# A System to Realize Time- and Location-Independent Teaching and Learning Among Learners Through Sharing Learning-Articles

Seiyu Okai, Tsubasa Minematsu, Fumiya Okubo, Yuta Taniguchi, Hideaki Uchiyama, Atsushi Shimada

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## ► To cite this version:

Seiyu Okai, Tsubasa Minematsu, Fumiya Okubo, Yuta Taniguchi, Hideaki Uchiyama, et al.. A System to Realize Time- and Location-Independent Teaching and Learning Among Learners Through Sharing Learning-Articles. IFIP World Conference on Computers in Education (WCCE), Aug 2022, Hiroshima, Japan. pp.475-487, 10.1007/978-3-031-43393-1_44. hal-05533313

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# A System to Realize Time- and Location-Independent Teaching and Learning Among Learners Through Sharing Learning-Articles

Seiyu Okai¹✉, Tsubasa Minematsu¹✉, Fumiya Okubo¹✉, Yuta Taniguchi¹✉, Hideaki Uchiyama²✉, and Atsushi Shimada¹✉

¹ Kyushu University, Fukuoka, Japan
{okai,minematsu,fokubo,yuta.taniguchi,atsushi}@limu.ait.kyushu-u.ac.jp

² Nara Institute of Science and Technology, Ikoma, Japan

**Abstract.** Teaching and learning from one another is one of the most effective ways for learners to acquire proactive learning attitudes. In this study, we propose a new learning support system that encourages mutual teaching and learning by introducing a mechanism that guarantees sustainability. Learners submit articles called “learning-articles” that summarize their own learning and knowledge. The proposed system not only accumulates and publishes these articles but also has a mechanism to encourage the submission of necessary topics. The proposed system has been in operation since the academic year 2020, and it has collected learning-articles across our university’s nine academic disciplines from more than 300 learners. To investigate the effects of sharing learning-articles on education from the learners’ perspectives, a questionnaire was distributed among 25 students.

**Keywords:** learner-centered design · learning-article · educational support system

## 1 Introduction

Teaching and learning from one another have been considered important for a long time [1]. In recent years, some educational methods, such as cooperative learning [2,3] and flipped learning [4–6], have attracted particular attention. The increased interest in these educational methods is due to a shift away from traditional educational methods toward new ones that emphasize learner independence. In the case of conventional educational methods, learners may assume that they cannot acquire the ability to learn independently, and thus, will be taught by the teacher [7]. This ability is, however, necessary to respond to the accelerating pace of social change resulting from the expansion of globalization
and the development of information technology. Therefore, educational methods that integrate teaching and learning from one another [2–6] are expected to enable students to acquire a proactive attitude toward learning and also learn more effectively.

Computer based education has two main types of systems, “synchronous” and “asynchronous,” to assist learners in teaching and learning from one another [8–11]. “Synchronous” learning support includes chat tools [8] and videoconferencing systems [9]. This type of system enables learners to quickly discuss the results among themselves in real time. However, its disadvantage is that the learners’ knowledge is shared within a small group of learners who belong to the same class and knowledge sharing across generations is not possible.

“Asynchronous” learning support involves learners compiling their learning knowledge into articles (including texts and diagrams) and storing them in a database for other learners to read. A typical example is a “wiki,” which allows learners to collaboratively edit articles on various topics from a textbook [10, 11]. Though the development of “asynchronous” learning support entails the advantage of passing on learners’ knowledge to the next generation, the challenge is how to ensure sustainability. For example, if learners were free to submit articles, many of them could be concentrated on simple or major topics that appear in textbooks. However, there might be fewer articles dealing with more advanced or novel topics that do not appear in textbooks. If this situation continues, it could soon become difficult for learners to submit articles that are useful to other learners. Therefore, it is not enough to simply accept and publish articles; a system that monitors the balance of topics in the articles submitted to the system and encourages learners to submit articles on topics that are currently in demand is needed.

Based on the above discussion, this paper proposes a learning support system that encourages teaching and learning from one another and is assisted by a mechanism to enhance sustainability. In the proposed system, learners can teach and learn from one another by sharing “learning-articles” that summarize their own learning in the form of articles. The proposed system consists of two subsystems. The first is a front-end subsystem, which accepts article submissions, browses through articles, and calls for submissions. The second is a back-end system, which includes a database for storing submitted articles and an analytics module for extracting topics that need to be submitted. Section 2 provides an overview of the proposed system and a detailed description of each subsystem. In Sect. 3, we report the results of a demonstrative experiment conducted in a class at our university, and show the usefulness of the learning-articles and the effectiveness of the calls for submission.

## 2 Proposed System

This section provides an outline of the proposed system (Fig. 1), which consists of two subsystems. The first is the learning-article publication system (LAPS), which accepts the submissions of learning-articles and presents the search results
for them. In addition, LAPS solicits submissions based on rankings to ensure that the submitted learning-articles are not skewed toward a particular topic. The second is the learning-article management system (LAMS), which consists of a database and an analytics module in which information on learning-articles and the operation logs of learners are stored. The aim of the proposed system is to enable learners to acquire an attitude of actively sharing their knowledge with others and trying to learn from others. In the following sections, we explain LAPS and LAMS in more detail.

<figure><img src="image_5.png" /><figcaption>Fig. 1. Diagram of the proposed system</figcaption></figure>

## 2.1 Learning-Article Publication System (LAPS)

In this section, we explain each module of LAPS, which is typically three in number. The first is the “Submission module,” which is used when learners submit their articles. The second is the “Call for learning-articles module,” which encourages learners to submit articles on specific topics. The third is the “Search module,” which learners use to search for articles to read. By using the modules provided by the LAPS, learners can submit and read learning-articles regardless of time and location.

**Submission Module.** The submission module is used by learners to submit learning-articles with the intent of sharing their knowledge with other learners. Figure 2 shows the screen used for submitting a learning-article. In LAPS, students can insert images, diagrams, and hashtags in the article. After entering the title and body of the article, the learner must click on the “Submit” button
at the bottom left of the screen to submit the article. The data on the learning-articles submitted are stored in the learning-article data table of the database. To guarantee the quality of the published articles, the LAPS has instituted an approval process overseen by teaching assistants (TA) and teachers (approvers). If an article submitted by a learner contains incorrect information or the content is incomprehensible, the approver will ask the learner (writer) to correct it. An example of a submitted article is shown in Fig. 3. This article was posted for the course "Digital Signal Processing," and explains the difference between "Periodic Signal" and "Aperiodic Signal." In this learning article, a hashtag was added at the top of the text. To prevent readers from identifying the author of each article, only their initials are provided at the top of each article. By using the search module, learners can read one another's articles. If they agree with the article, they can also give feedback by clicking on the "Like" button. Though learners do not explicitly give feedback in any other way than by pressing the "Like" button, implicit feedback, such as search logs and browsing time, is stored in the system. These logs are used to generate topic rankings in the Analytics module.

<figure><img src="image_6.png" /><figcaption>Fig. 2. The screen when submitting a learning-article</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 3. Example of a posted learning-article.</figcaption></figure>
**Call for Learning-Articles Module.** The call for the learning-articles module obtains data related to two learning-topic rankings (learning-topic ranking table in Sect. 2.2) from the database and presents the ranking before learners to encourage them to submit learning-articles. In the proposed method, a learning topic comprises a combination of keywords and article types. Article types are of the following four kinds: "basic," "development," "summary," and "other." The following two learning-topic rankings were displayed to the learners:

1. The ranking of topics for which many learners have searched but have not found the desired article. (This is called the "Ranking of difficult topics")

2. Topics with a limited number of submissions are ranked according to the number of submissions. (This is called "Ranking of few topics".)

The ranking screen presented to the learners is shown in Fig. 4. Learners can view the corresponding rankings by selecting a course from the buttons on the upper left of the screen. The left side of the screen shows the "Ranking of difficult topics," whereas the right gives the "Ranking of a few topics" from 1 to 10. The rules of ranking are explained in Sect. 2.2. If a learner finds a topic that they want to write about among the topics presented in the rankings, they can follow the following steps: first, click on the corresponding topic; next, click on the Submit button in the upper-right corner of the screen, which will take you to the page shown in Fig. 2; the hashtag related to the selected topic will be automatically added to the learning-article that you have posted. If a learner cannot find the topic they want to write about in the rankings, they can submit articles on new topics or topics outside the rankings.

The call for the learning-article module plays a major role in ensuring that a balance of topics of the submitted learning-articles is maintained by presenting the rankings to the learners and encouraging them to contribute.

<figure><img src="image_8.png" /><figcaption>Fig. 4. The screen when displaying the ranking of learning topics</figcaption></figure>
**Search Module.** The search module is used by learners to search for the desired articles. Either a personal computer or a smartphone can be used for this purpose; on a smartphone, articles can be searched for with the help of a chatbot. Regardless of the method used, all operations performed by the learner are stored in the operation log table of the database (Sect. 2.2).

Figure 5 shows a screenshot of the system when used from a personal computer. At the top of the screen there are options to set various parameters for the search. Articles can be narrowed down mainly using the following parameters.

- Keywords of the learning-article

- Hashtags

- Year of posting

- Course of study

- Sorting by date, time, title, or number of likes (learners can click the “Like” button for learning-articles they like)

When using a smartphone to conduct the search, the chatbot provides repeated prompts in order to narrow down the articles. The learner is asked for the following information: keywords, the type of learning-article, and the course of study. Based on the information obtained, the chatbot extracts three learning-articles and presents them to the learner.

<figure><img src="image_9.png" /><figcaption>Fig. 5. The screen when searching for learning-articles</figcaption></figure>

## 2.2 Learning-Article Management System (LAMS)

Unlike LAPS, LAMS does not directly interact with learners, but plays an important role in facilitating the sharing of learning-articles. The database accumulates the information necessary for the operation of the proposed system. The analytics module analyzes the data accumulated in the database and extracts the articles to be presented to learners as search results and learning topic rankings.
**Database.** This section describes the three important tables in the database. The first is the articles' data table, which mainly stores the following data sent from the submission module (Sect. 2.1) for each article:

- Id

- Article title

- Text of the body of the article

- Last modified time (e.g., "2021/01/01 13:20")

- Author name

- Year of submission (e.g., 2019)

- Article topic (e.g., "Fourier transform/basic," "Fourier transform/development")

- Subject name (e.g., "linear algebra," "digital signal processing," "pattern recognition")

- Number of "Likes"

The second is the operation log table, which stores the following data sent by the main search module (Sect. 2.1).

- The ID of the learning-article

- Operation name (e.g., "browse," "click 'Like' button," "feedback on smartphone," "submit a learning-article")

- Time of the operation

- Name of the learner who performed the operation

The third is the learning-topic ranking table, which is used to store the ranking information presented to learners when they use the call for learning-articles module (Sect. 2.1). It stores the following data:

- The name of the learning topic

- Type of ranking ("Ranking of difficult topics" or "Ranking of few topics")

- Course name

- Number of places

- Date and time when the rank was generated

**Analytics Module.** The LAMS analytics module has two main functions. The first is to determine which articles are to be presented to the learner as search results. As shown in Fig. 5, when a learner initiates a search using a personal computer or smartphone, the module extracts the learning-articles that contain the specified keywords, subject name, and year specified by the learner. If the learner requests for the search results to be sorted, then they are returned after sorting. The data necessary for extracting the learning-articles are stored in the article data table.

The second function is to decide which topics to present in the learning topic ranking list based on the data from the article data table and the operation log table. The module stores the data in the ranking data table based on the analytics results. The ranking of difficult topics is generated by collecting the
number of times learners failed to find the desired article for each learning topic and sorting them in order of increasing frequency. More specifically, the weighted sum of the number of times the learner viewed and pressed the “Like” button for each topic is calculated. The ranking of a few topics is generated by aggregating the number of posts of learning-articles for each topic from the article data table and sorting them according to topics with the least numbers of posts. The relation between topics and articles is determined based on the keywords and hashtags included in the title.

# 3 Experiment

The proposed system has been in operation at our university since the academic year 2020, and as of April 2022, it has collected learning-articles across nine academic disciplines (e.g., digital signal processing) from 314 undergraduate learners. In this section, we report on the effects the learning-articles have on learning (Sect. 3.1) and the impact of the call for learning-article module on learners (Sect. 3.2), both of which we investigated during the aforementioned period. If ranking can be proven to have an effect on encouraging submissions, the prospects for the sustainability of the proposed system are high.

## 3.1 Evaluation of the Usefulness of Learning-Articles

A questionnaire was prepared to investigate the effects of sharing learning-articles on education from the learners’ perspectives. The questionnaire was distributed among 25 students who had undergone training in digital signal processing in 2020 when the proposed system was put into operation. As homework after the final lecture, the learners posted articles based on their own learning and read the articles of others. The learners were informed by their teachers that their submission and reading of learning-articles would be used to evaluate their engagement in determining their grades. Thereafter, they responded to the questionnaire.

The results of the experiment are presented in Table 1. Four questions on the questionnaire required responses on a 5-point scale. Questions 1 (Q1) and 2 (Q2) asked the respondents to assess the process of contributing learning-articles. About half of the learners returned positive scores of 4 or 5, indicating that writing learning-articles helped them understand the main points of the lessons and the course content that was previously unclear. Questions 3 (Q3) and 4 (Q4) asked the respondents to assess the effect of the learning-articles on their learning. About half of the learners returned positive scores to each of these questions, indicating that for some learners, reading articles written by other learners helped them understand the course content and resolve points that they did not originally understand.
<table><caption>**Table 1.** Results of the questionnaire for learners about the learning-articles. 5-point scale, 1: don't agree at all, 2: don't agree, 3: can't say, 4: agree, 5: fully agree. The units are *n*.</caption><thead><tr><th>Question</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>Average</th></tr></thead><tbody><tr><td>Q1. Writing learning-articles helped you to better understand the main points of the course</td><td>1</td><td>2</td><td>8</td><td>10</td><td>4</td><td>3.6</td></tr><tr><td>Q2. Writing learning-articles helped you to determine what you didn't understand</td><td>1</td><td>1</td><td>7</td><td>12</td><td>4</td><td>3.7</td></tr><tr><td>Q3. By browsing articles by other learners, you were able to understand the course content</td><td>1</td><td>2</td><td>7</td><td>10</td><td>5</td><td>3.6</td></tr><tr><td>Q4. By browsing learning-articles by other learners, you were able to identify the points you did not understand</td><td>0</td><td>2</td><td>10</td><td>8</td><td>5</td><td>3.6</td></tr></tbody></table>

## 3.2 The Result of the Call for Learning-Articles Module

In 2021, the second year of implementing the system, we launched the call for learning-articles module for the course "Digital Signal Processing.", which is structured over a span of fifteen weeks. The class had 116 undergraduates in their third year. Similar to the experiment in Sect. 3.1, the learners were told by their teachers that their posting and reading of learning-articles would be used to evaluate their engagement in determining their grades.

Some of the topics that were presented to the learners in the ranking category of difficult topics and few topics are shown in Table 2. The total number of topics of the learning-articles on "Digital Signal Processing" posted by the end of the experiment period was 449. The total number of difficult topics presented to the learners during the experiment was 11, which constitutes 2.4% of the total number of topics. There was almost no change in the ranking of difficult topics throughout the experiment. The total number of few topics presented to the learners during the experiment was 124, which constitutes 27.6% of the total number of topics. As for the ranking of few topics, the number of topics with only one post was very high, which suggests that numerous topics were presented to the learners in the ranking.

<table><caption>**Table 2.** Correspondence table between each group and the ranking to be viewed</caption><thead><tr><th>Ranking name</th><th>Topic</th><th></th><th></th></tr></thead><tbody><tr><td>Ranking of difficult topics</td><td>Sampling theorem/Basic</td><td>Discrete-time systems/Basic</td><td>Signals/Basic</td></tr><tr><td>Ranking of few topics</td><td>Python/Basic</td><td>Noise reduction/Basic</td><td>Block matrix/Basic</td></tr></tbody></table>
## 3.3 An Effect of Call for Learning-Articles Module

This was conducted as an experiment in the class (same as Sect. 3.2). Students were divided into four groups to examine the effects of the two lists when presented together and separately. Table 3 shows the correspondence between each group and the ranking. A “✓” indicates that the students had browsed the corresponding list; “×” means that the students had not browsed the corresponding list. Students in Groups A to C could use the call for learning-articles module, but the ranking list presented to each group was different. The students in Group D were used as the reference group and were not allowed to use the call for learning-articles module; they submitted learning-articles from the LAMS without browsing the rankings.

<table><caption>Table 3. Correspondence table between each group and the ranking to be viewed</caption><thead><tr><th>Ranking name</th><th>Group A</th><th>Group B</th><th>Group C</th><th>Group D</th></tr></thead><tbody><tr><td>Ranking of difficult topics</td><td>✓</td><td>×</td><td>✓</td><td>×</td></tr><tr><td>Ranking of few topics</td><td>×</td><td>✓</td><td>✓</td><td>×</td></tr></tbody></table>

To examine how the call for the learning-articles module changes the topics posted, the number of posts on the topics presented in the ranking for each group and the percentage of posts in the group were tabulated (see Table 4). In row 2 of Table 4, a comparison between the totals for each group shows that Group A had the highest number of submissions (30) and Group C the second highest (23). Both of these groups were shown the ranking of difficult topics, and the number of posts was greater for these groups than that for Groups B and D, which were not shown the ranking of difficult topics. In addition, more than half of the topics posted by Groups A and C were related to the topics presented in the list of difficult topics. In row 2 of Table 4, Group B was the most common, followed by Group D. Group B was shown the list of topics with the fewest submissions. In addition, more than one-third of the topics posted by Groups B and D were related to the topics presented in the list of a few topics.

We found that the number of posts related to the presented topic increased when the ranking of difficult topics was presented. In addition, in the ranking of few topics, the total value of Group C was lower than that of Group D. This means that it is difficult to expect an increase in the number of submissions related to the ranking of few topics when two rankings are shown simultaneously.

The students in Groups A to C completed a questionnaire about the usefulness of the call for learning-articles module, the results of which are presented in Table 5. In Question 1, there was a slight difference between Groups A to C, ranging from 3.7 to 3.9, but the difference was not statistically significant. When the results of Groups A to C were combined, it was found that 28 of the 41 students selected a positive rating of 4 or 5 for Question 1. These results indicate that the presentation of the ranking was helpful for more than 60% of the
<table><caption>**Table 4.** A comparison of the number of posts on the topics that appeared in the ranking during the experiment and their percentage of the total number of posts (the value in brackets indicates that they account for *n*% of the total number of posts in the group).</caption><thead><tr><th>Ranking name</th><th>Group A</th><th>Group B</th><th>Group C</th><th>Group D</th></tr></thead><tbody><tr><td>Ranking of difficult topics</td><td>30 (68%)</td><td>13 (25%)</td><td>23 (59%)</td><td>12 (33%)</td></tr><tr><td>Ranking of few topics</td><td>2 (5%)</td><td>19 (37%)</td><td>5 (13%)</td><td>11 (31%)</td></tr></tbody></table>

<table><caption>**Table 5.** Questionnaire results for the evaluation of the call for learning-articles module. Question 1 is 5-point scale, 1: don't agree at all, 2: don't agree, 3: can't say, 4: agree, 5: fully agree. Question 2 is also 5-point scale, 1: very few, 2: few, 3: about right, 4: many, 5: very many. The units are *n*.</caption><thead><tr><th rowspan="2">Questionnaire</th><th rowspan="2">Group</th><th colspan="5">Evaluation</th></tr><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>Ave</th></tr></thead><tbody><tr><td rowspan="3">Question. 1 The call for learning-articles module was helpful in deciding the topic of the learning-article to be posted</td><td>A</td><td>1</td><td>0</td><td>2</td><td>9</td><td>3</td><td>3.9</td></tr><tr><td>B</td><td>0</td><td>0</td><td>4</td><td>5</td><td>3</td><td>3.9</td></tr><tr><td>C</td><td>0</td><td>2</td><td>4</td><td>4</td><td>4</td><td>3.7</td></tr><tr><td rowspan="3">Question. 2 The number of rankings displayed in the call for learning-article module was sufficient</td><td>A</td><td>1</td><td>4</td><td>8</td><td>1</td><td>1</td><td>2.8</td></tr><tr><td>B</td><td>1</td><td>4</td><td>5</td><td>2</td><td>0</td><td>2.7</td></tr><tr><td>C</td><td>0</td><td>4</td><td>7</td><td>2</td><td>1</td><td>2.9</td></tr></tbody></table>

students in deciding the topic when submitting a learning-article. In Question 2, there was no significant difference in the distribution between Groups A and C. About 20% of the students felt that the number of rankings was too high, and about 40% felt that the number was too few. This suggests that the appropriate number of rankings varies greatly among learners. As a countermeasure, we are planning to increase the flexibility of the number of rankings that the module can offer.

## 3.4 Discussion

From Table 4, it can be concluded that the call for learning-articles module is expected to increase the number of submissions on topics where the number of submissions is low and many learners cannot find the desired articles. The results in Table 5 suggest that the information provided by the call for learning-articles module is what learners can demand. In addition, although we do not explain it in detail in this paper, when we compared the number of “Likes” on articles posted under the influence of the call for learning-articles module and those without the module, we found no significant difference. Based on these results, it can be said that the introduction of the call for learning-articles module in the proposed system can encourage learners to submit articles while adjusting the balance of topics of learning-articles stored in the database. In common with
previous studies, reading each other's writing has been proven to have a certain effect on learning. As no previous study has evaluated learner-centered systems from the perspective of sustainability, the contribution of this study is significant [10, 11].

Our study has some room for improvement. First of all, the effectiveness of the proposed method can be demonstrated in a more general perspective by combining it with Adaptive Comparative Judgement and the Delphi Technique. During this experiment, 119 articles were submitted, and it took about five hours for one TA to approve them. Considering that the system will be deployed in large-scale lectures, it is important to improve the efficiency of the approval process for learning-articles and to secure more approvers. In addition, the learners submitted the articles after receiving instructions from the faculty that they would be added to the engagement evaluation in the lecture. Therefore, it is unclear to what extent learning-articles would be used if faculties encouraged the writing and viewing of learning-articles in a manner that was not used for their grades. Also, it is unclear whether there are cases where articles are approved by teachers even if they are difficult to read from the learners' point of view.

## 4 Conclusion

In this study, we propose a new framework that enables learners to teach and learn from each other through learning-articles posted online. The results indicate that the learners could indeed understand the course contents and solve their problems by utilizing the articles. In addition, the call for learning-articles module could play an important role in gathering articles needed by learners based on the analytics. We believe that the proposed system is designed so as to become a self-sustaining operation. In the future, we will obtain learning progress data from quizzes, and so on, and clarify what kind of progress is generated from reading learning-articles. In addition, we will conduct detailed user evaluations of the submission and search modules as these were not done in the current study. In the long-term, we will conduct cross-analyses among learning activities from other systems, such as digital textbooks, the learning management system, and test scores. We would like to identify particular linkages, such as the types of activities that have high correlations or contribute to the posting of relevant articles in order to provide individualized support for posting learning-articles.

**Acknowledgements.** This work was supported by JST AIP Grant Number JPMJCR19U1, JSPS KAKENHI Grant Number JP18H04125, and JP22H00551, Japan.

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