research on effective practices of mobile learning

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Learning Analytics Center, Kyushu University, Japan

Research on effective practices of mobile learning

Hiroaki Ogata

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Self‐introduction

Hiroaki Ogata (緒⽅ 広明) Distinguished Professor, Kyushu UniversityMobile and Ubiquitous Learning, CSCL,Learning Analytics

Over 300 Journal (including SSCI) and internationalconference papers.

Several paper awards and keynote speeches Associate editor of IEEE Trans. on LearningTechnologies (SSCI), IJCSCL (SSCI), etc.

Executive member of APSCE, IamLearn and SOLAR.

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九州⼤学(Kyushu University)

19,000 students 8,000 faculty staff BYOD(Bring Your Own Devices)

Wireless Internet access (300 Mbps) in all campus 

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Part 1: Mobile Learning in the context of Language Learning

Part 2: Mobile Learning Analytics in University Education

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How many of you have Smartphone?

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How many of you are using Smartphonefor learning or education?

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7Learning Analytics Center, Kyushu University, JapanBack to learning in the real world !

not only enables learning at anytime and any place but also provides the right information at the right timeat the right place in the right way.

Mobile Learning

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Examples

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Our research （１） Japanese language learning Provide a quiz and task in the certain place usingGPS.

Ogata, et al : LOCH: Supporting Mobile Language Learning Outside Classrooms, International Journal of Mobile Learning and Organisation, Vol. 2, No.3, pp.271-282 (2008)

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Ubiquitous Language Learning room(2)

RFID tag

CD boomboxCDラジカセ

Where is the microwave?

Microwave電子レンジ

Learner

ＰＤＡ

Ogata, et al, Computer Supported Ubiquitous Learning Environment for Vocabulary Learning, International Journal of Learning Technology, Vol.5, No.1, pp.5-24, 2010.

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Input:食べる

Name: YGrade: M2Age: 24

Name: ZGrade: UGAge: 22

Name: XGrade:M1Age: 25

Formal

Overview of Japanese Polite Exp. Learning

召し上がる．

くう．

RFID tag

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JAMIOLAS with sensors (3)屋内 屋外

ぬくぬく さむざむ

センサSensors

Cold outdoorWarm room

NukuNuku SamuZamu

Ogata, et al, JAMIOLAS2: Supporting Japanese Mimetic Words and Onomatopoeia Learning with Wireless Sensor Networks for Overseas Students, International Journal of Mobile Learning and Organisation, (in press)

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Our research project（4） Linking video and physical objects Ex, PC assembling, cooking

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SCROLL

System for Capturing and Reusing Learning Logs Scroll is used for writing a history.

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Example of learning log

Originally, learning log is definedas a record of Children’s learning process (1990s).

Overseas students has a memo for learning Japanese language to write what they learned in the daily life.

It is difficult to find some appropriate notes immediately when needed.

It is also hard to share the notes with others,because they bring it back to their home country. 

Lifelog technology to capture learning experiences

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What is lifelog?

Lifelog is a record ofa whole life.

Idea was in 65 years ago.Vannevar Bush.As we may think.The Atlantic Monthly,Vol.176, No.1,pp.101–108,1945.

Now, web log, moblog, twitter

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Current research

MyLifeBitsMicrosoft research in UK since 2001 SenseCam, fish‐eye camera is built in. internal sensor is triggered by a change intemperature, movement, or lighting.

Aiding serious cognitive memory loss http://www.viconrevue.com/

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Life Log using video

Record a video for 16 hours a day and 70 years

1TB (1000 GB) HDD= ~50 USD => 550USD (11TB)But a problem is … We need another 70 years to watch a life log. We have to develop effective indexing and retrieval methods.

Video quality Bit rate Size for 70 yearsMobile TV phone  64 Kbps 11 T bytesVideo CD 1 Mbps 183 T bytesBroadcasting 4 Mbps 736 T bytes

Hori and Aizawa, Context‐based video retrieval system for the life‐log applications,Proc. of ACM SIGMM, int’nl WS on Multimedia information retrieval, pp.31‐38, 2003.

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Lifelogging apps for Android, iPhone  Evernote 3banana

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http://ll.artsci.kyushu‐u.ac.jp

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Features of SCROLL

– Record a LL by photo, video and text with thelocation and time.Those information is a clue to recall thelearning experience and the context.

– Share your LL with other users.– Generate personalized quizzes from thestored LLs according to the learner’s locationand time.

– Analyze the past ULL using the time map.

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LORE model for “learning by logging”

Log

Organize

Recall

Evaluate SCROLL

“Lore” means knowledge that is not written down but is passed from person to person.

Simple method for data capture

Knowledge organization

Context-aware, personalization

Learning analytics

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Log

Organize

Reuse

Evaluate SCROLL

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Log

Active logging

Capturing when the user learns a new word. Capturing when the user has been taught.Example: what is the name of this flower in Japanese?

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Add new learning log

PhotoVideoLocationTime

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Log

Passive logging

Capturing everything by using a lifelog camera

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Filtering

Recommend Letters Face

Unrecommend Unfocused Dark Similar

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Similar photos

Similar learning logs

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Single user’s learning logs

Now1 month ago

1 year ago

3 years ago

Show the user’s past learning logs

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Multiple users’ learning logs

Now

1 month ago

1 year ago

3 years ago

User A

User B

User C

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Log

Organize

Reuse

Evaluate SCROLL

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Organized by tag, time, location, etc.

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Log

Organize

Reuse

Evaluate SCROLL

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How to reuse learning logs

Provide personalized quizzes that are automaticallygenerated by the stored learning logs.– （１）Context‐dependent quiz– （２）Context‐independent quiz

Recommend the past learning log that can beexperienced by the user

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Example of quiz

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Cognitive and brain sciences

Encoding specificity, Tulving, E. (1983). Picture superiority, Paivio, A. and Kalman, C. (1972). Spaced repetition, Pimsleur, P. (1967). Active recall

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Recommend learning logs

Make the user be aware of past ULL around the user.

N

S

WE

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Log

Organize

Reuse

Evaluate SCROLL

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Three-layered graph in SCROLL

user

knowledge

Time‐place

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Analysis in the same layer

1. User layer:The users who have many links are very active in the certain area, if not, they are not active in there.

2. Knowledge layer:The knowledge that has many links is important because it can be fundamental in the certain area.

3. Time‐and location layer:The location that that has many links is important because users can learn much knowledge there. 

More things can be revealed by analyzing the links between two layers.

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From the links between users and knowledge,the users who have many links may have many knowledge and the knowledge that have many links is important and essentialto for the learners.

user

knowledge

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knowledge

From the links between knowledge and time‐location,the knowledge that have many links is very important because the knowledge can be applied in many locations; and the location that have many links is very important because the users can learn much knowledge at the location.

Time and location

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From the links between users and time‐location,the users who have many links may have learned many knowledge in certain areas, and the location that have many links is important and essentialto for the learners in the certain location.

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Layout Visualizing ubiquitous learning logs

Where

WhenWhat

Who

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Theories that support SCROLL

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Forgetting speed Forget 56% after one hour Forget 73% after one day Forget 80% after one month

http://www.ultimatelanguagesecrets.com/

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Spaced repetition 

We are forgetting quickly, but recalling past ULLOseveryday is not a good idea.

The efficient repetition intervals were proposed: 5 s, 25 s, 2 m, 10 m, 1 h, 5 h, 1 day, 5 days, 25 days,4 months, 2 years.

Software: SuperMemo, Mnemosyne, Anki, Smart‐fm,Skiritter, Winflash

Pimsleur, Paul (1967). A memory schedule, The Modern Language Journal, 51 (2): pp. 73–75.

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Encoding specificity

When you store something in memory, the memory isnot just of the item being stored but also of thecontext in which the memory occurred. Recall thusmay be triggered by elements of the context beingpresent.

Thus, SCROLL recommends ULL when you are in thesame context and the location.

Tulving, E. (1983). Elements of episodic memory. Oxford: Oxford University Press

If you want something in downstairs…

If you go back to upstairs again,you remember…

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Types of long‐term memory

Procedural (implicit) memory like bicycle‐riding skill. Declarative (explicit) memoryepisodic memory and semantic memory.

Episodic memory is retrieved by time, location,contextual knowledge, and associated emotions.

Thus, SCROLL stores ULLOsassociate with the contextual info,and it enables to retrieve themby the contextual info.

Carolyn K. Rovee-Collier, Harlene Hayne, Michael Colombo, The development of implicit and explicit memory. 2001.

Episode

Semantic

Procedural

Easy to recall

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Picture superiority effect

Concepts are much more likely to be rememberedexperientially if they are presented as picturesrather than as words.

Thus, SCROLL shows pictures to recall ULLO. Nelson, D.L., Reed, U.S., & Walling, J.R. (1976).Pictorial superiority effect. Journal of ExperimentalPsychology: Human Learning & Memory, 2, 523‐528.

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Recall method

Passive recallReview learning materials passively like reading andwatching.

Active recallActively recall a memory by answer a question.More efficient for stimulate long‐term memory.

Thus, SCROLL provides quizzes which are generatedby past ULLOs.

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An application of SCROLL

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Seamless Learning

Learning with entwinement between in-class andoutside-class learned knowledge

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Seamless Learning

In-class

CALL

handhelds

Watching DVD at home Reading in

the trainReading at cafe

SCROLL

Link Outside-class

Learning with entwinement between in-class andoutside-class learned knowledge

Sharing contexts is important!

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‘subject to …’ ～に制約される、～を免れない 使服从, 使遭受

– All visitors and packages are subject toelectronic scan.滞在者と荷物全部にスキャンをかけることに

なっています。

– This agreement shall be subject to the laws of Japan.本契約は日本国の法律に従うものとする。

– The terms of your account are subject to change.口座の条件は変わることがあります。

Frequency of occurrence encourages incidental vocabulary learning and reappearance of a word reinforces the form‐meaning connection in the learner’s mental lexicon (Hulstijn, 1996).

Learn from contexts

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Link student’s learning with others

Same or related vocabulary and idioms are linked!

SMALL System

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Linking in‐class words and out‐class ones

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Part 2: Mobile Learning Analytics

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S

School

Society

Systems

Contents

LearnerTeacherLearning &teachingprocesses

Conceptual framework for educational systems 

To understand and enhance teaching and learning process is the core of educational researches.

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What is Learning Analytics?

the measurement, collection, analysis andreporting of data about learners and theircontexts, for purposes of understanding and optimizinglearning and the environments in which itoccurs.(SOLAR web page)

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e‐Book project in Kyushu Univ.

Since April 2014, Kyushu University has started a new curriculum to educate “active learner”

for the first‐year students (2700+ students) and for 200+ subjects.

All the teachers materials (ppt/pdf) are provided by e‐books system, called BookLooper.

The system records the user’s activities such as“next page”, “previous page”, “underline”,“comment”.

The logs are integrated with other systems’ logs such as Moodle and Mahara, and analyzed in order to improve learning and teaching. 

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e-learning/e-portfolio

Educational Big Data project

Digital text

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BYOD in KU (2013〜)M2B system for 19,000 (2015〜)180,000 log data / day8,000,000 log data in total

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Video

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Characteristics of e‐book1. Paper‐less and light weight:

No need to bring several heavy books.2. Searchable:

Find keywords across e‐books.3. Interactive:

Quiz, Simulation, Link to web and videoAugmented reality

4. Personalized︓Contents can be adaptable and adaptive to the user.

5. Traceable:All the learning process is recorded and replayed.

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Educational Big Data Project in Kyushu Univ. 

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M2B(Mitsuba) Learning Support System

① Moodle: e-Learning system (E/J)Attendance, assignment management, BBS, survey, etc.

② Mahara: e-portfolio system (E/J)Diaries, reflection, information sharing

③ BookLooper: e-textbook management system(J)Sharing and distributing learning materials.

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Fact data

Semester Students Staff Moodle Mahara BookLooper

2015 1st 2,687 10,490 206 courses 866 diaries 132 files

2015 2nd 19,293 10,490 112 courses 302 diaries 95 files

2016 1st 19,293 10,490 712 courses 86 courses 105 files

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http://moodle.kyushu-u.ac.jp

e-Learning system

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Teaching portfolio (TP) for teachers Learning portfolio (LP) for students

e-Portfolio system

You can combine LP and TP for improving your course.

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e-Textbook management system 5

• Professors provide their slides (ppt/pdf) to BookLooper.• Students read them by using BookLooper in their PC,

smartphones. • All the action logs are stored.

download

Book shelf

Book store

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53.93%

10.49%0.02%

0.03%0.22%0.19%

13.71%

1.02%20.39%

April 1st, 2015 – Dec 24, 2015

ページめくりコンテンツOPEN

キーワードブックマークマーカーメモ拡⼤・縮⼩ジャンプ機能その他

Distribution of BookLooper activity logs

(54%)(10%)

(14%)

0.46%

(1%)(20%)

Flip a pageOpen a contentKeyword searchBookmarkUnderlineMemoZoom in/outJumpOthers

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Preparation

Reflection

Overview

Course design

CourseImprovement

Syllabus, learning materials, exercises,assignments, examinations

workshop

M2B system・e-portfolio logs・e-learning logs・e-Book logs Learning advisorLA

Professors Students

Storing logs

Teaching advisor

Consulting

LA

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成果５

成果６Discover rules to predict final

score

⇒Predict the score from the first four lectures

If students make preview more than 5 minutes in the first four lectures ⇒ 100% of them will get grade A

If students make preview less than 5 minutes in the first four lectures ⇒ 93.8% of them cannot get grade A.

Integrate with Moodle

⇒Link BookLooper from Moodle and show data using Moodle

plugin

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1244

5.20

8132

.73 

7340

.80 

1028

7.17

5541

.17 

7842

.06 

7909

.83 

B L予習・復習

中間・期末も成績がA判定だった中間・期末も成績がB判定だった中間・期末も成績がC、D判定だった中間→期末にA判定に成績が上がった （例︓D→A）中間→期末にB、C判定に成績が上がった （例︓D→B）中間→期末にB判定以下に成績が下がった （例︓A→D）全体の平均

閲覧

時間

Relation between the preview group and score

⇒the preview group got betterscore than the average.

Relation between the preview time and score up/down

⇒Up-score group made a longerpreview than the others.

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Visualization of e-book reading

⇒3D Cubic Gantt Chart

Visualization of e-book reading

⇒page transition graph

廣川

中村・岡⽥

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Real time data analysis

Student

Teacher

Time

Page

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Ontological support for e-book reading

⇒Support teacher to knowstudents knowledge and

support studentsʼ knowledge construction

Slide summarization

⇒Automatically generate 3or 5 minute slide from full

slides for 90 minutes

王

島⽥

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Visualization of e-book reading patterns

⇒start to read one weekbefore the lecture and read it

every one week

Score prediction by e-book and Moodle activities

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⼤久保

⼤井

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Slide summarization

⇒ for preview and review

Real-time grouping

Based on BoolLooper logs

Kojima

Shimada

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teacher

students

Active learner dashboard

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BookLooper report

Learning Analytics Center, Kyushu U i it J

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Mahara report

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Learning Analytics Center

http://lac.kyushu‐u.ac.jp

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Members 21 Faculty staff and 5 technical staff 

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Research grantsGrant name 2015 2016

Grant‐in‐Aid for Scientific Research (S)（2016〜2020: 140,900,000 JPY） ー 38,800,000 JPY

NICT Research grant（2014〜2017: 140,000,000 JPY） 50,000,000JPY 30,000,000 JPY

Grant‐in‐Aid for Scientific Research (B)（2013〜2016: 13,200,000 JPY） 4,300,000JPY （Switch to (S)）

Grant‐in‐Aid for Challenging Exploratory Research（2015〜2017: 2,800,000 JPY）

900,000JPY 1,000,000 JPY

JST PRESTO（2015〜2019: 39,500,000 JPY） 6,100,000 JPY 12,000,000 JPY

Grant‐in‐Aid for Scientific Research on Innovative Areas（2016〜2017: 8,400,000 JPY）

4,100,000 JPY 4,300,000 JPY

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Seamless support

K12

Univ.

OJT

Formal

Cram schoolsLibraryHomework

MuseumsAbroad

MOOCs

Self-learning

M2B System (NICT)EDB analytics

SCROLL (JST PRESTO)Context-aware learningPrevious research

Educational Data Science

Informal

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Research areas

Computer Science Data engineering

Cognitive science

Neuro‐Science

EducationPsychology

Management and analysis for Educational big data

Pedagogy for digital learning / teaching New theory by educational big data

Learning styles Learner models

Educational technology Ergonomics・AI

Learning sciences

Educational data science

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Item Previous research This researchImprovement of education and educational materials

Based on teacherʼs experiences and subjectivity

Based on analysis of EBD (educational big data)-> objective

Evaluation of students and teachers

Based on Examination and questionnaire

Based on analysis of learning and teaching process

Lecture style Based on the curriculum(pre-planned) Based on EBD (adaptive)

Researchmethod

Based on questionnaire, observation, and examination

Based on EBD

Originality of this research

→Toward e‐Science and Open‐Science based on EDB

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Connecting DOTs

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Speech to the graduates of Stanford University in 2005

https://www.youtube.com/watch?v=D1R-jKKp3NA

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My Message

A dot means your learning logs (learning experiences), what you learned before.

It is very important to record, connect and analyze your dots.

Connecting dots is also very important to re‐think your past learning, to improve your learning, and to create a new idea for the future.

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Conclusions

Educational data science based on learning analytics of educational big data

Connecting dots

Improve teaching and learning

Learning Analytics Center, Kyushu University, Japan 20

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Thank you!

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謝謝!

hiroaki.ogata@gmail.com