Interactive System for Collaborative Historical Analogy
Ryo Yoshikawa1 , Ryohei Ikejiri2 , and Yasunobu Sumikawa3
1

Faculty of Information and Media Studies, Nagoya Bunri University, Japan
yoshikawa.ryo@nagoya-bunri.ac.jp,
2Interfaculty Initiative in Information Studies, The University of Tokyo, Japanikejiri@iii.u-tokyo.ac.jp,
3
University Education Center, Tokyo Metropolitan University, Japansumikawa-yasunobu@tmu.ac.jp

Abstract. Supporting learning history has become an important topic in education research. To discuss social issues using historical analogy, group learningcomposed of two pairs is effective. In this paper, we propose a novel interactive
system for collaborative historical analogy. This system first provides news articles to users from our database. Then, it uses a clustering algorithm that makes
groups from what the users assign event categories for news articles. After assessing the result of the clustering algorithm, our system provides two functions
for promoting collaborative learning: discussion spaces and archiving the discussions. The results of quantitative and qualitative evaluation show that our systemhave the potential to enhance group discussion and collaborative historical analogy in class.
Keywords: collaborative learning, history education, analogy, grouping

1

Introduction

One of the goals of teaching history is to find meaningful connections or analogies
over time, and supporting learning historical analogy has become an important topic
in education research. Historical analogy allows learners to study how people in thepast tried to solve issues, and then apply the acquired knowledge in order to propose
alternative solutions to similar issues in other periods. Indeed, Staley claims that history
provides not only information on the past but also alternative solutions to similar modern issues [1]. Teaching guidelines for high school education published by the Japanese
government include the ability to apply historical knowledge and concepts to modern
issues using collaborative learning [2]. Furthermore, to support collaborative historicalanalogy, researchers have developed effective learning methods [3, 4], algorithms mining past events similar to a given present event [5], and an interactive system that isuseful in class [6].
Finding similar past and present events plays a key role in promoting historicalanalogy. However, how people believe that a past and present event is similar is up tothem [7]. Furthermore, Fischer found that historical analogies are often misused, and itis necessary to be cautious when using historical analogy [8]. According to [4], grouplearning composed of two-pair discussions in history has been found to be effective for2

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collaborative learning, checking the validity of each historical analogy, and for improving each historical analogy with various points of view. Although the present study’s
findings also showed the positive potential for group learning, the gap that needs tobe addressed is that no interactive system that makes groups for studying collaborative
historical analogy currently exists.
In this paper, a novel interactive system for collaborative historical analogy is thus
proposed. The proposed system first shows news articles describing present social issues to users. It then considers users’ interests in specific issues and the particular aspects they focus on. From this information, the system groups users by combining similar users as pairs, and not similar pairs as groups. After this step, users can have a
discussion within their own groups, and an online text editor and chat plugins to facilitate the discussions are provided. Finally, the system archives the results of the users’
discussions and chat logs to use them for reflection in post-learning.
Contributions. The core contribution of the system proposed by the present paper
is that it is interactive. There are several grouping systems that function as learning
environments [9, 10]; however, these require pre-testing to analyze what the users areinterested in. The present system obtains the information by having users assign one ormore event categories that are originally defined as connecting past and present events.

2

Related Works

This section compares the proposed system with two research fields: clustering algorithms and Computer-Supported Collaborative Learning (CSCL).
2.1

Clustering Algorithm

Clustering is an algorithm to make groups. This algorithm is an important and fundamental technique in NLP, ML, and other computer science related research fields.
One of the most popular algorithms is K-means [11], which divides data into groups
satisfying the following two conditions: (1) each group must contain at least one object,and (2) each object must belong to exactly one group. K-means updates the centers ofclusters by iteratively computing the averages of all points and coordinates representing
the arithmetic mean until the specific criteria are satisfied.
Gaussian mixture model (GMM) [12] is another popular algorithm, which takes
the assumption that each object in the same cluster is generated from several Gaussian
distributions.
The main difference between our system and these past works is objective. Our
system focuses on how to collect data for clustering and to output the results of the
discussions, whereas the clustering algorithms focus on how to make groups. Thus, ourframework is orthogonal compared to past clustering work.
2.2

Computer-Supported Collaborative Learning

Computer-supported Collaborative Learning (CSCL) is a major research topic in educational technology and learning sciences with recent findings showing that well-designed

Interactive System for Collaborative Historical Analogy

3

technologies can have positive effects on collaborative learning, as well as describing
the contexts where students’ collaboration and interaction were promoted through sometechnologies [13].
In CSCL, visualization and awareness are key point to promote collaborative learning. For example, it has been reported that the visualization of participation contributed
more to performance with the designed tool that can visualize how much each groupmember contributes to his/her communication in online groups than without such tool [14].
Other research has shown that a designed online collaborative writing tool with a groupawareness functionality, which can analyze and visualize their engagement, increased
students’ behavioral engagement conpared with students not using the tool [15].
How to form an effect group has been getting more attention in CSCL researchlately [16]. According to research reviewing Argumentation-Based CSCL(ABCSCL),
many studies have focused on group composition and students’ traits [17]. For this paper, the quality of performance in heterogeneous groups is better in homogeneous ones
and there are important traits of students in ABCSCL: Gender, learning styles, willingness to argue, openness to argue, internal argumentative script. Moreover, new methodsof forming groups automatically in CSCL have been proposed based on two criteria:
the complementary skills on concepts and the learning styles obtained according to theFelder-Silverman model to make heterogeneous groups [18]. This research, however,
did not evaluate the learning effect but showed its usefulness compared with random
grouping. Similar research has provided other systems for clustering heterogeneousgroups automatically [19]. This system used the students’ grades and showed that thissystem could determine heterogeneous groups as good as groups created by a teacher.
To sum up, former CSCL research points out that the visualization of interaction that
corresponds to the effect grouping with some students’ important traits is essential forenhancing collaborative learning. However, there is no research to date about interactive
system to visualize collaborative writing by using historical analogy.
Compared with these past works, our system can easily performs group learning inhistory. If group learning is required with changing news articles and/or users several
times, it is possible to change the selections of the new data, and because the systemdoes not require data to make groups, it is easy to set up classes.

3

Design of System

3.1

System Overview

Fig. 1 shows an overview of process in our system. First, our system gets news articles
from a database. We can dynamically determine which and how many news articles wewill use. Note that we assume that teachers or lecturers prepare this database before
using our system. After collecting the texts, this system performs five steps: providing
news and related historical events, making feature vectors, making pairs and groups,
making collaborative spaces for pairs and groups, and archiving results of the discussion. In the remainder of this section, we detail each step. If the database is available,
we can quickly use this system by changing news articles and users.

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News DB
Prac. ID:1

Prac. ID:2

Prac. ID:3

News A

News D

News G

News B

News ENews HNews CNews F

News I

Provide News andRelated Hist. Events

Provide News andRelated Hist. Events

Provide News andRelated Hist. Events

Collect
All Users’ Data

Collect
All Users’ Data

Collect
All Users’ Data

Make
Pairs and Groups

Make
Pairs and Groups

Make
Pairs and Groups

Create Collab. Spaces
for Pairs and Groups

Create Collab. Spaces
for Pairs and Groups

Create Collab. Spaces
for Pairs and Groups

Make
Discussion Archives

Make
Discussion Archives

Make
Discussion Archives

Fig. 1. System overview. Each practice has some news articles about modern social issues. According to selected news, historical events user selected, this system can make pairs and groupsinteractively.

3.2

Provision of News and Related Historical Events

The objective of this step is to provide historical events to users. This system performs
this using a search engine [5]. As this search engine takes event categories that connect
past and present events, our system must show news articles to users before using thesearch engine. From these, the users select news in which they have an interest, andthen assign one or more suitable event categories to the news. After obtaining historicalevents from the search engine, this system records the two kinds of information (theassigned event categories and historical events that are results of the search engine) for
each user in order to make groups that are performed in the following steps. To makeassociation between IDs of the users and the two kinds of information, our system firstrequires a log-in process. Fig. 2 details the process between a server and clients.

Interactive System for Collaborative Historical Analogy

ClientServer [Main]

Server [Etherpad]5Server [Grouping]

Provide News and Related Histrical Events

Log InSend Notification
Search News
Provide News

Select News
Send
Selected Newsand Categories
Search Related
Historical Events
Return Result
Select Hist. Events

Fig. 2. Sequence diagram of providing news and related historical events. When a user logs into
the system, the system server get news linked to user’s practice ID from DB. After users selecting
news and categories, the server searches historical events related to user interests. Users canchoose one of historical events as a source of historical analogy.

3.3

Feature Vectors Creation

In this step, our system takes recorded results of the previous step, and then translates
them into feature vectors to make pairs and groups for the next step. Given the complete
event category list E, this step creates a feature vector for each user. If an event category
ei ∈ E is stored in the results of the previous step, we use 1 as the ith element of thefeature vector; otherwise, we use 0.
3.4

Make Pairs and Groups

This step inputs the feature vectors created in the previous step into clustering algorithms [20]. This algorithm outputs groups taking care to maximize improvements indiscussions. This algorithm first solves maximizing problem to combine two users who
focus on similar aspects for the same news. It then makes groups by solving minimizing
problem to combine two pairs that focus on different aspects for the same news. Fig. 3
shows processes for using the clustering algorithm. The main server sends users’ future

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vectors to grouping program. Once the program receiving them, the program starts tomake pairs and groups. Each pair contains two students who have the same concerns.
In contrast to pair creation, each group consists of two pairs with different concerns.
This pairs and groups creation method is designed to enhance discussion from various
perspectives. When all pairs and groups are made, the grouping program sends the result of the creation to the main server. After the server receives data, the server collates
the received result and user ID, and sends the number of their pair ID and group ID toclients.

ClientServer [Main]

Make Pairs and Groups

Server [Etherpad]

Server [Grouping]

Connect

Return “Connected”
alt[1st access]Send Feature Vectors and User ID
Create Collab. Spaces

Load Number of Users

Call API

Create Spaces for Pairs
Create Spaces for Groups
Return Results[from the 2nd time]

Send Feature Vectors and User ID
Collect Users' Data

[When Connected All Clients]

Send All Users' Data

Make Pairs & Groups
Send
Pair Number
and Group Number

Return Pairs and Groups Combination

Fig. 3. Sequence diagram of Making Pairs and Groups. In a practice, when a user reaches thisphase first, the server get the number of users in this client sent from this user. the server calls
API to Etherpad (collaborative editor plugin) server to make half number of users edit spaces forpairs, quarter number of users spaces for groups. After collected all users’ data, the server sends
this data to grouping program. The detail of this grouping algorithm, refer Ikejiri et al. [20].

Interactive System for Collaborative Historical Analogy

3.5

7

Creation of Collaborative Editor Spaces for Pairs and Groups

After taking groups of users, our system prepares their discussions. The system createscollaboration editor spaces for both pairs and groups. The trigger for this creation is a
log-in notification from the client who is the first to log into the pair discussion page.
Clients send the number of clients in a practice. Once the main server acquires thisnumber, the main server calls API to create a designated number of collaborative editor
spaces and set instructional text into them. To create spaces, the system uses Etherpadlite plugin. This plugin provides function such as coloring text and user chatting space
to make collaborative editor environment. After these processes, the server sends a pairand group ID to each client. Once clients receive these IDs, clients use these IDs as apart of the URL of the collaboration editor embedded in discussion pages, then clients
can access designated discussion pages.
3.6

Archiving Results of the Discussion

Fig. 4 shows how our system archives the results of all discussions. This is performed
if a member of a group decides to store their discussions. When the server receive therequest of archiving from the clients, the main server calls API to the Etherpad server

ClientServer [Main]

Server [Etherpad]

Server [Grouping]

Discussion

Save Pair and Group ID

Access Designated URL
Serve Collaborative Editor

Load Discussion Page
Disconnect
Make Discussion Archives

Connect

Return “Connected”
Request Archive Data

Call API
Copy Discussion Pages
Send URL

Send URL
of Archive Page

Get URL

Show Archive Page
Log Out
Disconnect

Fig. 4. Sequence diagram of Discussion phase and Making Archives. In discussion phase, clientssave received pair and group IDs. To access collaborative editor spaces, clients use these IDs as apart of URL.

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to make an copy of discussion. If the copying is successful, the server sends an URL ofarchive discussion and strings of discussion. Once clients receive the notification anddata, they embed the data into the archive phase pages to ease the viewing of the results.
We believe that this is useful when the users engage in post-study of the lecture.

4

Evaluation

In this section, we evaluate the usability of our system. We first evaluate how fast oursystem outputs results. From this quantitative analysis, we show that our system can bea useful learning system without making students frustrations in practice. We then perform qualitative analysis to understand how well our system can enhance collaborative
historical analogy in class. This analysis investigates by asking teachers after using thissystem in practice.

4.1

Quantitative Analysis

Data Preparation. We evaluate our system by changing the numbers of clients from4 to 40. As our system makes groups, we use the five different multiples of four: 4, 8,
16, 32, and 40. Note that each class usually has 40 students in Japanese high schools.
As we focus on process times in this analysis, we created artificial data to perform oursystem. In other words, we randomly assign the event categories for the data by assuming that the data selected the categories to a present news. Then, we apply the clustering
algorithm [20] described in Sec. 3.4. Analyzing qualities of the results obtained by thealgorithm is reported in the paper, we skip taking care of the qualities.

Measurements. For each the number of clients, we prepare desktop computers whose
OS is Windows 10. To measure process times, we use log files stored in the server orclients. Tabs. 1 and 2 show results of average of process time of server and clients,
respectively. We can see that each process in the server outputs results within 140 ms
and each process in clients outputs results within 400 ms.
Table 1. Average duration on server [ms].
Number of clients
Process
4
8
16
32
40
Ave.
S.D.
Create Collab. Spaces
1.878
4.093
7.718 12.362 16.009
8.412 5.199
Make Pairs & Groups
136.922 115.857 128.041 136.637 113.893 126.270 9.856
Discussion
0.552
1.095
1.976
3.329
3.729
2.136 1.231
Make Discussion Archives
3.154
1.826
2.579
4.082
5.117
3.352 1.150
TOTAL
142.505 122.872 140.314 156.410 138.748 140.170 10.681

Interactive System for Collaborative Historical Analogy

9

Table 2. Average duration on clients [ms].
Number of clients
Process
4
8
16
32
40
Ave.
S.D.
Make Pairs & Groups
193.800 191.037 163.669 172.378 167.948 177.766 12.308
Discussions
297.775 387.762 289.906 361.322 384.235 344.200 42.182
Make Discussion Archives 389.300 377.538 366.062 375.691 348.444 371.407 13.653
TOTAL
880.875 956.337 819.637 851.206 894.202 880.452 45.829

Process Times On the Server. We show that each process in our system outputs resultsof making pairs and groups within 140 ms in the case where there are 40 users in Tab.1.
Results also show total process time the on server side is within 160 ms in the samecase. This result indicates that we can use our system in practical situations because
each class has on average 40 students in Japanese high school. The process times ofCreate Collab. Spaces and Discussion are linear orders with the number of users. Thismay be caused by the amount of clients.
Process Times On Clients. Tab.2 shows that the average duration of each phase iswithin 150 ms, and the average of total process time is 880 ms. Even the case when 40
clients connect to the server, an average of total process time is within 900 ms. It means
this framework has a stability and a scalability for various cases.
4.2

Qualitative Analysis

Procedure. We hold an interview for a high school history teacher. The purpose of thisevaluation is to verify whether this system is useful for collaborative historical analogy.
For the evaluation, we set an online environment in which the teacher can experienceeach phase we presented above. To let the teacher experience a pair discussion and agroup discussion, we let researchers who have an ability of historical analogy. Thisevaluation held the following steps: Description of this evaluation, the purpose, and theusage of this system (10 min.), Using this system actually (50 min.), Interview to theteacher about this system (30 min.). We asked to the teacher from two perspectives:– Which part of this system can be effective positively for collaborative historicalanalogy?
– Which part of this system should be modified for enhancing collaborative historicalanalogy?
After the interview, we collect comments about features that may enhance collaborative historical analogy and improvements for enhancing collaborative historical analogy.
Results. The teacher referred to two features for enhancing collaborative historicalanalogy.
The first was collaborative editing and diversified perspectives. The teacher said
that each student would be thinking about how their opinion could be connected tohistorical events.

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Writing sentences with others was interesting for me. Students will demonstrate their thinking ability to edit sentences in his/her own way taking othermembers’ feelings and what other members intend to write down into consideration. So, I guess that students will be thinking about how their opinion can be connected with historical events and to each other, although I
don’t know if they can achieve this type of thinking level. I feel it’s very
interesting that students can connect historical events with their opinions
together with everyone in a group instantly and simultaneously, while I’m
not sure if their final opinions can include other viewpoints of world history.
- Did you feel that students think differently between pair discussion andgroup discussion?
I think that working in pairs makes it easier to think, but more diversified
perspectives are added into sentences when working in groups. In fact, because viewpoints in our opinion were increased after Y joined in our discussion, I think there is a merit to adding diversified perspectives in working
in groups. I feel this is a good point.
The second is coloring the authorship text. The teacher also pointed out that the authorship color function on texts is needed for collaborative editing and could be helpful
for facilitating an activity.
I think it’s an essential function. [...] It’s better to write sentences about
their opinion by coloring text in which writers can be distinguished. If there
is no feature for coloring sentences by each student, this activity would be
difficult, I think. I guess students will fail to recognize the sentence he/she
wrote.
On the other hand, the teacher commented about two improvements for enhancing
collaborative historical analogy. The first is decreasing consciousness to apply historical events while working in groups. She said that when the teacher worked in pairs andgroups, she tried to apply the historical events she selected to solve the modern issues
but that could not be accomplished completely due to the amount of thought required.
Each pair would try to tell their intention to the other when working inpairs. However, they have to make a conclusion together based on the pairs’
opinion when working in a group. Because of this, it was difficult for me toapply my intention from applying the historical event I selected, although asituation would be different if we chose a different historical event from thisone. Asserting my opinion was difficult because working with four people
required to think more diversely than in pairs. On the other hand, I also felt
something new will be created from discussions in a group since various
opinions in a group can be told.
The second is spreading gazing while editing sentences and chatting in pairs orgroups. The teacher pointed out that some of students would not be able to both edit
sentences in a collaborative editor window and participate in discussions in the chatwindow.

Interactive System for Collaborative Historical Analogy

11

Let me see. . . comments. . . some impressions came up, but I worked on
this activity desperately. Users have to watch both the edit window andchat window, right? While they are concluding their discussion about whatshould be in the future, the discussion is also proceeding in chat window.
I felt it is more or less tough for unskillful high school students to workon editing sentences and applying the ideas in the chat window simultaneously.
4.3

Discussion

The result of measuring duration of process time proved that the system will not prevent
working on a collaborative historical analogy even if the number of students increases
up to 40. This proves the scalability of this system for enhancing collaborative historicalanalogy.
According to the interview for the teacher, we found that this system has the possibility of enhancing collaborative historical analogy in the part of providing collaborative
editor spaces and coloring authorship text. These functions of the system can be effectpositively on collaborative historical analogy.
From these evaluations both from quantitative and qualitative, we proved that thissystem has an eligibility for enhancing collaborative historical analogy held in highschool history lessons.
We also found two improvements, (1) scattering gazing while editing sentences ineditor window and discussing in chat window while working in pairs and a group, (2)decreasing consciousness of applying historical events. To improve (1), we may needto embed additional instruction in a lecture or in the system. To improve (2), showing
text of selected historical events and future prediction user wrote in even if working inpairs and a group can be a solution.

5

Conclusion

Supporting collaborative historical analogy is becoming popular studies to enhance thehistorical analogy with checking the validity of its usage. In this paper, a novel interactive system for collaborative historical analogy was proposed. The proposed systemcreates groups from users’ interests in specific issues and the particular aspects they
focus on to the same news article. After the grouping users, they can have a discussion
within their own groups. The results can be archived for reflection in post-learning.
Future work will identify (a) how the system is useful for collaborative historicalanalogy with several users. As it was confirmed that it is possible to provide a practicallearning environment by checking whether the system can output results within a second, the authors will, in the future, investigate how this system can enhance collaborative historical analogy; and, (b) how stable the system works in the case of simultaneoususe in several classes. By studying this, the system will be able to provide a collaborative historical analogy environment, not only in one high school, but with remote highschools also.

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Acknowledgments
This work was supported by JSPS KAKENHI Grant Number 16K16314.

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