Artificial Intelligence Accelerates Human Learning: Discussion Data Analytics

158 Pages·2019·8.08 MB·English

by  Katashi Nagao

Katashi Nagao
Artificial
Intelligence
Accelerates
Human Learning
Discussion Data Analytics
fi
Arti cial Intelligence Accelerates Human Learning
Katashi Nagao
fi
Arti cial Intelligence
Accelerates Human Learning
Discussion Data Analytics
123
KatashiNagao
Nagoya University
Nagoya,Japan
ISBN978-981-13-6174-6 ISBN978-981-13-6175-3 (eBook)
https://doi.org/10.1007/978-981-13-6175-3
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Preface
Discussion is something that often occurs in everyday conversation, for example,
sharing your opinion with others and answering questions. However, it is slightly
more sophisticated than just chat. People who are good at communicating are
generally good at discussion.
Inthecaseofcommunicationabilityingeneral,youmightthinkofsomeonethat
considers the feeling of their conversation partner, or thinks about what is appro-
priate conversation for the current environment, or maybe even that they are more
skilled atmakingsmall talk.AlthoughIeven believe that these arevalid,themost
essential skill to acquire is discussion ability. In addition, I would like to explain
about three very important technical concepts in this book.
The first is data analytics, which is a scientific system for gathering objective
facts(data)inlargequantities,analyzingdataprobabilisticallyandstatistically,and
makingnewdiscoveries.Thesecondisnaturallanguageprocessing,atechniquefor
analyzingandgeneratinghumanlanguage.Thethirdtypeisgamification,whichis
a methodology that introduces game elements (such as scoring points and com-
petition, to gradually increase the skill level of the target by giving a sense of
accomplishment, etc.) for daily activities.
These three concepts play an important role in efficiently improving discussion
ability. Data analytics is used in the objective analysis of human behavior, and
natural language processing is used to extract features of human behavior, espe-
cially to extract and utilize features contained in words. One should think about
training for discussion ability in the same way as training for improvement in
sports. Inother words,we need self-analysis andpractice toovercome weaknesses
and improve strengths.
However, different from sports, discussion is an intellectual activity, and those
data are composed mainly of words. Although the technology concerning the
analysis of words is considerably advanced, it is still very difficult to analyze its
semanticcontentswithhigh accuracy.So,Iwill proposeacreative idea. Thatisto
attachattributestopeople’sremarks.Wethinkaboutwhatwewanttodiscussnext,
anddecideifitisrelatedtothepreviousspeaker’sspeechornot,thenwecategorize
the speech. Of course, if you follow conversation properly as a human, you can
v
vi Preface
understand without the need for categorization. However, it is quite difficult for a
machine to judge the relatedness of remarks in the same manner. Interestingly, if
you were to askmost peopleif they were awarein what way theirutterances were
related to the flow of the conversation, you would find that in most cases they are
not completely aware. To tell the truth, it is good for both machines and human
beings to listen to other people’s speech and think about the connections.
We now think about how to apply data analytics to a discussion, where the
simplest analysis is to examine the number of remarks produced by each partici-
pant. After collecting data for around a year, one of the first things we found was
thatpeoplewithahighernumberofutteranceswerefoundtohaveahigherlevelof
communication ability in comparison to those who produced less utterances. Of
course, there are some people that make many utterances with little content.
However, we have established practical policies for our meeting system in our lab
that encourage thoroughly listening to the speech of others before talking during
theirturnandtoproduceutterancesthatarerelatedtothepreviousspeechorifitis
not related to ask clear questions.
Next, we examine the relationship between a remark and the preceding utter-
ance. This makes it easier for analysis by having metadata on speech. What I
learned from the analysis is that people who are good at discussions tend to make
related remarks when someone speaks related to their remarks. In other words,
people who are capable of making related statements consecutively will have high
communication skills.
By using data analytics, you can explain such things with a clear basis.
On the other hand, what is gamification? Applying game-like elements to
everyday activities is not an entirely new concept. We all are familiar with the
teacher praising the first student to get a question right in front of the class.
It is an important requirement for games to reward achievement of tasks, allow
competition with multiple people, and show results in an easy-to-understand
mannerbeforetheplayergrowsboredofthegame,butthisalsocanapplytodaily
activities where it isn’t rare to gauge motivation.
Nevertheless,whytrytosystemizegameswithanamelikegamification?Tothat
end, information technology (IT) is also heavily involved. Data are important here
as well. In short, IT is a mechanism that processes digitized data to suit the cir-
cumstancesofhumanbeings,sogamingcanalsoactivelyutilizethedatatoexpand
its effect. For example,a small survey can now be easily filled out via smartphone
with the results being quickly summarized for everyone present to view. If you
happen to have past data from a similar survey, you can quickly compare it and
carry out statistical processing. In this way, IT is providing humans with new
opportunities to think. The fact that we are able to collect and quickly process
informationforhumanunderstandingwillchangeourdefinitionofwhatispossible.
So, I would like to try to make discussion a game. Some might think that there
already exists debate which is like discussion made game-like. Debates have clear
rulesandframeworkstodecidetheoutcome.Givenaproblem,suchas“whetherthe
Japanese constitution should be revised”, arguing and arguing against and against,
wewillcompetetodeterminewhichclaimismoreconvincing.Wecanimaginethat
Preface vii
debate will be good training for discussion. However, it is far from the commu-
nication which we usually do. We will express our opinion based on our thinking
andexperience.Itisconstructivetotrainpeoplethinkingabouthowtoexpresssuch
opinions effectively to the opponent. Debate is not appropriate for it.
Then, what should we do?
Ruleslikedebatearenotnecessaryfortrainingtoimprovediscussion.However,
there are some restrictions. In order to introduce gamification, at least you need to
do the following. First, you have the speaker record their name when they speak
and whether their remarks are related to previous statements. This also makes it
easy to use IT, so that you can enter it automatically for the former speaker. Next,
whensomeone’sremarksareover,wewillevaluatethatstatement.Thisevaluation
isalittledifficult,buthereitiseasytothinkthatitiswhetheryouwereconvinced
bytheremarkornotbyenteringyesorno.Infact,youmayhavetoentersomewhat
more complicated things, but that is when the stage in the game has advanced.
The way to train the discussion ability using IT like this is very effective. We
havebeendoingresearchonthisforover10years.Thisbookiswrittentosharethe
research results with readers. In addition, I will explain data analytics and gami-
fication in order to understand the research contents. Of course, I would like to
touch on artificial intelligence which is the cutting edge of IT, especially machine
learning and data mining.
It would be an unexpected pleasure if readers would someday become good at
discussions and communication by utilizing our research.
I greatly appreciate the assistance provided by people who contributed to this
book. The staff and students of Nagao laboratory of Nagoya University, Shigeki
Ohira,ShigekiMatsubara,KatsuhikoKaji,DaisukeYamamoto,TakahiroTsuchida,
Kentaro Ishitoya, Hironori Tomobe, Kei Inoue, Kosuke Kawanishi, Naoya
Kobayashi, NaoyaMorita,SayaSugiura, Kosuke Okamoto,Ryo Takeshima, Yuki
Umezawa, Shimeng Peng, Ryoma Senda, and Yusuke Asai, whom I have worked
with and have kindly supported me during the development of the prototype sys-
tems introduced in this book. Kazutaka Kurihara, Professor of Tsuda University,
providedmewithmuchhelpfuladvicetodeveloptheideasdescribedinthisbook.
Also, I thank Miku Suganuma, Miyuki Saito, and William Samuel Anderson
who gave me helpful advice on the wording used in the book.
The work described in Chap. 5 was supported by the Real-World Data
Circulation Leaders’ Graduate Program of Nagoya University. I thank Kazuya
Takeda, program coordinator, and Mehrdad Panahpour Tehrani and Jovilyn
ThereseB.Fajardo,designatedprofessorsoftheprogram,whoareco-authorsofthe
journal paper based on Chap. 5.
Furthermore, I am indebted to my editor, Mio Sugino, who hasbeen a constant
mentor during the writing process.
Finally, I am particularly grateful to my wife, Kazue Nagao, and my daughter,
Saki Nagao, without whom I could not have undertaken this effort.
Nagoya, Japan Katashi Nagao
Contents
1 Artificial Intelligence in Education . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 AI and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 e-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Intelligent Tutoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Learning Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5 Machine Learning Accelerates Human Learning. . . . . . . . . . . . . 11
1.6 Deep Learning Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Discussion Data Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1 Structure of Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2 Discussion Mining System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Structuring Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4 Summarization of Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5 Task Discovery from Discussion . . . . . . . . . . . . . . . . . . . . . . . . 27
2.6 Active Learning for Improving Supervised Learning. . . . . . . . . . 33
2.7 Natural Language Processing for Deep Understanding
of Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.8 Natural Language Processing for Discussion Structure
Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.9 Correctness of Discussion Structure. . . . . . . . . . . . . . . . . . . . . . 46
2.10 Structuring Discussion with Pointing Information . . . . . . . . . . . . 51
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3 Creative Meeting Support. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.1 Meeting Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2 Machine Learning for Structured Meeting Content . . . . . . . . . . . 63
3.3 Post-meeting Assistance to Support Creative Activities. . . . . . . . 64
ix
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3.4 Evaluation of Creativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.5 Future of Creative Meeting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4 Discussion Skills Evaluation and Training . . . . . . . . . . . . . . . . . . . . 77
4.1 Evaluation of Speaking Ability . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.2 Feedback of Evaluation Results. . . . . . . . . . . . . . . . . . . . . . . . . 83
4.3 Evaluation of Listening Ability . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.4 Discussion Skills Training. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.5 Gamification for Maintaining Motivation to Raise Discussion
Abilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5 Smart Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.1 Environments for Evidence-Based Education . . . . . . . . . . . . . . . 106
5.2 Related Work and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.2.1 Discussion Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.2.2 Presentation Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.2.3 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.3 Leaders’ Saloon: A New Physical–Digital Learning
Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.3.1 Discussion Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.3.2 Digital Poster Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.3.3 Interactive Wall-Sized Whiteboard . . . . . . . . . . . . . . . . . 111
5.4 Importing Discussion Mining System into Leaders Saloon . . . . . 112
5.4.1 Discussion Visualizer. . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.4.2 Discussion Reminder . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.4.3 Employing Machine Learning Techniques. . . . . . . . . . . . 116
5.5 Digital Poster Presentation System. . . . . . . . . . . . . . . . . . . . . . . 116
5.5.1 Digital Posters Versus Regular Posters . . . . . . . . . . . . . . 116
5.5.2 Authoring Digital Posters. . . . . . . . . . . . . . . . . . . . . . . . 117
5.5.3 Data Acquisition from Interactions with Digital
Posters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.6 Skill Evaluation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.6.1 Discussion Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.6.2 Presentation Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.7 Future Features of Smart Learning Environments . . . . . . . . . . . . 125
5.7.1 Psychophysiology-Based Activity Evaluation. . . . . . . . . . 127
5.7.2 Virtual Reality Presentation Training System. . . . . . . . . . 130
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
6 Symbiosis between Humans and Artificial Intelligence . . . . . . . . . . . 135
6.1 Augmentation of Human Ability by AI . . . . . . . . . . . . . . . . . . . 136
6.2 Intelligent Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Contents xi
6.3 Singularity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.4 Human–AI Symbiosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
6.5 Agents Embedded in Ordinary Things . . . . . . . . . . . . . . . . . . . . 145
6.6 Artificial Intelligence Accelerates Human Learning. . . . . . . . . . . 147
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151