Online Discussion Transition Analysis
for Group Learning Support
Yasunobu Sumikawa

Akikazu Takada

Akira Ichinose

Ari Murakami, Yuki Toyono

Takushoku University
Tokyo, Japanysumikaw@cs.takushoku-u.ac.jp

Ddrive K.K.
Ehime, Japan
takatter@ddrive.ai

First Torrent LLC.
Fukuoka, Japanichinose@ﬁrst-torrent.com

Ddrive K.K.
Ehime, Japan{ari,yuki}@ddrive.ai

Ryohei Ikejiri, Meiko Sakasegawa

Kaoru Sekine

Yuhei Yamauchi

The University of Tokyo
Tokyo, Japan{ikejiri,sakasegawa}@iii.u-tokyo.ac.jp

Tohoku University
Miyagi, Japankaoru.sekine@gmail.com

The University of Tokyo
Tokyo, Japanyamauchi@iii.u-tokyo.ac.jp

Abstract—Group learning has been recognized as an effective
learning method. Recently, group learning using online communication tools and face-to-face group learning have been increasing.
This change in the learning environment has highlighted theimportance of supporting teachers in monitoring the learning
progress of each group. In this study, we propose an algorithm
to analyze the discussion transitions. The algorithm ﬁrst extracts
the entities and Wikipedia categories from the discussion text. It
then applies a queue model, which limits the analysis to the mostrecent topic, or a memory model, which emphasizes repeated
topics. Subsequently, it triggers another algorithm that allocatesa Wikipedia category as the main theme for each discussion
text. To evaluate the effectiveness of the proposed algorithms, wetested them on the W2E dataset, which includes topics comprisingmultiple events and an actual conversational dataset. The test
results conﬁrmed that the algorithms analyzed the discussion
transitions and main themes with high accuracy.
Index Terms—Discussion analysis, transition analysis, topic
detection and tracking, Wikipedia

I. I NTRODUCTION
The educational technology and learning science ﬁelds haverecognized group work as an effective method for higherorder learning. In conjunction with this research ﬁnding, theOrganization for Economic Co-operation and Development
and Japan’s new guidelines [6] urge group work in classrooms.
Recently, opportunities for group learning have been increasing through face-to-face group learning and the useof online communication tools. Unlike face-to-face classes,
online tools use mechanisms, such as breakout rooms for group
activities, which limit the participation of non-group members.
While these mechanisms assist in group activities, teachers
ﬁnd it difﬁcult to monitor all the groups simultaneously. Thus,
providing support for group learning and teachers is becoming
increasingly important.
This study aimed to analyze whether the discussions ineach group are appropriately transitioning and what themes
The authors would like to acknowledge Google Inc. for supporting thisproject through the “Research on developing a system for detecting the groups
that need group work support.”

the current discussions are. These analyses will help teachers understand the challenges participants face during grouplearning. Below are some examples of the kind of discussions
in which teachers would intervene in this study. In theseexamples, we assume two students, A and B, discussing thedifference between birds and dinosaurs.
A: Let’s ﬁnd out the differences between birds and dinosaurs.
B: I will look into dinosaurs.
A: Then I will look into birds.
A: There is a difference between living and extinct.
B: Sure. In addition, are there any differences in the skeletons of dinosaurs and birds?
A: Good. Let’s look into the differences between their skeletons now.
B: Speaking of which, what’s the next class?
A: I don’t know.
B: Oh, a recent study reports about the characteristics ofdinosaurs.
A: I’m reading those research results, but I don’t understand.
B: I wonder what these results are.
The above discussion was congruous with the theme until
it focused on skeletal differences. However, after deciding toresearch independently, B remarked, “what’s the next lesson?”This question is irrelevant to the discussion. Then, B foundthe latest research results, and the discussion returned to theoriginal theme of the conversation. However, both A and B
had difﬁculty understanding the report content, thus impeding
further discussion. For such cases, this study proposes consulting a teacher so that the group can continue the discussion on
birds vs. dinosaurs in two scenarios: (1) when the discussion
moved inappropriately, as in the case of the question, “what’s
the next lesson?” and (2) when the discussion stalled because
neither understood the research result. In this example, we
assumed that in the former case, the conversation between thetwo students returned immediately to the theme. If the situation
had persisted, teachers would have been required to address
another challenge of avoiding wasting learning time.

We assumed that although the discussion transition happens
over time, the change is continuous. To represent this assumption, we propose a queue model that limits the analysis tothe most recent texts, and a memory model that emphasizes
repeated topics. In addition, we present two algorithms toanalyze the main themes of the discussion texts. The ﬁrst ﬁnds
the center of gravity after mapping the Wikipedia categories
obtained from texts to the coordinate space. The second
considers the important themes repeated over time.
When a discussion text is given, the output of our algorithms
are a numerical value indicating that the discussion is appropriately transitioning and a Wikipedia category representing
the main theme. These results would help teachers ﬁnd groups
that require intervention.
We applied the proposed algorithms to the W2E dataset
to evaluate their effectiveness. This dataset includes topics
comprising multiple events and actual conversational data. The
test results conﬁrmed that our algorithms correctly analyzed
the discussion transitions for 98% of the texts. They allocatedappropriate Wikipedia categories as the main themes fordiscussion texts with 70% accuracy. We created a subset ofdeviating and stalling discussions from the W2E dataset andfound that the proposed algorithms could correctly detect allof them. Finally, we performed the same analysis on actual
conversational text to conﬁrm they analyzed of the discussion
transition properly.
The remainder of this paper is structured as follows. In
the next section we present related work. In Section III, theproposed algorithm. Section IV provides the experimental
evaluation results with detail analyses. Finally, the last section
concludes the paper and outlines future work.
II. R ELATED W ORK

learning as topic detection and tracking (TDT). Radinsky andDavidovich proposed a TDT algorithm for predicting future
events after mining causal relationships [7]. Their algorithm
analyzes whether the entered news is on the same topic aspreviously reported. If it is the same, the algorithm associates
this latest news with the chain it has accumulated as news.
This procedure is identical to the discussion transition
analysis in this study. If the entered discussion text is the sameas the previous content, our algorithm also accumulates new
text. Otherwise, the algorithm discards the newly entered text.
III. A LGORITHM
Fig. 1 shows an overview of the proposed algorithm. Before applying the algorithm, we assumed that the learner’s
discussion had already been converted to text1 . The algorithm
ﬁrst extracts the entities and Wikipedia categories from thetext. These are used in the discussion transition analysis
that reveals three types of inputs: appropriately transitioning
discussion, deviating discussion, and stalling discussion. If thealgorithm determines the input text as appropriately transitioning discussion, the algorithm records the result so thatfor subsequent analyses. Subsequently, the algorithm returns anumerical value indicating a recommendation for the teacher
to intervene. Finally, the algorithm analyzes the main themeof each text to identify the ongoing discussions of each groupeasily. The two results are then presented on the screen for
the teacher to review.
The following sections describe the processes of extracting
entities and Wikipedia categories from discussion texts. We
then describe the discussion transition and thematic analyses.
Student group

A. Discussion Analysis
Research has conducted on supporting teachers for grouplearning. Taouﬁq et al. proposed a system using latent Dirichlet allocation (LDA) to visualize learners’ discussions [8]. The
system applies LDA to each discussion text and displays thewords that constitute the topic with the highest probability,
resizing them by their probabilities in the UI.
Taouﬁq et al.’s study aimed to assist teachers in providing
appropriate feedback to all groups. By contrast, this study
aimed to discover groups in which teachers should intervene.
As the objectives of these two studies are different, they canbe used simultaneously. For example, if no group requires theteacher’s intervention the system proposed by Taouﬁq et al.
could assist the group in better discussions. However, if the
discussion is not going well, the proposed algorithm can help
teachers promptly join the group in the middle of a session,organize the issues, and encourage learners to discuss.
B. Topic Detection and Tracking
This study analyzed whether each group was adequately
engaged in the discussion. This evaluation is similar to analyzing whether an entered text has the same content as theprevious texts. Such analysis has been studied in machine

Tokenizing

Speech-to-Text
Proposed algorithm
Discussion
Transision
Analysis

Entity&Category
Analysis

Main
Theme
Analysis

Visualization

TeacherFig. 1. System overview
1 Our implementation applies Google Cloud Speech-to-Text to obtain discussion texts.
https://cloud.google.com/speech-to-text

A. Entity & Category Analyses
In this study, we have used two terms: entity and Wikipedia
category. The former represents those used in the text, whereas
the latter represents the meaning of the text. We can use any
existing tools for entity detection2 . To map descriptions from
Wikipedia, we employ explicit semantic analysis (ESA) [2],which outputs Wikipedia articles ranked based on their relevance to the input text. ESA helps retrieve Wikipedia articles
highly relevant to the discussion text. Wikipedia articles haveseveral categories deﬁned by their editors. We regarded thesecategories as abstractions of their articles. In other words, thisWikipedia category set is considered the topic of the input
discussion text. However, Wikipedia categories are assigned ifthey are relevant to the article; some categories are unrelated tothe discussion. To remove such noise, we analyzed only thosecategories containing nouns extracted from the discussion
text. The entity and Wikipedia categories produced from thisprocess were aggregated into a word list words for further
analysis.
B. Discussion Transition Analysis
This study proposes two models for analyzing discussion
transitions: queue and memory models. Both models assume
that themes change over time. However, these two models
function differently to capture this change. The queue model
excludes old text from the analysis. The memory model
assigns weights to new analysis results assuming topics changeover time; however, repeatedly appearing topics are central tothe discussion content. To implement this assumption, ourmodel reduces these values over time. We describe the twomodels in the following sections.
1) Queue model: Queue is a type of data structure thatremoves objects in the First In First Out order. It analyzes thediscussions from the most recent texts by storing these texts
and excluding the oldest ones.
This model limits the analysis of the discussion transition
to the most recent N pieces. It assumes a uniform distribution,
implying the analysis is equal for N texts. In other words, themodel analyzes whether the new text matches any N previous
results. If the number of common results is below a threshold
α, the discussion is considered deviating. In contrast, if thecommon number is greater than a threshold β, the discussion
is considered stalled.
Eq. 1 shows the formal equation of this model.
∑
δ(w, Words(N ))QVal (words, N ) =w∈words| words ∪ Words(N ) |(1)

where Words(N ) is a function that retrieves words from N
past discussion texts. δ(w, Words(N )) denotes a function that
returns 1 if w is included in Words(N ); otherwise, it returns 0.
The numerator of this equation counts the number of common
words between the past and the new texts. As the denominator
2 We use AutoML in our implementation. https://cloud.google.com/natural-language/
automl/docs

represents the number of words in all analyzed texts, thisdivision expresses the number as a probability. In the queue
model, if the value of this equation is between α and β, newtext is added to the discussion text, and the oldest text is
excluded.
2) Memory model: This model assumes that content discussed repeatedly for a long time is more important than themost recent content. We represent this assumption by modelingthe memory forgetting curve [3], which describes how content
is retained through repeated learning.
Fig. 2 shows the weight calculation for words included inwords. It shows the weight calculation of three texts generated
at times t, t’, and t” containing the words “soccer”, “sports”,
and “Japan”. The model reduces the weight of each word,
exponentially over time. However, the weights for each word
are summed so that words that appear repeatedly have a higher
weight. We use the exponential function exp to calculate theweights according to the forgetting curve of memory model.
If the difference between the current time t and the time t′ ofthe previously posted text increases, the weight of the word
decreases. Thus, this difference is given as the argument ofexp. The following equation shows the proposed method for
calculating the weights of a single text using:′f (t, t′ ) = exp(−(t−t ))(2)Each time a new text is entered, the weights are recalculated
because the already parsed text becomes older than before.
Additionally, because the same word is assigned different
weights in each text, the sum of these weights is the ﬁnal
weight of that word at time t. We calculate the sum of Eq.
2 to ﬁnd words that represent the current themes from thealready parsed text, as shown in the following equation:∑
W (w, t) =f (t, t′ ) × δ(w, text(t′ ))(3)
t′ ∈PText(t)where w is a word. The function PText(t) retrieves the time
recorded before time t. δ is a function that returns 1 if w isused in the text(t′ ); otherwise, it returns 0.

Score
100

SoccerSportsJapan

Total score
Sports: 110
Japan: 50
Soccer: 30
Soccer
Soccer: 80

80
60

Japan: 50

40
Soccer: 30
Sports: 30

20
0
t’’’

t’’

t’

Fig. 2. Memory model

t

Time

Finally, we sum up the analyzed results of all previous texts.
The ﬁnal result is calculated using the following equation:∑ ∑(4)
MVal (words, N ) =W (w, t)t∈N w∈words

Similar to the queue model, we compared this score withthe two thresholds, α and β, to determine the results. The
memory model compares the score of the text in PText(t)with the two thresholds so that it is analyzed at time t.
C. Main Theme Analysis
To analyze the main theme of each text, we select theappropriate Wikipedia categories obtained by ESA. Wikipedia
categories were originally introduced to organize Wikipedia
articles; however, these categories are considered as a summary. Using this feature, we regard the Wikipedia categories
obtained for a discussion text as abstractions and select themain theme from them. This selection method uses a different
procedure for each discussion transition model to quantify
each category and select the appropriate one as the main
theme.
1) Queue model: The queue model uniformly analyzes
the most recent text, thus, embedding all categories into thecoordinate space. Each Wikipedia category is assigned to one
or more Wikipedia articles. We regard articles belonged tothis category as its describing texts. We ﬁrst train Doc2Vec [5]using all Wikipedia articles to embed these categories. We thenapply the Doc2Vec model to texts of all Wikipedia articles,
collated as one document from each category. This process
allows us to convert each Wikipedia category into a feature
vector. We then compute the center of gravity. Subsequently,
we regard the Wikipedia category that is the closest to thecenter of gravity in coordinate space as the main theme.
2) Memory model: The memory model computes a scorefor each Wikipedia category using Eq. 4. Wikipedia categories
repeatedly appearing over a long period are assigned a higherscore. Thus, we regard the Wikipedia category with the highest
value obtained from Eq. 4 as the main theme.
D. Algorithm Overview
Algorithm 1 shows the pseudo code for the proposed
algorithm. It ﬁrst extracts entities and Wikipedia categories
from the text input txt (lines 2∼4). Then, we apply one of
the two models, queue or memory, described in Sec. III-B
to integrate these results in line 5 to obtain the score forall discussion texts (lines 6∼7). If this score is between thethresholds, we consider the discussion progressing properly
and record it as the discussion text (lines 8∼10). Finally, lines
11∼12 show the analyzed main theme of the input text. This
result and the probability values of the discussion transition
are returned as the results of proposed algorithm.
IV. E VALUATION
A. Research Questions
In this study, we performed experimental evaluations basedon the following ﬁve research questions:

Algorithm 1 Algorithm overview
Input: A text txt, discussion texts N , thresholds α, βOutput: A score score, main theme m
1: Function DiscussionAnalysis(txt, N, α, β)2:// Text Analysis
3:
entities ← ExtractEntities(txt)4:
WikiCats ← ESAbasedWikiCatExtraction(txt)5:words ← entities + WikiCats
6:
7:
8:
9:
10:
11:
12:
13:

// Discussion Transition Analysis
score ← a result of Eq. 1 or Eq. 4
if α < score < βN ← txt // Store txt to discussion texts
end if// Main Theme Analysism ← results of Sec. III-C
return score, m

RQ1: Can our algorithm detect properly transitioning
discussions?
• RQ2: Can our algorithm detect stalling discussions?
• RQ3: Can our algorithm detect deviating discussions?
• RQ4: Can our algorithm assign the Wikipedia category
as the main theme for text?
• RQ5: How well does our algorithm work for actual
conversational texts?
•

B. Experimental setting1) Dataset for RQ1 and RQ4: As RQ1 and RQ4 are
TDT problems, we performed quantitative evaluations on theW2E dataset [4] created for the TDT problem. W2E dataset
includes topics comprising events reported by news agencies,
such as Reuters, the New York Times, and BCC. This dataset
had topics from news reports of 2016; however, 3,083 topics
are available. The events are stored in the English version
of Wikipedia. These events were manually aggregated withmultiple related events as topics.
The W2E dataset was created as a ground-truth TDT
dataset; however, some topics includes only one event. We
selected topics containing more than 3 events as we analyze
the topic transition of multiple texts. After the ﬁltering, wecollected 269 topics containing 1,781 events from the W2E
dataset. In our experimental evaluation, each topic had oneof the following 9 categories: Sport (S), Armed conﬂicts
and attacks (AA), Business and economy (BE), Arts andculture (AC), Law and crime (LC), Politics and elections (PE),International relations (IR), Disasters and accidents (DA), andHealth and medicine (HM).
Table I shows the statistics of the ﬁltered W2E dataset. Table
II shows the average number of events, topics, and tokens in
the texts for each category. Additionally, we opened the ﬁltered
W2E dataset used in this study for re-examination3 .
3 https://onl.tw/NnFiEXMTABLE IS TATISTICS OF TEST DATASET.

TABLE IIIR ESULTS OF RQ2.

Num. of topics
Num. of events
Ave. num. of tokens
Ave. num. of events per topic
Num. of subjects for RQ5
Num. of characters in conversational text for RQ5

269
1,781
32.9
6.62
7
7,081

TABLE IIS TATISTICS OF TEST DATASET BY CATEGORY.

Ave. Num. of events
Num. of topics
Ave. Num. of tokens
Ave. Num. of events
Num. of topics
Ave. Num. of tokens

S
4.4
13
35.5
LC
5.7
24
34.6

AA
10.0
69
30.3
PE
4.8
73
32.6

BE
5.8
9
29.0
IR
4.8
56
38.4

AC
3.2
4
45.4
DA
9.8
15
34.3

HM
8.0
6
28.4

2) Dataset for RQ2: To evaluate RQ24 , we extended theW2E dataset to simulate stalled discussions by duplicating
events in each topic. This evaluation used 269 topics as wellas RQ1.
3) Dataset for RQ3: For the evaluation of RQ35 , weextended the W2E dataset again by inserting an event of a
topic to the end of another topic to produce unrelated andunexpected stories. We performed this operation for all two
topic combinations. However, to ensure that two completely
unrelated topics were mixed, mixing was discontinued if oneof the following conditions was satisﬁed:• If the text of the event to be added and the text of all theevents of the topic have common words.
• The feature vector created by the latent semantic analysis(LSA) for the texts of the event to be added depends onthe feature vector created by the LSA for the texts ofeach event of the topic.
To test the second condition, we used the mutual information shown in the following equation:()∑∑
p(a, b)MI (A, B) =p(a, b) log(5)p(a)p(b)a∈Ab∈BThe higher the measurement score, the more the correlation
between the events. This procedure produced 2,055 topics.
4) Dataset for RQ5: After analyzing the research questions
using Wikipedia texts, we evaluated the proposed algorithm
for the actual conversational data. We conducted the ﬁnal
analysis with Japanese speakers. They discussed topics fromseveral disciplines, such as science, social studies, the Japanese
language, and information science, along with topics fromJapanese elementary, junior high, and high schools. Each
discussion was conducted for 10 minutes between two people. The conversational data were converted to text using
4 This extended W2E dataset is available from https://onl.tw/jQsLdtP
5 This extended W2E dataset is available from https://onl.tw/ppekiMM

Correct ratio
Transition analyzing time (sec.)

TDT
28.5%
5.52e-06

Queue
96.6%
0.08

Memory97.0%
1.59

Google Cloud Platform’s speech-to-text. Seven discussions
were conducted for RQ5. Table I lists the statistics of theactual conversational data.
5) Parameters: The value of N in the cue model is set to 4.
We set 0.2 and 0.8 as α and β, respectively, in the discussion
transition analysis.
6) Baselines: We compared our algorithm with the following methods:• TDT: This is a TDT method proposed by [7]. For texts
similar to each other collected using cosine similarity, thismethod selects texts that increase the entropy calculatedwith entities.
• LDA: We use LDA for only the main theme analysis.
Indeed, previous main theme analysis performs LDA [1];words with the highest weight from the topics of the LDA
were assigned as keywords representing the discussion
content. In this study, we trained LDA on the W2E dataset
for comparison.
7) Evaluation Criteria: We calculated the number of times
the proposed algorithm determined the appropriate transitionof the events of W2E topic to test RQ1. For RQ2 and RQ3,
we determined the number of times the algorithm identiﬁed themanually inserted noise. We manually checked whether the assigned Wikipedia categories were suitable as the main themesfor events in the evaluation of RQ4. We obtained the RQ5
results using the same procedure as for RQ1∼RQ4; however,
the authors and three volunteers performs the conﬁrmation
processes manually.
C. Results on the W2E dataset
RQ1. Can our algorithm detect properly transitioning discussions?
A1. Both queue and memory models could correctly parse approximately 97% of the text.
A2. The memory model correctly parsed more text
than the queue model.
Table III shows the results of the discussion transition
analysis of the baseline and proposed models. Results showthat the proposed models achieved a higher accuracy (approximately 97%) than the baseline. When we look at the numberof incorrect results, the queue model had 50, whereas thememory model had 44, indicating that the memory model
was more accurate. In contrast, the queue model took lessthan 0.1 second to analyze the discussion transition, and thememory model took approximately 1.6 seconds. The queue
model counts the number of occurrences by restricting theanalysis to the most recent instances. However, the memory

1.0
IR
PE

0.8

AA
LC

0.6

DA

not a “perfectly different discussion and analysis” but reported
them to have little correlation with the transition.
RQ4. Can our algorithm assign the Wikipedia category as the main theme for text?
A. The proposed algorithms obtained the appropriate
category with 70% accuracy.

0.4

BE
S

0.2

AC
HM

0.0
IR PE AA LC DA BE S ACHM
Fig. 3. Category pairs resulted as not perfectly different discussions

model calculates weights exponentially for occurrences in old
discussion texts; thus, the computational cost tends to be high.
However, this is not a major problem in practice because theanalysis was completed within 1 second.
RQ2. Can our algorithm detect stalling discussions?
A. Our algorithm could detect stalled discussions
adequately in 99.6% of the text. The detection failure
was due to the failure to retrieve valid entities andWikipedia articles.
In this evaluation, the queue and memory models accurately
detected stalled discussions for 268 of 269 (99.6%) intentionally inserted redundant events. We examined the one eventin which the proposed models failed. We examined the nouns,
Wikipedia articles, Wikipedia categories, and entities extracted
from the correctly analyzed 268 event texts and found theirmean values were 10.9, 24.9, 170.1, and 5.4, respectively. In
contrast, the mean values for failed detections were 6.0, 10.0,
74.0, and 0.0, respectively. Results show that all the meanvalues for detection failures were less than the events that weredetected correctly. Particularly, no entities could be extracted
from small discussions.
RQ3. Can our algorithm detect deviating discussions?
A. For all texts, we could detect unrelated discussion
transitions.
The analysis results for RQ3 were similar for the memoryand queue models. For all 2,055 analyzed events, we could
determine that the discussion transition was unrelated. A
detailed analysis of the results showed that 1,150 could beanalyzed as perfectly different discussions. The remaining 905
events had a little correlation with the previous transition.
Fig. 3 shows the topic categories comprising the 905 events.
The categories with particularly high proportions were S mixed
with HM and AC with DA or HM. Table II shows that S andAC had the lowest average number of events in a single topic(about 3∼4). If this value is low, less information is available
for analyzing the discussion transition, and the presence ofeven a few common entities or Wikipedia categories will havea greater impact. Thus, the proposed models predicted them as

For this evaluation, we randomly selected 100 events fromthe ﬁltered W2E dataset. Subsequently, we manually evaluatedthe suitability of the results. As this study was designed tosupport teachers during group learning, we set the criterion
for this evaluation as the commonalities between model results
and the events. In other words, if the same proper noun wasused in the event text, we consider that it is correct. In addition,
if the proper noun was the name of a place (e.g., Tokyo) but
the Wikipedia category was a country name (e.g., Japan), we
also consider that it is correct.
The algorithm accuracy for predicting the main themes fromthe topics using LDA as a baseline was 32%. The proposedqueue and memory models achieved accuracies of 69.3% and72.7%, respectively. Therefore, it was difﬁcult to apply LDA todetermine the main theme, whereas our models performed wellin many cases. Next, we examined the analysis time for thetwo proposed models: the queue and memory models took 4.82
and 1.44e-4 seconds, respectively. The queue model required
more computation time than the memory model because it
computed the center of gravity of the feature vectors of the
Wikipedia categories in the vector space, whereas the memory
model used the top of its analysis results. However, the queue
model produced results within 5 seconds, which is sufﬁciently
fast not to be a problem considering actual usage.
D. Results on actual conversational text
RQ5. How well does our algorithm work for actual
conversational texts?
A1. For 28 of the 33 conversational texts, Wikipedia
categories relevant to the main theme of each session
were appropriately obtained.
A2. There was only one time occurrence during theconversation where the topic was completely unrelated
to the discussion; however, the proposed models could
detect it correctly.
A3. The models could correctly analyze the discussion
transition for 20 of 25 conversational texts.
Finally, we analyzed the performance of the proposed algorithms on actual conversational data. Three Japanese who hadcompleted their graduate studies participated in this discussion.
This conversational data contains the actual 7 discussion
text data that were collected and separated into one-minutetexts. After excluding the conversations with failed speech
recognition, 33 actual conversational texts were recorded. A
text discussing the differences between birds and dinosaurs,
intended for a science unit, is given below. The text istranslated from Japanese into English.

I love birds so much. I’ll do some research on Triceratops.
Birds, dinosaurs, nobody eats anything or doesn’t eat anything,all sorts of things. Yes, there is a difference. It’s a difference,
isn’t it? Yes, yes, yes. Yes, yes, yes. We’re talking about adinosaur that’s close to a bird. Uh...
The Wikipedia article, translated to English from Japanese
for the paper presentation, obtained by applying ESA to
this text, is as follows: “Lists of extinct species,” “Translation of Dinosaurs! - Discover the giants of the prehistoricworld,” “Dinosaur King,” “Torii,” “List of birds of Korea,”“List of birds of Japan,” “Edo o Kiru,” “List of Prefectural road of Tottori,” “Torii Ryūzō,” “Mito Kōmon.” Fromthese articles, we extracted the following three categories.
“Category:Dinosaurs in video games,” “Category:Dinosaurs
in comics,” “Category:Torii.” The obtained categories include“Torii” (鳥居), meaning a gate of a shrine, because it has thesame characteristics as the Japanese word for birds (鳥), which
is a part of the discussion topic. However, it does not represent
a discussion. The other obtained categories were related todinosaurs, the actual discussion topic.
The following is a continuation of the above conversation:
I see. Okay, okay, okay, okay. What is it again? There’s a birdthat can’t ﬂy. But it seems to me that the story starts with theeating part. But they share the same characteristics as birds thatcan ﬂy. Yes, yes, yes, yes, yes... I see. That’s good. Can I startwith the characteristics? First of all, it has feathers on its body...
feathers... Feathers...
We then obtained Wikipedia articles for the Japanese text.
“Japan Association of City Mayors,” “Feathered dinosaur,”“List of birds of Japan,” “List of Prefectural road of Tottori,”“List of City planning area,” “Mito Kōmon,” “List of Elementary school in Osaka,” “Mito Kōmon.” From these articles, we
extracted the category “Category:Feathered dinosaurs.”
The above results conﬁrmed that Wikipedia categories could
be extracted correctly from actual conversational texts. Furthermore, the results for all conversational texts veriﬁed thatthe relevant Wikipedia categories were obtained for 28 of the
33 main themes from each session. In the actual discussion, thetexts included conversations that were completely unrelated tothe discussion. However, the proposed models could determinethat the text was unrelated to the discussion. Therefore, we
checked whether our models could detect discussion transition
for 25 conversational texts by removing 1 unrelated conversation and the ﬁrst conversational text for 7 conversations. Our
results revealed that the models could correctly analyze thediscussion transitions for 20 conversational texts. When weevaluated the discussion transition analysis on actual conversational texts, we obtained approximately 80% accuracy. During
the 5 analysis iterations, one wrong result was displayed tothe teacher who had to intervene in the discussion. However,this does not interfere with the learning activity, because theteacher could verify that the discussion is not facing any
challenges, by analyzing the actual conversation.

V. C ONCLUSIONS & F UTURE W ORK
We proposed algorithms to support teachers in monitoringgroup learning sessions easily. Based on the assumption that
discussion content changes over time, the proposed algorithms
compare a group’s discussion text with previous content. The
implemented algorithms assign a Wikipedia category as themain theme of each discussion. We conducted experimental
evaluations to conﬁrm the effectiveness of our algorithms andfound that the proposed algorithms performed well. Additionally, the proposed queue model provided faster results fordiscussion transition analysis, whereas the proposed memory
model was more accurate. Finally, during the evaluation of
allocating the main theme, we found that the memory model
provided immediate results, whereas the algorithm ﬁnding thecenter of gravity was more accurate.
Further research is necessary to evaluate the quality of the
discussions. The current algorithm assumes that discussions
are continuously changing. However, the actual discussion
may involve a quality debate that brings together multiple
topics that have already been discussed. It is important toevaluate the transition of the discussion by representing it asa tree structure to capture such discussions properly.
R EFERENCES[1] Thushari Atapattu and Katrina Falkner. 2016. A Framework for Topic Generation and Labeling from MOOC
Discussions. In L@S ’16. Association for Computing
Machinery, New York, NY, USA, 201–204.
[2] Ming-Wei Chang, Lev Ratinov, Dan Roth, and Vivek
Srikumar. 2008. Importance of Semantic Representation:
Dataless Classiﬁcation. In AAAI’08. 830–835.
[3] Hermann Ebbinghaus. 1987. Memory : a contribution toexperimental psychology. Dover Publications.
[4] Tuan-Anh Hoang, Khoi Duy Vo, and Wolfgang Nejdl.
2018. W2E: A Worldwide-Event Benchmark Dataset for
Topic Detection and Tracking. In CIKM ’18. Association
for Computing Machinery, 1847–1850.
[5] Quoc Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents. In ICML’14.
JMLR.org, II–1188–II–1196.
[6] Sports Science Ministry of Education, Culture andTechnology. 2018.
Koutou Gakkou Gakushuu
Sidou Youryou Kaisetsu Chiri Rekishi Hen.
http://www.mext.go.jp/component/a menu/education/micro
detail/ icsFiles/aﬁeldﬁle/2018/08/29/1407073 03 1.pdf[7] Kira Radinsky and Sagie Davidovich. 2012. Learning to
Predict from Textual Data. J. Artif. Int. Res. 45, 1 (2012),
641–684.
[8] Taouﬁq Zarra, Raddouane Chiheb, Rdouan Faizi, and Abdellatif El Aﬁa. 2018. Student Interactions in Online Discussion Forums: Visual Analysis with LDA Topic Models.
In LOPAL ’18. Association for Computing Machinery,
Article 30, 5 pages.