# Development and Evaluation of a Field Environment Digest System for Agricultural Education

Kanu Shiga, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi

Shimada, Rin-Ichiro Taniguchi

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Kanu Shiga, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada, et al. Development and Evaluation of a Field Environment Digest System for Agricultural Education. IFIP World Conference on Computers in Education (WCCE), Aug 2022, Hiroshima, Japan. pp.87-99, 10.1007/978-3-031-43393-1_10. hal-05533339

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# Development and Evaluation of a Field Environment Digest System for Agricultural Education

Kanu Shiga, Tsubasa Minematsu(✉) ID, Yuta Taniguchi ID, Fumiya Okubo ID, Atsushi Shimada ID, and Rin-ichiro Taniguchi ID

Kyushu University, Fukuoka, Japan

{shiga,minematsu}@limu.ait.kyushu-u.ac.jp,

taniguchi.yuta.941@m.kyushu-u.ac.jp, {fokubo,atsushi}@ait.kyushu-u.ac.jp,

rin@kyudai.jp

**Abstract.** Smart agriculture has assumed increasing importance due to the growing age of farmers and a shortage of farm leaders. In response, it is crucial to provide more opportunities to learn about smart agriculture at agricultural colleges and high schools, where new farmers are trained. In agricultural education, a system is used for managing environmental information, such as temperature and humidity, obtained from sensors installed in the field. However, it is difficult to make effective use of this system due to the time required to detect changes in the field interfering with class time and the problem of oversight. In this study, we proposed a field environment digest system that will help learners by providing the summarized field sensing information, and support them in analyzing the data. In addition, to examine the potential for using field sensing information in agricultural education, we investigated the usefulness of the summarized sensor information and students' usage of this information. In this paper, we outline the contents of the developed system and the results of the digest evaluation experiments.

**Keywords:** smart agriculture · agricultural education · educational support system

## 1 Introduction

Smart agriculture has grown rapidly because of improvements to the Internet-of-Things (IoT), such as drones and sensor networks. Farmers obtain agricultural sensing information, such as the temperature and humidity in farm fields [1], with which they can manage the fields effectively [2]. Additionally, a wide range of practical applications are being implemented, including growth diagnosis using drones [3] and production management using satellite imagery [4]. It is important for young farmers and students in agriculture courses to develop decision-making skills based on such information. In recent agricultural education, field management systems [5] have also been used for managing farm based on field sensing

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T. Keane et al. (Eds.): WCCE 2022, IFIP AICT 685, pp. 87–99, 2023.

https://doi.org/10.1007/978-3-031-43393-1_10
information in addition to traditional e-Learning systems, such as e-book systems and learning management systems [6].

In agricultural education, it is necessary to train farmers who can utilize this kind of smart agriculture in the future. Toward this goal, IoT infrastructures are being built to support agricultural education [5]. Various web-based applications, cell phone applications, and learning modules are also being developed to utilize the real-time sensing data obtained through such IoT infrastructure [7]. To utilize field sensing information such as temperatures and humidity on farms, data visualization approaches are proposed, such as graphs of time series data or heat maps of field temperatures. In current agricultural pedagogy, the visualization system is used to provide field sensing information to the learner [8].

As mentioned above, systems and applications for smart agricultural education have been developed, but the effective use of such visualized data for agricultural education remains unclear. It has been pointed out that training programs for teachers are needed in order to motivate learners to use sensing data [9]; teachers are not familiar with sensing data and therefore need additional training. Moreover, a lecture on data analytics was provided to experts who were already engaged in agriculture [10]. However, the targets of smart agriculture education may not only be experts who have agricultural know-how, but also include the learners who have less agricultural knowledge, such as students in agricultural high schools. These students have a variety of tasks, such as acquiring knowledge and growing crops, and they cannot devote their time solely to studying sensing information analysis.

Although such field sensing information is represented by graphs and heat maps in conventional systems, it is difficult for students to find relevant information from these formats as they need to possess the necessary knowledge of agriculture and field sensing information. With these problems, it is difficult to effectively utilize field sensing information within a limited lecture time. We believe that students' learning can be supplemented with summarized sensor information in order to facilitate their studies. However, it is not clear whether the summarized sensor information would be useful. In addition, since the utilization of field sensing information agricultural education lacks clarity, we need to ascertain its effective usage for agricultural education. For the investigation, we need to track how learners use the field sensing information. Therefore, our research questions are as follows:

(RQ1) Is the use of summarized sensor information useful?

(RQ2) How do learners utilize the summarized sensor information?

In this study, we proposed a field environment digest system to help learners by providing the summarized field sensing information and to support them in analyzing sensing data, such as time series data of temperature and humidity. This system can provide a digest as a candidate for sensor values to focus on. To investigate how learners use the system, it was equipped with a mechanism for collecting their operation logs, such as switching sensors, changing time periods, manipulating charts, selecting list elements, and selecting table elements. As a specific summary method in this system, we prepared two methods: a table digest and a list digest. The table digest is for summarizing and visualizing
the field sensing information as statistical information, and the list digest is
for extracting and visualizing the period considered important from the field
sensing information based on change detection. Sudden changes in temperature
and humidity can lead to the deterioration of crop quality, and therefore it is
expected that changes in the condition of crops can be captured by monitoring
changes in the field. In extracting the periods representing the changes, we used
ChangeFinder [11] for change detection in time series data.

## 2 Related Work

To resolve the issues of reduction in the labor force and increased production
costs in rural areas, there is growing demand for smart agriculture processes such
as drone breeding diagnostics [3], rice paddy management [4], and visualization
of temperature and humidity information on maps [12]. Such progress in smart
agriculture technology has made it necessary to increase opportunities to learn
about them in agricultural education. According to Ejifor et al. [9], a curriculum
for smart agriculture education is being developed. In addition, Bryceson [1]
has shown that there is actually a movement for the use of smart agriculture in
agricultural education.

There are several cases of smart agriculture education being conducted. Take-
mura et al. [10] conducted a course on data analysis for agriculture at a univer-
sity. Research on building IoT infrastructure to support education has also been
conducted by Gunasekera et al. [5], who have also developed various web-based
applications, cell phone applications, and learning modules to leverage the real-
time sensing data obtained through such IoT infrastructure. There are various
examples of such monitoring systems as IoT infrastructure. SALATA [2], devel-
oped by Akayama et al., is a system that collects and shares field sensing and
farm operation information collected in the field, before relating them to each
other in a time series. In addition, research has been conducted by Taniguchi et
al. utilizing SALATA in the field of agricultural education [8]. Field sensors that
collect field sensing information include MIHARAS<fnref n="1" /> and Midori Cloud. Both
are aimed at reducing the labor required for patrolling and increasing produc-
tivity. There are various types of sensors, including those for fields and weather.
SALATA provides users with a simple graphical representation of the time series
in the visualization.

However, it is difficult for students to analyze the sensor values in a sim-
ple graphical visualization, and there is no research to assist them in doing
so. In addition, it is not clearly defined how field sensing information should
be utilized in agricultural education, so it is necessary to clarify what kind of
utilization is effective for agricultural education. Therefore, in this study, we
obtained the learners' operation logs and tracked students' activities. Notably,
all of the research cases mentioned above were conducted involving university
students and experts with some knowledge. It is still unclear what effect these

<footnote n="1">¹ https://www.nishimu-products.jp/miharas/en. Last accessed 25 Feb. 2022.</footnote>
studies have on beginners, such as agricultural high school students, which is thus an issue that should be investigated.

# 3 Field Environment Digest System

The field environment digest system provides digests of several field sensing information sources, in addition to visualizing time series data. As shown in Fig. 1(a), our system provides three types of information: chart visualization, table digest, and list digest. This system also has the capability to collect operation logs. By investigating how learners use the system, we aimed to make the summarized field sensing information provided by the system useful.

The configuration of this system is shown in Fig. 1(b). The blue arrows represent field sensing information, and the red arrows represent operation logs. Since the field sensing data from the MIHARAS field sensors are stored in a database on AWS (Amazon Web Services), the field environment digest system obtains the data from the database and provides the learner with the original field sensing information as well as the summarized field sensing information over the LMS (Learning Management System). Students using this system are assumed to be those who use e-Learning systems. In this system, temperature, humidity, saturation, solar radiation, rainfall data, electrical conductivity, and volumetric water content obtained from MIHARAS sensors are used as field sensing data. The field sensing information to be displayed is based on the data obtained from the application programming interface in SALATA API [2].

<figure><img src="image_3.png" /><figcaption>Fig. 1. Field Environment Digest System</figcaption></figure>

## 3.1 Chart-Based Visualization of Field Sensing Information

As in the previous system, time series data is visualized using charts. In the chart visualization, the user selects the period of time for displaying the field sensing information. In the upper part of the screen, in the date area of Fig. 1(a), the
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Highlighting charts from a list item. Selecting an item in the list will highlight the corresponding period in the charts.</figcaption></figure>

time period can be selected from the calendar. In the chart area at the center of the screen in Fig. 1(a), time series data are visualized using charts. The chart visualizes the field sensing information obtained from the field by dividing it into categories. These can be viewed by each category, or multiple categories can be viewed simultaneously. Users can view the charts in more detail, zoom in and out easily, and change the category of the charts.

## 3.2 Table Digest

In the table summarizing the field sensing information, the maximum, minimum, and average values of the data for each day are calculated and displayed, as illustrated in the table area of Fig. 1(a). The table digest provides statistical values, and users can grasp longer-term trends at a glance. When selecting an element in the table, the chart of the corresponding sensing information can be displayed in the corresponding period. The users can refer to detailed field sensing information from the table quickly.

## 3.3 List Digest

In the list digest, we extract and visualize the periods that are considered important from the field sensing information. This system provides change points in the sensing information as the important periods. The reason is that sudden changes in temperature and humidity affect the quality deterioration of crops, therefore it is possible to capture the changes in the condition of crops by detecting the changes in the sensing information.

To create this list, we used a change detection method called ChangeFinder [11]. ChangeFinder can detect change points from time-series data by computing change scores for each time period. For further information on ChangeFinder, refer to [11]. When detecting change points from time series data, we used each category of the field sensing information separately.

We apply ChangeFinder to the time series data {$x_t$} ($t = 1, 2, \dots, n$) for computing the change scores. The change scores are normalized for all periods for determining the change point as a percentage of change scores $Score(x_t)$. The point $x_t$ is detected as a change point when $Score(x_t) > p$, where $p$ is a
threshold value. Since the change scores are normalized, the threshold value is set as a percentage. In addition, there is a forgetting parameter *o* as a parameter of ChangeFinder. The smaller this forgetting parameter *o* is set to, the more the change score will be calculated considering the influence of past data. We adjust *p* and *o* based on the time series data of the field sensing information. We describe the details in Sect. 4.1.

The periods extracted as change points are displayed in the form of a list. The list of each category of the field sensing information is generated, and the users can change the displayed list by selecting category tabs on the list area. As shown in the Fig. 2, the users can select an item in the list to highlight the corresponding part of the charts. By highlighting the change points on the chart area, the points become clearer when users need to focus on the points. This makes the sensing information of the field useful, as it reduces the need to find the periods that represent the changes in the field.

## 3.4 Collecting Operation Logs

The field environment digest system can collect the user's operation logs and timestamps such as switching sensors, changing time periods, operations on charts, elements in selected lists, and elements in selected tables. We collected operation logs in the date, chart, list, and table areas in Fig. 1(a). In the date area, by collecting logs related to switching sensors and changing the display period, it is possible to check which sensor and period the user viewed the field sensing information of. In the chart area, by collecting operation logs about chart operations, we can check categories and time periods users have viewed in detail by manipulating the charts. In the list area, by collecting operation logs related to lists, we can check what kind of digests are viewed by users. In the table area, by collecting operation logs related to tables, we can check what categories and time periods users have browsed in detail by manipulating the tables. In short, by collecting these logs in detail, we can track the usage history of learners. For example, by tracking the logs, we can see that a user has used the list and then used the chart to see the period highlighted by the list.

# 4 Experiment

## 4.1 Experimental Setting

As an evaluation of the field environment digest system, a questionnaire with nine questions scored on a five-point scale and a free-description questionnaire with three questions were administered to students at an agricultural high school. The 37 students attended a twice-weekly lecture on agricultural economics at an agricultural high school, and we instructed them to use our digest system in four lectures from July 15 to August 26, 2021. First we showed the students how to use our digest system. Specifically, we created and displayed a video explaining how we expected them to use the system (e.g., using charts after using lists).
Moreover, we set up questions on Moodle, and instructed students to answer the evaluation questionnaire and free-description questionnaire while using the system on August 30. In this experiment, each student used this system with an iPad provided by the school, and they started to use e-Learning systems and the table devised in April 2021. At the agricultural high school, we installed MIHARAS sensor poles to measure the farm field where the students study. The students observed the sensor values from the sensor poles via our digest systems.

In the field environment digest system, the list digest was created using Change-Finder. We set the parameters of ChangeFinder to detect 40% of the points when the temperature difference was more than 10 °C in one hour. Although it is possible to increase the detection rate, doing so would increase the total number of detection points and worsen the visibility. As the results of the parameter adjustment, our system presented two types of digests to the users: one with the forgetting parameter *o* and the threshold *p* set to *o* = 0.01 and *p* = 15, and the other with *o* = 0.05 and *p* = 35. The digest with the forgetting parameter *o* and threshold *p* set to *o* = 0.01 and *p* = 15 makes it easier to capture the changes over a long time period in the time series; for example, a digest that captures the different changes that occurred after three consecutive days of changes in temperature. The digest with forgetting parameter *o* and threshold *p* set to *o* = 0.05 and *p* = 35 makes it easier to capture the changes over a short period in the time series; for example, the digest captures changes in moisture content, such as a sudden increase in moisture content due to rainfall. We also set the other ChangeFinder parameters, the order of the autoregressive model was set to 1, and the range of smoothing was set to 10. To create the digest, we normalized the change point scores for the period (September 1, 2019 to July 1, 2021), excluding the change point scores for the training data (August 1-31, 2019).

## 4.2 Experimental Results

**Results of the Questionnaire.** The questions and results of the questionnaire are shown in Table 1. The number of the respondents was 37. On a five-point scale, 1 is not at all applicable, 2 is not applicable, 3 is undecided, 4 is applicable, and 5 is very applicable.

Between Q1 “the table improves your understanding of the sensor values” and Q2 “the list improves your understanding of the sensor values”, the percentage of respondents who gave a score of 5 in Q1 was lower than in Q2. The table digest can be more effective for learners than the list digest; however, most of the learners accepted both digests. This implied that digests can be useful for learners. In Q3 “the main points of the sensor values became clearer”, about 80% of the students responded positively. This showed that learners felt our system was able to help students find points of interest in sensor values. In the responses to Q4 “you were able to efficiently check your field environment during periods when there was no class” and Q5 “became easier to think about why the sensor values are changing”, about 80% of the students answered affirmatively. This implied that checking the simplified field sensing information helped the
<table><caption>**Table 1. Questionnaire Results**</caption><thead><tr><th>Questions</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>Q1. The table improves your understanding of the sensor values</td><td>0</td><td>0</td><td>6</td><td>28</td><td>3</td></tr><tr><td>Q2. The list improves your understanding of the sensor values</td><td>0</td><td>2</td><td>4</td><td>24</td><td>7</td></tr><tr><td>Q3. The main points of the sensor values became clearer</td><td>0</td><td>0</td><td>6</td><td>24</td><td>7</td></tr><tr><td>Q4. You were able to efficiently check your field environment during periods when there was no class</td><td>0</td><td>2</td><td>5</td><td>21</td><td>9</td></tr><tr><td>Q5. Became easier to think about why the sensor values are changing</td><td>0</td><td>2</td><td>4</td><td>23</td><td>8</td></tr><tr><td>Q6. You became more aware of the weather sensor values</td><td>0</td><td>1</td><td>11</td><td>18</td><td>7</td></tr><tr><td>Q7. You became more aware of the field sensor values</td><td>0</td><td>1</td><td>9</td><td>23</td><td>4</td></tr><tr><td>Q8. It is useful for understanding the course content</td><td>0</td><td>1</td><td>2</td><td>21</td><td>13</td></tr><tr><td>Q9. You became more motivated in class</td><td>0</td><td>0</td><td>13</td><td>14</td><td>10</td></tr></tbody></table>

students to consider the sensor values and the field environment. However, about 15% of the students gave a not positive response, with score lower than 3 in Q5. This indicated that it was difficult for them to interpret the sensor values and understand the field environment. In Q6 "you became more aware of the weather sensor values" and Q7 "you became more aware of the field sensor values", about 70% of the students answered positively, which indicates that the awareness of the sensor values improved. About 90% of the students answered positively in Q8 "it is useful for understanding the course content" and about 65% in Q9 "you became more motivated in class", respectively, which indicated that this system was useful for understanding and engaging in the class. From this result, we can conclude that almost all of the responses to the questionnaire were relatively positive. However, as the results of Q4 and Q5 show, it was still difficult to interpret the sensor values and evaluate the field environment. In addition, among the additional comments, we received positive responses such as "Was able to establish and improve a habit of examining data" and "Was able to establish and improve a habit of connecting data to the field environment."

Regarding RQ1, based on the results of these questionnaire responses, it was found that learners are positively affected by the provision summarized field sensing information to them. Therefore, we believe that our system will enhance the usefulness of field sensing information. In addition, when we asked learners to write freely about this system, many commented that the system was easy to use and that they became more aware of sensor values, suggesting that the system was user-friendly.

**Results of the Free Description Type Questionnaire.** This section describes the results of the experiment and the relationship between the charac-
<table><caption>**Table 2.** Results of K-means clustering</caption><thead><tr><td>Cluster</td><td>Description</td><td>Students</td></tr></thead><tbody><tr><td>0</td><td>mainly used charts</td><td>7</td></tr><tr><td>1</td><td>mainly used lists</td><td>3</td></tr><tr><td>2</td><td>mainly used tables</td><td>5</td></tr><tr><td>3</td><td>used a combination of charts and lists</td><td>2</td></tr><tr><td>4</td><td>used a combination of charts and tables, the charts more frequently</td><td>7</td></tr><tr><td>5</td><td>a combination of charts and tables, the tables more frequently</td><td>4</td></tr><tr><td>6</td><td>a combination of lists and tables</td><td>5</td></tr></tbody></table>

teristics of each student's operation obtained from the operation log. The three questions asked in the free description type questionnaire are as follows:

1. Choose a date and time to focus on from the list of water content and answer why you focused on that date and time.

2. Consider the causes of the increase in water content on July 9, 2021 and July 25, 2021, respectively.

3. Choose a date and time to focus on and discuss the change in moisture content during that period.

The number of respondents was 34. For all questions, respondents were instructed to answer freely using the survey form on the learning platform Moodle. The questions are for the sensor values of volume moisture content from July 1, 2021 to July 31, 2021. In answering the questions, the students were instructed to answer for the sensors in their own field while they use this system. The average response time was about 16 min. One student, who answered without using this system, was excluded from this evaluation.

For analysis of students' operation logs, we focused on three components: chart, list, and table components. These components correspond to the chart, list, and table areas in Fig. 1(a), respectively. We counted the number of the transitions between their two components and self-loops. In other words, we computed a nine-dimensional vector representing activities of each student in this experiment. Each nine-dimensional vector was normalized by its maximum value. K-means was applied to their nine-dimensional vectors to categorize the features of each student. In this experiment, we set a hyper-parameter *K* of K-means to 7 based on the elbow diagram.

Figure 3(a) shows the calculated center vectors of each cluster. Each clustering vector was normalized by its maximum value and signified a representative pattern showing how the students in the cluster used our system. Based on each large feature, we manually categorized the clusters. A description of each cluster and the number of students to which it belongs is shown in Table 2.

The results of these categorizations indicate that we were able to provide multiple uses for the learners. The usage was categorized into 15 students who
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Results of K-means clustering and NMF for analyzing student usages. In (a), the color means the ratio of operation transitions, where the brighter the color, the more the operation transition occurs. In (b) and (c), the color indicates the NMF scores, where the brighter the color, the higher the scores. Note that the usage and description evaluation in (c) are normalized for visualization.</figcaption></figure>

mainly used a single element and 18 students who used a combination of multiple elements, with more students using a combination of multiple elements.

From the results, we found that there were various ways to use the information, and it might be necessary to include other summarized field sensing information. In addition, students used the multiple elements provided by our system. Thus, even if the learners were beginners, they could combine various types of the summarized sensing information. However, since it is not clear what kind of use would be more effective, we need to evaluate how to use it.

We also examined the relationship between the responses to the questionnaire and the characteristics of the operation. In this study, we investigated the descriptions based on the following four criteria. These criteria were based on interviews with teachers at an agricultural high school.

1. Does it describes the specific sensor values?

2. Were multiple sensors considered in the description?

3. Were multiple time zones considered in the description?

4. Does it describes a discussion of the field environment?

We evaluated the three responses of the free description type questionnaire using binary values based on four evaluation criteria. We conducted Non-negative Matrix Factorization (NMF) [13] on the matrix that concatenates the 4-dimensional vector that is the sum of each evaluation and the 9-dimensional vector described above. For each student's 9-dimensional vector, each element of the vector was normalized by the maximum value of the same element in all
students. In this experiment, we set the number of components of NMF to four based on the elbow diagram.

In NMF, a matrix is approximated by the product of two small matrices. In this study, the small matrices represent the features of the students and the patterns of usage and scores. For example, Component 0 of Student 1 in Fig. 3(b) had a large value, meaning Student 1 had the pattern of Component 0 column in Fig. 3(c); the Component 0 column represents patterns for Criterion 4 and the list usage. Since each component is associated with each evaluation criterion, we examined which operations are associated.

According to Fig. 3(c), there is a relationship between usage and each evaluation criterion. Evaluation Criterion 1 is related to students who used a combination of lists and tables, and used charts by themselves, Evaluation Criterion 2 to students who used a combination of lists and tables, and used charts from lists, Evaluation Criterion 3 to students who used a combination of charts and tables but not lists, and Evaluation Criterion 4 to students who used a combination of lists and tables, used charts from lists, and used lists by themselves.

These results indicate that the students who used multiple elements wrote descriptions that met the above evaluation criteria. The students who used the lists and tables in combination were more likely to consider multiple sensors and dates. In addition, they were more likely to describe their thoughts about the field environment. Therefore, we suggest that a system needs to provide multiple field sensing information, not just singular ones. In this investigation, we found that there was a relationship between usage and each evaluation criteria. Therefore, we believe that we can support the students who did not describe the criterion.

Regarding RQ2, the results of categorizations by K-means indicate that learners used our system in various ways. From the questionnaire results, we understand that providing the summarized field sensing information could encourage the students to use the data. In addition, the results of the NMF indicate that there was a relationship between the usage of our system and the evaluation of the description content. In particular, we found that students who used the summarized field sensing information such as lists and tables were more likely to write descriptions that satisfied the evaluation criteria prepared by the teacher. Therefore, these findings suggest that providing multiple elements, rather than just a single element, can improve the effectiveness of students' use of data.

To support the students who received low evaluations, it is possible to advise them on how to effectively use the system. For example, showing them ways in which students with higher ratings use the system might enable them to write descriptions with high ratings. In addition, as a support for teachers, by analyzing the logs, it is possible to find out how the highly evaluated students are using the system, and thus design their lectures based on this information.

# 5 Conclusion

In this paper, we proposed a field environment digest system to help learners by providing summarized field sensing information and to support learners in
analyzing the sensing data. To evaluate the potential for agricultural education using field sensing information, we investigated whether the summarized sensor information would be useful, and how students would use the summarized field sensing information. In the experiment, an evaluation questionnaire and a free-description questionnaire were administered at an agricultural high school in order to find out the effectiveness of providing summarized field sensor information to help the learners and determine what kind of summary would be appropriate. From the results of our experiments, we believe that summarizing field sensing information and providing it to learners can help them analyze the field sensing information. In addition, we were able to provide multiple uses to the learners. We found that students using multiple digests were likely to describe more details, such as specific sensor values. However, even though it was easier for students to understand the focus of the sensor values, it is still difficult to analyze and consider the environment of the field.

A limitation of our study was the short duration of this experiment. Consequently, we could not evaluate the use of the system in the entire process of growing crops. Therefore, we could not evaluate the effectiveness for skill required in agricultural education. In addition, this system was not implemented in the other classes in the high school. In the future, systems should be developed that encourage students' activities according to their usage status, and be evaluated for longer periods. In addition, we did not analyze the contents of the agricultural information used by the students in this system. From our experiments, we found that there are several ways of using the system, but we believe that students who use this system in the same way may check different categories and for different periods of time. In future work, we consider investigating the time and categories students checked through the system to provide more detailed feedback to students and teachers.

**Acknowledgements.** This work was supported by JSPS KAKENHI Grant Number JP18H04117.

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