A Visualization System of Discussion Structure in Case Method Learning

Daichi Hisakane and Masaki Samejima

Graduate School of Information Science and Technology, Osaka University, 2-1, Yamadaoka, Suita, Osaka, Japan

Keywords:

Case Method Learning, Discussion Structure, Visualization, Graph Representation.

Abstract:

In the case method learning to develop learners’ problem-solving skills, an instructor plays a role on the

facilitation of the discussion. As one of facilitators’ techniques to support learners’ discussion, visualization

of discussion structure based on graph representation is often used. Automatic visualization of the discussion

structure without the facilitator will contribute to expanding the learning opportunities for learners. So we

propose a visualization system of the discussion structure by a graph representation with nodes and links

through speech recognition of learners’ voice. The proposed method improves the conventional method to

visualize discussion structure by considering the relation in the sequence of learners’ opinions.

1 INTRODUCTION

It is necessary to obtain problem-solving skills to an-

alyze problems and to propose solutions to the prob-

lems (Schwarz, 2002). Case method learning is one of

the ways of developing problem-solving skills (Ham-

mond, 1980). In the case method learning, an instruc-

tor makes a documented case that describes what hap-

pens in the past. The sentences in the documented

case imply some problems in the case. The learners

exchanges their opinions regarding what are problems

and how to solve the problems in the documented

case. This enables the learners to share the knowl-

edge about how to deal with the problem.

In the discussion, it sometimes happens that the

learners get off the arguing point or become too inac-

tive to express their opinions. In this case, the learners

can not share the knowledge sufﬁciently. Therefore,

the instructor facilitates the discussion neutrally by

asking some questions or showing summary of dis-

cussion in order to make the learners aware of what

point to be argued (Brooke, 2006).

As one of facilitator’s techniques to support learn-

ers’ discussion, visualizing the structure of the dis-

cussion by a graph representation is often used (Ya-

mashita, 2000). Learners can intuitively understand

and share the arguing point of the discussion and its

structure by the visualization. The graph representa-

tion of the discussion consists of ‘node’ and ‘link’.

The nodes represent learners’ opinions and the links

between nodes represent relations between the opin-

ions. Every time the learners speak their opinions,

the facilitator adds the opinions as the nodes to the

graph of the discussion structure. Watching the graph

made by the facilitator, the learners can recognize the

arguing point and ﬁnd when they miss the arguing

point. This enables to lead the learners to the effective

discussion (Gragg, 1951). However, facilitators are

lacking for learners. Learners can not always receive

the facilitator’s support of visualizing the discussion

structure.

So we propose a visualization system of the dis-

cussion structure as a graph representation in the

case method learning. In using the proposed system,

the learners speak their opinions to a microphone.

The proposed system captures the learners’ opinions

by speech recognition. Analyzing the opinions, the

proposed method makes the graph of the discussion

structure and displays to the learners.

The rest of the paper is organized as follows. Sec-

tion 2 describes the literature review of visualizing

discussion structure. Section 3 outlines our proposed

system of visualizing discussion structure. Section 4

shows the experimental results in applying the pro-

posed method to the real data from learners. Section

5 deals with the conclusion derived from the experi-

mental results.

126

Hisakane D. and Samejima M..

A Visualization System of Discussion Structure in Case Method Learning.

DOI: 10.5220/0005025101260132

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2014), pages 126-132

ISBN: 978-989-758-050-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

2 LITERATURE REVIEW

2.1 Visualization of Discussion

Structure by Graph Representation

In order to improve the discussion, there are some re-

searches on argument diagramming method for facil-

itators (Conklin and Begeman, 1988; Rienks et al.,

2005; Werner and Rittel, 1970). The discussion struc-

ture is visualized by using nodes as opinions and links

as relations between opinions. Figure 1 shows a graph

representation of an example of discussion structure

by argumentdiagramming. In the graph, an opinion at

the top is related to 2 nodes in parallel. The relations

to the other opinions in the discussion are positive or

negative response, proposal, additional explanation,

and so on (Hobbs, 1990). The facilitator creates links

between two opinions which have such relations. In

the graph of Figure 1, opinions of ‘I think a dolphin

is the most intelligent animal.’ and ‘A monkey is the

most intelligent I think.’ are answers to the opinion of

‘Which animal is the most intelligent?’. And an opin-

ion of ‘A monkey can identify itself in the mirror.’

complements the opinion of ‘A monkey is the most

intelligent I think.’ Watching this graph makes it easy

to understand the progress of the discussion. For the

purpose of supporting discussion without facilitators,

we propose an automatic graph visualization system

of the discussion structure as the previously described

graph.

The node indicates

the answer of the

top node.

Which animal is

the most

intelligent ?!

I think a dolphin is

the most intelligent

animal.!

A monkey is the

most intelligent I

think.!

A monkey can

identify itself in

the mirror.!

!Node"

!Link"

Figure 1: Graph representation of discussion structure.

2.2 Conventional Method of Automatic

Discussion Graph Generation

Zhao et al. proposed a visualization method of the

relation between keywords extracted from the min-

utes of the meeting (Zhao et al., 2006). However,

learners have to comprehend not only the relation be-

tween the keywords but also the relations between

the opinions. So, this conventional method is insuf-

ﬁcient for the learners to understand the discussion

structure. On the other hand, based on the change

of the word frequency in a minute of the discussion,

Matsumura et al. addressed visualizing the relations

of opinions after the discussion (Matsumura et al.,

2003). For n opinions recorded in the minute, let

(t = 1, 2, ··· , n) denote the tth opinion by a learner.

A set of the series from the 1st opinion to the nth opin-

ion is represented as a window of opinions W(1, n) =

, S

, ··· , S

}. Subsequently, a feature vector of

the each opinion V

= {I

, I

, · ·· , I

} is calcu-

lated to clarify what the opinion means (Gerard and

McGill, 1983). I

(i = 1, 2, 3, ··· , n) represents the

weight of the each ith word w

(i = 1, 2, 3, ·· · , n) in

the opinion S

by the following formula:

= tf(w

, S

)



log

t f(w

, W(1, n))

tf(w

.W(1, n))−tf(w

, S

)



(1)

where tf (w

, S

) is the frequency of the word w

the opinion S

and tf(w

, W(1, n)) is the frequency

of the word w

in the window W(1, n). The conven-

tional method extracts the words from each opinion

by morphological analysis and calculates tf(w

, S

)

and t f(w

, W(1, n)) automatically. Top 20 weights of

words as the signiﬁcant words are used for the feature

vector words V

In the discussion, learners often rephrase the past

opinions for summarizing or clarifying past opinions

(Okumura and Takeo, 1994). Because there are sim-

ilar words between an opinion and its rephrased one,

this conventional method identiﬁes the similar words

with thesaurus, uniﬁes the similar words to one word,

and creates links between similar opinions (Hearst,

1997). The similarity is calculated as the following

cosine similarity between V

and V

sim(V

, V

) =

·V

| ·|V

(2)

If a learner speaks an opinion B long after an opin-

ion A from a learner, the opinions A and B rarely have

a relation each other. So, links should be created to

the recent opinions by calculating similarity between

the opinions. When an opinion is inputted, feature

vectors of all opinions V

(t = 1, 2, ·· · , n) are decided

by the formula (1). Then similarities between V

and

t+1

, V

t+2

, · ·· , V

t+a

(t + a ≤ n) are calculated by the

formula (2) where a is a parameter that indicates how

many recent opinions inﬂuence the current opinion. If

the similarity is more than or equal to a certain thresh-

old, the link is created between these opinions.

AVisualizationSystemofDiscussionStructureinCaseMethodLearning

127

2.3 Research Issue

Matsumura’s method visualizes the discussion struc-

ture from a minute after the discussion. The minute is

well written by a secretary with adding some informa-

tion. However, the learners often speak their opinions

with referring the past opinions in discussion. Most

of the opinions include less words than the minutes

include (Fillmore, 2011). So the links are not created

between opinions by the conventional method even if

the opinions have a relation. In order to create the

link, it is necessary to ﬁnd the related opinions by

not only the similar words but also the other words

such as demonstratives. In addition, the sequence of

opinions would be useful to understand the discus-

sion as people can understand the context from the

sequence of the opinions. We also need to consider

the sequence of opinions to create the link.

3 VISUALIZATION SYSTEM OF

DISCUSSION STRUCTURE

3.1 Outline of the Visualization System

Figure 2 is the outline of our proposed system. The

input of this system is a sequential opinion from learn-

ers through a microphone mounted on the system.

The proposed system converts learners’ voice to a

sentence by speech recognition. We adopted a speech

recognition software ‘Julius’ that is based on large-

scale speech corpus (Lee and Kawahara, 2009). How-

ever, it is known that precision of speech recogni-

tion is not enough to support discussion (Zhao et al.,

2006). So, in order to improve the accuracy, we pre-

liminary input the documented case to the Julius, and

emphasize on the words in the document for speech

recognition by maximum likelihood estimation. This

means that the learners tend to use the words that ap-

pear in the documented case. In this way, accuracy

of recognizing the speech related to the documented

case can be improved.

After extracting nodes as the sentences converted

from learners’ voice, the system identiﬁes the rela-

tion between nodes and visualizes the structure of the

discussion. First, the proposed method identiﬁes the

relation by the dictionary of demonstratives and con-

nectives that indicate the relation between opinions.

Then we apply Matsumura’s method using the simi-

larity between recent opinions. Finally, based on the

sequence of opinions that have been already linked

each other, the proposed system complements links

to the nodes that still have no links.

Analyze a

sentence

Learners’

Voice

Output

microphone!

Opinion!

I think a dolphin is

the most intelligent

animal.

Speech Recognition by Julius!

Speech

Sentence!

Transform!

!"#$%&'"(")*+,$%&"%-.!

Speech Waveform!

Input

/*0123&#3)""

4(-3

5$3"(&%2(+"""!

Identify Relation between Nodes!

Use the dictionary of

demonstratives and connectives.

Use similarity

between recent opinions

(conventional method).

Complement links.!

Graph representation of the

discussion structure

Figure 2: Conﬁguration of discussion support system.

In the following sections, we describe the step of

only identifying the relations between nodes because

we have already conﬁrmed that the speech recogni-

tion is well done by the existing tool.

3.2 Graph Visualization by

Demonstratives and Connective

Opinions in the discussion sometimes include demon-

stratives, such as “this”, “that”, “as you said” etc.,

that refer a past opinion. The learners need to under-

stand the past opinion referred by the demonstrative

in order to understand the opinion including demon-

stratives. So the opinions including the demonstra-

tives tend to have a relation to other opinions referred

by the demonstratives. Therefore when the opinions

include the demonstratives, the system identiﬁes the

opinion including a word or a sentence referred by

the demonstrative, and creates a link between them.

In this system, when the opinion has the demon-

stratives, the opinion links with the previous opinion

with assuming that a demonstrative may point to the

previous opinion. In addition, connectives are the

words which represent the relation between the sen-

tences. So the opinions including the connective can

have a relation to the recent opinions.

The proposed system identiﬁes the opinions that

include demonstratives or connectives by keyword

matching to the words in the dictionary that the facili-

tators preliminary deﬁne the demonstratives and con-

nectives on. This enables to ﬁnd the relation between

opinions more accurately.

3.3 Graph Visualization by Similarity

between Recent Opinions

The Matsumura’s method of the visualization of dis-

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128

St+1!

St+2! St+3! St+4!

St+5!

Create links

sim(Vt,Vt+1)=0.040

sim(Vt,Vt+2)=0.218 "#θ

sim(Vt,Vt+3)=0.045

sim(Vt,Vt+4)=0.131 "#θ

sim(Vt,Vt+5)=0.065

Threshold θ = 0.1

Calculate similarity!

Figure 3: Create links based on the similarity between re-

cent opinions.

cussion structure, introduced in section 2.2, is also

effective to identify the relation between opinions.

So, we apply the conventional method to the opin-

ions. When an opinion is inputted, the system cal-

culates similarity between opinions and creates links

as shown in Figure 3. In the Figure 3, in case of

a = 5 as the number of recent opinions where a opin-

ion has an inﬂuence, the similarities between S

and

t+1

, S

t+2

, · ·· , S

t+5

are calculated. Then links are cre-

ated from S

to S

t+2

and S

t+4

because sim(V

, V

t+2

)

and sim(V

, V

t+4

) are over the threshold θ.

3.4 Link Complement Considering the

Relation in Sequence of Opinions

In discussion, learners often speak their opinions with

referring the past opinions. Even if we visualize the

structure of discussion only by the method of using

demonstratives, connectives, and similarity between

recent opinions, some of links are not created between

opinions because the learners do not always use the

same words for the same meaning. So, the opinions

that have different words from other opinions tend to

be separate nodes. However, as the discussion goes

on, the different similar words are gradually used by

the learners, which increases the similarity and en-

ables to create links with the separate nodes without

any links to other nodes.

So we link the separate nodes to other nodes be-

fore and after the separate node. An opinion of the

separate node may be connected to earlier or later

opinions than the opinion on the separate node by

considering the relation in the sequence of learners’

opinion. Figure 4 shows the process of link comple-

ment. First, the similarity of the separate node to the

other opinions before and after the separate node are

calculated by the formula (2). The number of opin-

ions whose similarities are calculated is the same as

the number of recent opinions a in section 3.2. Be-

cause the separate node has not been linked by the

St-1

St+1

St+2

A separate node!

sim(Vt,Vt-a)=0.003

!!!

sim(Vt,Vt-1)=0.064 "#θ

sim(Vt,Vt+1)=0.014

!!!

sim(Vt,Vt+1)=0.008

Complement threshold

θ’ = 0.05

St-1

St+1

St+2

Complement

a link!

Calculate similarity!

Figure 4: Complement links into separate nodes.

method described in section 3.2, the similarity of each

separate node must be below the threshold θ. So we

introduce another threshold θ

′

that is smaller than θ

in order to create links with the separate nodes. When

the similarity is more than or equal to threshold θ

′

, a

link is complemented between the nodes. In Figure

4, S

is a separate node, and earlier or later opinions

t−1

, S

t+1

, S

t+2

have links. Then a new link is created

between S

t−1

and S

because the similarity of them is

more than or equal to complement threshold θ

′

4 EXPERIMENT

4.1 Outline of the Experiment

In this experiment, we use a documented case that

is really used in an educational institute for project

managers. The documented case describes prob-

lems about delay in information system development

project because of lack of communication in the

project, difﬁcult process of agreement, and so on. We

ask 2 groups of 3 students, Group A and Group B,

to discuss the case for 20 minutes. The numbers of

learners’ opinions in Group A and in Group B are 50

and 65, respectively. During their discussion, a facil-

itator updated the graph of discussion structure auto-

matically using two methods shown in the followings:

• Conventional method : Visualize discussion struc-

ture based on conventional method described in

section 2.2 (Matsumura et al., 2003).

• Proposed method : Visualize discussion struc-

ture using demonstratives and connectives, simi-

larity between recent opinions and link comple-

ment considering the relation in the sequence of

opinions.

In order to evaluate these graphs , we compare the

graphs by both methods to the graph made by a facil-

itator. Total number of the links in the graph made by

a facilitator was 45 for Group A and 57 for Group B.

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129

Figure 5 shows the part of the graph made by the facil-

itator for Group A. The nodes are numbered in serial

order. We evaluate these methods by precision rate

and recall rate calculated by the formulas (3) and (4).

We determined a = 5 as the number of recent opinions

where a opinion has an inﬂuence, and set thresholds

as θ = 0.1, θ

′

= 0.05.

Precision rate =

The number of links created correctly

Total amount of links

(3)

Recall rate =

The number of links created correctly

The number of correct links judged by a facilitator

(4)

18) The problem is that he result of the

business 2 team is extremely separate

from the plan of the team in the progress

graph of development process.

19) This is because the

reader of business 2

team didn’t make a plan

considering the ability of

the team members and

the development scale.

20) 2 months have passed, so

changing the leader is one of

the good measure because

the leader is not suitable.

21) I think one of the

factor of this problem is

that the development

scale of the business 2

team is the most large.

22) The other factor of

this problem is the

members are not told

about detail of this plan

due to the quantity of the

members because new

members increase highly.

23) So it is good to change the

leader of this team, and it is also

good to add the members who

assist the new members.

24) These members should have

experienced the first plan, who are

in the other team now.

25) I have the same opinion as B. !

Figure 5: Graph representation of discussion structure made

by a facilitator for Group A.

4.2 Experimental Result

Figure 6 and 7 show the transition of precision rate

and recall rate by both methods during discussion by

Group A. Figure 8 and 9 show the transition of preci-

sion rate and recall rate by both methods during dis-

cussion by Group B. The number of complemented

links is also shown in both graphs. And the ﬁnal preci-

sion rate and recall rate after the discussion are shown

in Table 1. According to Figure 6, 7, 8 and 9, the pro-

posed method improves both precision rate and recall

rate during the discussion compared to the conven-

tion method. On average, the proposed method im-

proves both precision rate and recall rate by 5.6% and

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35 40 45 50

# of complemented links!

Precision rate!

Number of speech!

# of complemented links

Proposed method

Conventional method

Figure 6: Transition of precision rate for Group A.

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35 40 45 50

# of complemented links

Recall rate

Number of speech

# of complemented links

Proposed method

Conventional method

Figure 7: Transition of recall rate for Group A.

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35 40 45 50

# of complemented links!

Precision rate!

Number of speech!

# of complemented links

Proposed method

Conventional method

Figure 8: Transition of precision rate for Group B.

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35 40 45 50

# of complemented links!

Recall rate!

Number of speech!

# of complemented links

Proposed method

Conventional method

Figure 9: Transition of recall rate for Group B.

23.8% respectively after the discussion. So precision

rate and recall rate can be improvedby identifying the

relation with demonstrativesand connectives, similar-

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130

Table 1: Precision rate and recall rate after the discussion.

(B method Precision rate Recall rate

Group A

Conventional

method

14/35

(40.0%)

14/45

(31.1%)

Proposed

method

26/53

(49.1%)

25/45

(57.8%)

Group B

Conventional

method

9/18

(50.0%)

9/57

(15.8%)

Proposed

method

21/41

(51.2%)

21/57

(36.8%)

Average

Conventional

method

44.6% 23.5%

Proposed

method

50.2% 47.3%

ity between recent opinions, and link complement.

The graph of visualized discussion structure by

the conventional method and the proposed method for

Group A are shown in Figure 10 and 11, respectively.

The graph by the proposed method has less separate

nodes than the graph by the conventional method. As

these graphs show, the separate nodes can be reduced

by complementing links considering the relation in

the sequence of learners’ opinions, even if the opin-

ions do not include the same words.

We discuss the effectiveness of additional steps in

the proposed method: creating links by demonstra-

tives and connectives and complementing links by the

sequence of opinions. Focusing on the separate nodes

in Figure 10, we ﬁnd that opinions of (19) and (21)

include the demonstrative words of “That”, “This”,

and “the same as”. So, these opinions and an opin-

ion of (18) that is linked to (19) can have links to the

other opinions. As shown in Figure 6, Figure7, Fig-

ure 8 and Figure 9, the proposed method can improve

the recall rate and the precision rate during the dis-

cussion compared to the conventional method. This

is because links are complemented by the sequence

of opinions every time a learner speeches a new opin-

ion. At the 8th and 9th opinions in Group A, the pre-

cision rate by the conventional method is a little bit

better than one by the proposed method. But, at the

same opinions, the proposed can improve the recall

rate drastically. Because it is important to visualize

the discussion structure during the discussion, it can

be expected that the proposed system contributes to

supporting the discussion.

Finally, we evaluate the understandability of those

graphs. The graph made by the conventional method

has many nodes that are not connected to any other

nodes. For learners, to see this graph is almost the

same as to see the history of opinions, which makes

18) The problem is

that he result of the

business 2 team is

extremely separate

from the plan of the

team in the progress

graph of development

process.

19) This is because

the reader of

business 2 team

didn’t make a plan

considering the

ability of the team

members and the

development scale.

20) 2 months have

passed, so changing

the leader is one of

the good measure

because the leader is

not suitable.

21) I think one of

the factor of this

problem is that

the development

scale of the

business 2 team

is the most large.

22) The other factor of this

problem is the members are

not told about detail of this

plan due to the quantity of the

members because new

members increase highly.

24) These members should have

experienced the first plan, who are in the

other team now.

25) I have the

same opinion

as B.

23) So it is good to change the leader of

this team, and it is also good to add the

members who assist the new members.

Figure 10: Graph representation of discussion structure by

conventional method for Group A.

18) The problem is

that he result of the

business 2 team is

extremely separate

from the plan of the

team in the progress

graph of development

process.

19) This is because

the reader of business

2 team didn’t make a

plan considering the

ability of the team

members and the

development scale.

20) 2 months have

passed, so changing

the leader is one of

the good measure

because the leader is

not suitable.

21) I think one of the

factor of this problem is

that the development

scale of the business 2

team is the most large.!

22) The other factor of

this problem is the

members are not told

about detail of this plan

due to the quantity of the

members because new

members increase highly.

24) These

members should

have experienced

the first plan, who

are in the other

team now.

23) So it is good to

change the leader of

this team, and it is

also good to add the

members who assist

the new members.

25) I have the

same opinion as B.

Figure 11: Graph representation of discussion structure by

proposed method for Group A.

little sense. On the other hand, the graph made by the

proposed method has more node that are connected to

other nodes. It gets easier for learners to understand

the relation.

4.3 Future Issues

As Figure 6, Figure7, Figure 8 and Figure 9 show,

the recall rate and the precision rate are still not so

good even if we apply the proposed method. In the

case method learning, the learners often uses domain-

AVisualizationSystemofDiscussionStructureinCaseMethodLearning

131

speciﬁc words such as “parent company”, “sub sys-

tem”, and so on. The proposed system has not found

the relations based on the domain-speciﬁc words. As

our future work, we will try to obtain the knowledge

including the domain-speciﬁc words from the other

resources, i.e. the documented case, the other lecture

book, wikipedia and so on. In addition, the size of

the graph gets larger than we expects. Some learn-

ers indicate difﬁculty in ﬁnding the past opinions and

their relations. So, contraction of the discussion struc-

ture is also necessary. Furthermore, coloring nodes of

important opinions will help the learners’ understand-

ings.

5 CONCLUSION

We proposed the method identifying opinions which

have relations by demonstrativesand connectives, and

complementing links with the separate nodes by con-

sidering the relation in the sequence of learners’ opin-

ions. In the experiment, on average, we conﬁrmed

that the proposed method could improve both pre-

cision rate and recall rate by 5.6% and 23.8% re-

spectively compared with the conventional method.

Opinions including demonstratives and connectives

are linked with the previous opinion in this method.

However, it is necessary to create links by extracting

the content correctly, so we will improve the method

in the future. In addition, we will develop the method

in order to complement links more precisely.

ACKNOWLEDGEMENTS

This work was partially supported by KAK-

ENHI:JSPS (25730205).

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