and a check result display field 11f.

[0067] The learning status data display field 11a is a display field that displays the learning status data of the learner. The comment selection combo box 11b is a combo box that accepts the selection of the instructor's comment on the learning status data. It should be noted that the combo box is not limited to this and may be, for example, a text field that accepts manual input of the instructor's comment. The selection button 11c is a button that accepts the selection of the instructor's comment. The comment display field 11d is a display field that displays the selected comment. The check button 11e is a button that checks the validity of the comment. The check result display field 11f is a display field that displays the check result of the validity of the comment.

[0068] The instructor terminal 3 acquires the time-series learning situation data of the learner from the server 1. The instructor terminal 3 generates a graph showing the time-series learning situation data based on the acquired learning situation data. The graph showing the time-series learning situation data may be generated on the server 1 side. The instructor terminal 3 displays the generated graph in the learning situation data display field 11a.

[0069] As shown in the figure, a vertical bar graph showing the relationship between learning situation data and a period is displayed in the learning situation data display field 11a. The horizontal axis of the vertical bar graph indicates the period (e.g., April to February of the following year). The vertical axis of the vertical bar graph indicates the learning situation data (e.g., the amount of learning or the learning time). Note that while FIG. 10 shows a vertical bar graph format, this is not limiting and other graph formats may be used.

[0070] The instructor terminal 3 acquires a plurality of preset comments for the instructor from the comment DB 175 of the server 1, and sets the acquired plurality of comments in the comment selection combo box 11b. For example, the instructor terminal 3 acquires "Try harder," "Nicely done," and "You worked really hard," which are registered in advance in the comment DB 175, and sets them in the comment selection combo box 11b. When the instructor terminal 3 receives a touch (click) operation of the selection button 11c, it acquires the comment selected by the comment selection combo box 11b. The instructor terminal 3 displays the comment of the selected instructor in the comment display field 11d.

[0071] It is also possible to select multiple comments. For example, when the instructor terminal 3 receives the selection of "Good job" from the comment selection combo box 11b, it displays the received "Good job" in the comment display field 11d. When the instructor terminal 3 receives the selection of "You worked really hard" from the comment selection combo box 11b, it displays the received "You worked really hard" in the comment display field 11d on the line following "Good job."

[0072] When the instructor terminal 3 receives a touch operation of the check button 11e, it transmits the selected comment together with the learning situation data of the learner to the server 1. The server 1 estimates (checks) the validity of the comment using the validity estimation model 171 based on the learning situation data transmitted from the instructor terminal 3 and the comment corresponding to the learning situation data. The server 1 transmits the estimation result (check result) of the validity of the comment to the instructor terminal 3.

[0073] The instructor terminal 3 receives the inference result of the comment sent from the server 1, and displays the received inference result in the check result display field 11f. As shown in the figure, for the selected comments "Good job" and "You worked really hard," the inference result that "Good job" is appropriate and "You worked really hard" is inappropriate (unreasonable) is displayed in the check result display field 11f. Inappropriate comments may be displayed in bold or italics, for example. Furthermore, for inappropriate comments, the instructor terminal 3 may output a message indicating inappropriateness (for example, "This advice is not suitable for learner A") to the check result display field 11f.

[0074] 11 and 12 are flowcharts showing the processing procedure when performing the process of estimating the validity of a comment. The control unit 21 of the learner terminal 2 acquires time-series learning status data indicating the learning status of the learner (step S211). For example, the control unit 21 may accept an input of the time-series learning status data via the input unit 24. Alternatively, when the learner uses an E-learning system, the control unit 21 may acquire the learning status data of the learner stored in the E-learning system via the communication unit 23. The control unit 21 transmits the learner ID and the acquired learning status data of the learner to the server 1 via the communication unit 23 (step S212).

[0075] The control unit 11 of the server 1 receives the learner ID and learning status data transmitted from the learner terminal 2 via the communication unit 13 (step S111), and transmits the received learner ID and learning status data to the instructor terminal 3 (step S112). The control unit 31 of the instructor terminal 3 receives the learner ID and learning status data transmitted from the server 1 via the communication unit 33 (step S311), and displays the received learner ID and learning status data on the display unit 35 (step S312).

[0076] The control unit 31 acquires a plurality of preset comments for the instructor from the comment DB 175 in the mass storage unit 17 (step S313). The control unit 31 displays the acquired plurality of comments on the display unit 35 (step S314). The control unit 31 may directly accept input of the instructor's comments through the input unit 34. The control unit 31 accepts a selection of a comment corresponding to the learning situation data from the displayed plurality of comments through the input unit 34 (step S315). The control unit 31 transmits the learner ID, the learning situation data, and the selected comment to the server 1 through the communication unit 33 (step S316).

[0077] The control unit 11 of the server 1 receives the learner ID, learning situation data, and comments transmitted from the instructor terminal 3 through the communication unit 13 (step S113). Based on the received learner ID, the control unit 11 acquires the learner's personality type from the learner DB 174 of the mass storage unit 17 (step S114). Based on the acquired personality type, the control unit 11 specifies the validity estimation model 171 (step S115). Specifically, the control unit 11 acquires and specifies the corresponding validity estimation model 171 from the learning model management DB 173 of the mass storage unit 17 based on the acquired personality type.

[0078] Based on the received learning situation data and comment, the control unit 11 estimates the validity of the comment using the identified validity estimation model 171 (step S116). Specifically, the control unit 11 inputs the learning situation data and the comment to the validity estimation model 171 and outputs an estimation result estimating the validity of the comment. Note that when the control unit 11 inputs multiple comments on the learning situation data to the validity estimation model 171, it outputs an estimation result estimating the validity of each comment.

[0079] The control unit 11 judges whether the comment is appropriate or not based on the output estimation result (step S117). If the instructor's comments include multiple comments, the control unit 11 judges whether each comment is appropriate or not. If the control unit 11 judges that any of the comments is inappropriate, it judges that the instructor's comments are inappropriate.

[0080] When the control unit 11 determines that the comment is inappropriate (NO in step S117), it transmits the comment estimation result and the comment to be corrected (inappropriate comment) to the instructor terminal 3 via the communication unit 13 (step S118). The control unit 31 of the instructor terminal 3 receives the estimation result and the comment to be corrected transmitted from the server 1 via the communication unit 33 (step S317), and displays the received estimation result and the comment to be corrected on the display unit 35 (step S318).

[0081] If the control unit 11 determines that the comment is valid (YES in step S117), it transmits the comment estimation result to the instructor terminal 3 via the communication unit 13 (step S119), and transmits the comment to the learner terminal 2 (step S120).

[0082] The control unit 31 of the instructor terminal 3 receives the inference result transmitted from the server 1 through the communication unit 33 (step S319), and displays the received inference result on the display unit 35 (step S320). The control unit 21 of the learner terminal 2 receives the comment transmitted from the server 1 through the communication unit 23 (step S213). The control unit 21 displays the learning situation data and the received comment on the display unit 25 (step S214), and ends the process.

[0083] According to this embodiment, it is possible to use the validity estimation model 171 to estimate the validity of the instructor's comment corresponding to the learning situation data of the learner.

[0084] According to this embodiment, by creating a validity estimation model 171 for each personality type of a learner, the validity of a comment is estimated for each personality type, making it possible to generate appropriate comments for the learner.

[0085] (Embodiment 2) The second embodiment relates to a form in which an instructor's comments are generated using artificial intelligence based on the learning situation data of the learner. Note that a description of the contents that overlap with the first embodiment will be omitted.

[0086] FIG. 13 is a block diagram showing an example of the configuration of the server 1 of the second embodiment. The same reference numerals are used for the contents overlapping with those in FIG. 2, and the description thereof will be omitted. The large-capacity storage unit 17 stores a comment estimation model (second learning model) 176. The comment estimation model 176 is an estimator that estimates the instructor's comments based on the learning situation data of the learner, and is a trained learning model generated by machine learning. The training data DB 172 stores training data for constructing the comment estimation model 176. The learning model management DB 173 stores information related to the trained comment estimation model 176.

[0087] FIG. 14 is an explanatory diagram showing an example of a record layout of the training data DB 172 of the second embodiment. Note that explanations of contents that overlap with FIG. 3 will be omitted. The training data DB 172 includes a training ID column, an input column, an output column, and a personality type column. The input column stores the time-series learning status data of the learner. Note that the learning status data is the same as in the first embodiment, so explanations will be omitted. The output column stores the instructor's comments on the learning status data.

[0088] 15 is an explanatory diagram regarding the comment estimation model 176. In this embodiment, the server 1 performs machine learning to learn the relationship between the time-series learning situation data of the learner and the instructor's comments corresponding to the learning situation data, and generates (creates) the comment estimation model 176.

[0089] For example, the comment prediction model 176 is a neural network generated by deep learning, and is an attention model that is a type of RNN.

[0090] The comment prediction model 176 related to the attention model is classified into a network that functions as an Encoder and a network that functions as a Decoder, as shown in FIG. <eos>is a reserved word that indicates the end of data during training or estimation processing.

[0091] The Encoder is composed of multiple neurons called LSTM blocks. The Encoder accepts input of time-series learning status data and performs calculations in the LSTM block. The LSTM block performs calculations while referring to the calculation results in the previous LSTM block, i.e., the calculation results based on the learning status data that appears immediately before. This allows the comment estimation model 176 to estimate the comment corresponding to the learning status data based on the time series order.

[0092] The Encoder passes the hidden state vectors (hidden states) associated with the input data at each time to the Attention layer. The Attention layer receives the hidden state vectors at each time passed from the Encoder and performs conversion processing on the received hidden state vectors at each time. Note that the conversion processing of the hidden state vectors is the same as that in FIG. 8, so a description thereof will be omitted.

[0093] The Attention layer passes the converted hidden state vector to the Decoder. The Decoder is composed of neurons called LSTM blocks. The Decoder adds up the hidden state vectors input from the Attention layer at each time, combines them with the Decoder's hidden state vector, converts them, and obtains the Decoder's final hidden state vector. The Decoder outputs an estimation result that estimates the comment corresponding to the learning situation data based on the final hidden state vector.

[0094] As shown in the figure, the Encoder accepts input of time-series learning status data (e.g., learning status data for April, May, and June) and performs calculations in the intermediate layer. The Encoder passes the hidden state vector associated with the input data for each time to the Attention layer. The Attention layer converts the hidden state vector for each time and passes the converted hidden state vector to the Decoder. The Decoder combines and converts the hidden state vector for each time input from the Attention layer with the hidden state vector of the Decoder, and outputs a comment corresponding to the learning status data (e.g., "Try harder.").

[0095] Note that the attention model is an example of the comment estimation model 176, and for example, the server 1 may generate other neural networks such as Seq2Seq as the comment estimation model 176. In addition, the comment estimation model 176 is not limited to a neural network, and may be a model based on other learning algorithms such as a decision tree, a random forest, or an SVM.

[0096] For example, the server 1 generates a comment estimation model 176 by using training data stored in the training data DB 172. The training data is a combination of time-series learning situation data of a learner and comments corresponding to the learning situation data. The training data is generated based on a large amount of learning situation data collected from learners and comments by instructors corresponding to the learning situation data. The training data may be data manually created separately.

[0097] The server 1 inputs the time-series learning status data for training as input data to the comment estimation model 176, and obtains an estimation result (output data) of an estimated comment corresponding to the learning status data from the output layer. The server 1 compares the correct output data included in the training data with the output data output from the comment estimation model 176, and optimizes various parameters such as weights used for calculation in the intermediate layer so that the two are similar to each other.

[0098] As a result, the server 1 generates a comment estimation model 176. The server 1 stores the generated comment estimation model 176 in the learning model management DB 173. In this embodiment, the server 1 uses the above comment estimation model 176 to estimate a comment for the learning situation data of a learner, and outputs an estimation result of the comment corresponding to the learning situation data.

[0099] Also, the comment estimation model 176 can be generated for each personality type of the learner. Specifically, the server 1 acquires training data for each personality type of the learner from the training data DB 172. For example, when generating a comment estimation model 176 for personality type A, the server 1 extracts multiple pieces of training data corresponding to the personality type that is "personality type A" from the training data DB 172. The server 1 generates the comment estimation model 176 for personality type A using the extracted training data.

[0100] 16 is an explanatory diagram showing an example of a display screen of the comment estimation result. The screen includes a learning status data display field 12a, a generate button 12b, and a comment display field 12c. The learning status data display field 12a is a display field that displays the learning status data of the learner. The generate button 12b is a button for generating (estimating) a comment. The comment display field 12c is a display field that displays the generated comment.

[0101] The instructor terminal 3 acquires the time-series learning status data of the learner from the server 1. The instructor terminal 3 generates a graph showing the time-series learning status data based on the acquired learning status data. The graph showing the time-series learning status data may be generated on the server 1 side. The instructor terminal 3 displays the generated graph in the learning status data display field 12a. Note that the display of the graph is the same as in FIG. 10, so a description thereof will be omitted.

[0102] When the instructor terminal 3 receives a touch operation of the generate button 12b, it transmits the time-series learning situation data to the server 1. The server 1 inputs the time-series learning situation data transmitted from the instructor terminal 3 to the comment estimation model 176, and outputs the instructor's comment corresponding to the learning situation data. The server 1 transmits the output instructor's comment to the instructor terminal 3. The instructor terminal 3 receives the instructor's comment transmitted from the server 1, and displays it in the comment display field 12c.

[0103] 17 is a flowchart showing the processing steps when performing comment generation processing. The control unit 11 of the server 1 acquires the time-series learning situation data of the learner (step S131). For example, the control unit 11 may acquire the learning situation data of the learner from the learner terminal 2 via the communication unit 13. Alternatively, when the learner uses an E-learning system, the control unit 11 may acquire the learning situation data of the learner stored in the E-learning system.

[0104] The control unit 11 transmits the learner ID and the acquired time-series learning status data to the instructor terminal 3 via the communication unit 13 (step S132). The control unit 31 of the instructor terminal 3 receives the learner ID and learning status data transmitted from the server 1 via the communication unit 33 (step S331). The control unit 31 displays the received learner ID and learning status data on the display unit 35 (step S332).

[0105] When the control unit 31 receives, via the input unit 34, a comment generation operation corresponding to the received learning situation data (step S333), the control unit 31 transmits a comment generation request to the server 1 via the communication unit 33 (step S334). For example, when the control unit 31 receives, via the input unit 34, a touch operation of a button for generating a comment, the control unit 31 generates a comment generation request. The generation request includes the learner ID and the time-series learning situation data. The control unit 31 transmits the generated comment generation request to the server 1 via the communication unit 33.

[0106] The control unit 11 of the server 1 receives the comment generation request transmitted from the instructor terminal 3 through the communication unit 13 (step S133). The control unit 11 acquires the learner's personality type from the learner DB 174 of the mass storage unit 17 based on the learner ID included in the received generation request (step S134). The control unit 11 identifies the comment estimation model 176 based on the acquired personality type (step S135). Specifically, the control unit 11 acquires and identifies the corresponding comment estimation model 176 from the learning model management DB 173 of the mass storage unit 17 based on the acquired personality type.

[0107] The control unit 11 inputs the time-series learning situation data included in the received generation request into the identified comment estimation model 176, and generates (estimates) the instructor's comment corresponding to the learning situation data (step S136). The control unit 11 transmits the generated instructor's comment to the instructor terminal 3 via the communication unit 13 (step S137). The control unit 31 of the instructor terminal 3 receives the instructor's comment transmitted from the server 1 via the communication unit 33 (step S335). The control unit 31 displays the received instructor's comment on the display unit 35 (step S336), and ends the process.

[0108] According to this embodiment, it is possible to use the comment estimation model 176 based on the time-series learning situation data of a learner, to generate a comment of an instructor corresponding to the learning situation data.

[0109] According to this embodiment, by creating a comment estimation model 176 for each personality type of a learner, the instructor's comments are estimated for each personality type, which is useful for creating appropriate comments.

[0110] According to the present embodiment, by outputting a comment to be corrected from among a plurality of comments on the learning situation data, it becomes possible to assist in creating an appropriate comment.

[0111] According to this embodiment, by automatically generating the instructor's comments, it is possible to significantly reduce the workload of the instructor.

[0112] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the scope and meaning equivalent to the claims. [Explanation of symbols]

[0113] 1. Information processing device (server) 11 Control section 12 Storage section 13. Communications Department 14 Input section 15 Display section 16 Reading unit 17 Mass storage 171 Validity Estimation Model (First Learning Model) 172 Training Data DB 173 Learning Model Management DB 174 Learner DB 175 Comments DB 176 Comment estimation model (second learning model) 1a Removable storage media 1b Semiconductor memory 1P control program 2. Information processing terminal (student terminal) 21 Control section 22 Memory section 23 Communications Department 24 Input section 25 Display section 2P control program 3. Information processing terminal (instructor terminal) 31 Control Unit 32 Storage section 33 Communications Department 34 Input section 35 Display section 3P Control Program< / eos>

Claims

1. a receiving unit for receiving time-series learning status data indicating the learning status of a learner and a comment from an instructor regarding the learning status data; an output unit that inputs the learning status data and the comments received by the receiving unit into a first learning model that has been trained to estimate the validity of the comments when time-series learning status data for each predetermined period and comments on the learning status data are input, and outputs an estimation result of the validity of the comments; An information processing device comprising:

2. inputting learning status data and a plurality of comments on the learning status data into the first learning model, and outputting an estimation result of estimating the validity of each comment; Based on the output estimation results, invalid comments are output as comments to be corrected.

2. The information processing apparatus according to claim 1,

3. The first learning model is created for each of the predetermined personality types that indicate the personality of the learner.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. a storage unit for storing the identification information of the learner and the personality type of the learner in association with each other; 4. The information processing device according to claim 1, further comprising:

5. A plurality of comments from which the comment can be selected are registered, The reception unit receives a selection of a comment from the plurality of comments.

5. The information processing apparatus according to claim 1,

6. a second output unit that inputs the learning status data received by the receiving unit into a second learning model that has been trained to output a comment of an instructor on the learning status data when the learning status data is input in a time series for each predetermined period, and outputs a comment on the learning status data.

6. The information processing device according to claim 1, further comprising:

7. The second learning model is created for each of the learner's personality types that are set in advance.

7. The information processing apparatus according to claim 6,

8. receiving time-series learning situation data indicating the learning situation of the learner and comments from the instructor regarding the learning situation data; The received learning status data and the comment are input to a first learning model that has been trained to estimate the validity of a comment when time-series learning status data for a predetermined period and a comment on the learning status data are input, and an estimation result of the validity of the comment is output. An information processing method in which processing is performed by a computer.

9. receiving time-series learning situation data indicating the learning situation of the learner and comments from the instructor regarding the learning situation data; The received learning status data and the comment are input to a first learning model that has been trained to estimate the validity of a comment when time-series learning status data for a predetermined period and a comment on the learning status data are input, and an estimation result of the validity of the comment is output. A program that causes a computer to carry out processing.

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