Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
英語教育研究の始め方・進め方:目的に合致した手法選択の重要性
Search
Ken Urano
December 21, 2019
Education
1
880
英語教育研究の始め方・進め方:目的に合致した手法選択の重要性
名古屋学院大学大学院2019年度英語セミナー
名古屋学院大学丸の内サテライト
2019.12.21.
Ken Urano
December 21, 2019
Tweet
Share
More Decks by Ken Urano
See All by Ken Urano
The Task is not the End: The Role of Task Repetition and Sequencing In Language Teaching
uranoken
0
200
学習者を対象にした英語教育研究における倫理的配慮
uranoken
0
710
学習者データを「見る」:外国語教師のためのデータの入力、分析、解釈方法
uranoken
0
870
英語教育研究でエビデンスを「つくる」:メタ分析、再現性、追試
uranoken
0
1.1k
タスク·ベースの英語授業:基本的な考え方とデザイン方法
uranoken
0
1.1k
英語の授業をタスクで組み立てる
uranoken
0
1.2k
Designing Task-based ESP Syllabi: Two Cases from an English for Business Purposes Program
uranoken
0
1.2k
第二言語習得と外国語教育における 「文法知識」の位置づけ
uranoken
0
1.2k
Japanese learners’ reliance on specificity when using the English articles: A forced-choice gap-filling study
uranoken
0
830
Other Decks in Education
See All in Education
BrightonSEO, San Diego, CA 2024
mchowning
0
100
Chapitre_1_-__L_atmosphère_et_la_vie_-_Partie_1.pdf
bernhardsvt
0
230
The Gender Gap in the Technology Field and Efforts to Address It
codeforeveryone
0
270
Tableau トレーニング【株式会社ニジボックス】
nbkouhou
0
22k
Image compression
hachama
0
180
Algo de fontes de alimentación
irocho
1
440
Medicare 101 for 2025
robinlee
PRO
0
300
オープンソース防災教育ARアプリの開発と地域防災での活用
nro2daisuke
0
200
20241002_Copilotって何?+Power_AutomateのCopilot
ponponmikankan
1
190
Web Architectures - Lecture 2 - Web Technologies (1019888BNR)
signer
PRO
0
2.7k
Master of Applied Science & Engineering: Computer Science & Master of Science in Applied Informatics
signer
PRO
0
630
Web 2.0 Patterns and Technologies - Lecture 8 - Web Technologies (1019888BNR)
signer
PRO
0
2.4k
Featured
See All Featured
Large-scale JavaScript Application Architecture
addyosmani
510
110k
Building Better People: How to give real-time feedback that sticks.
wjessup
365
19k
Building a Scalable Design System with Sketch
lauravandoore
460
33k
Mobile First: as difficult as doing things right
swwweet
222
9k
A Philosophy of Restraint
colly
203
16k
Speed Design
sergeychernyshev
25
660
Done Done
chrislema
181
16k
We Have a Design System, Now What?
morganepeng
51
7.3k
Raft: Consensus for Rubyists
vanstee
136
6.7k
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
232
17k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
28
9.1k
Building Flexible Design Systems
yeseniaperezcruz
327
38k
Transcript
ӳޠڭҭݚڀͷ࢝ΊํɾਐΊํ తʹ߹கͨ͠ख๏બͷॏཁੑ Ӝ ݚʢେֶӃӳޠֶઐ߈٬һڭतʗւֶԂେֶʣ email:
[email protected]
໊ݹֶӃେֶେֶӃ2019ӳޠηϛφʔ ໊ݹֶӃେֶؙͷαςϥΠτ ɹɹ2019.12.21. https://www.urano-ken.com/research/NGUSeminar
͡Ίʹ • ͡Ίͯͷӳޠڭҭݚڀɿ ԡ͓͖͍͑ͯͨ͞ίπͱ ϙΠϯτʢݚڀࣾʣ • ຊॻͷ༰ʹ৮Εͭͭɺຊॻ ʹؚΊΔ͜ͱͷͰ͖ͳ͔ͬͨ ͜ͱ͓͠͠·͢ ݚڀ
͡Ίʹ ʮݚڀʯͱԿ͔ ݚڀ
ݚڀʢresearchʣͱ Research is a systematic process of inquiry consisting of
three elements or components: a. a question, problem, or hypothesis b. data, and c. analysis and interpretation of data (Nunan, 1992, p. 3) question data interpretation
question data interpretation → → ݚڀʢresearchʣͱ systematic
ݚڀ՝ σʔλ ղऍ → → ݚڀʢresearchʣͱ ϧʔϧʢํ๏ʣ
ݚڀʢresearchʣͱ • ݚڀͱɺݚڀ՝ʢ͍ʣΛઃఆ ͠ɺࠜڌͱͳΔσʔλΛूΊɺͦͷ ͑Λಋ͖ग़͢ӦΈ • ্هͷ̏ཁૉͦΕͧΕʹ͍ͭͯҰఆͷ ଋ͝ͱ͕͋Δ
ݚڀͷతʢgoalʣ • ݚڀՌΛͲ͜ʹؐݩ͢Δ͔ • ڭࢣʢݚڀऀʣݸਓ • ӳޠڭҭʢݚڀʣશମ
ݚڀͷతʢgoalʣ • ڭࢣݸਓͷؐݩΛࢦ͢ • ࣮ફͱͯ͠ͷݚڀ ʢpractitioner researchʣ • શମͷؐݩΛࢦ͢ •
ֶज़తͳݚڀ ʢacademic researchʣ
ݚڀͷతʢgoalʣ • ࣮ફͱͯ͠ͷݚڀͷత • ࣗͷੜెͨͪʹ͍ͭͯͷཧղΛ ਂΊɺ࣮ફ্ͷΛղܾ͢Δ • ڭࢣͱͯ͠ͷ
ݚڀͷతʢgoalʣ • ྫɿ • ࢼߦࡨޡͷ݁Ռɺࣗͷतۀ͕ ͏·͍͘͘Α͏ʹͳͬͨ • ੜె͕ΔؾΛݟͤΔΑ͏ʹͳͬͨ • ࣗͷ୲Ϋϥεͷ্͕͕ͬͨ
ݚڀͷతʢgoalʣ • ֶज़తͳݚڀͷత • ӳޠڭҭʢݚڀʣશମΛҰา લʹਐΊΔͨΊͷߩݙ • ࣗͷΫϥεҎ֎Ͱཱͭݟ • ଞͷจ຺Ͱͯ·Δݟ
ݚڀͷతʢgoalʣ • ࣮ફͱͯ͠ͷݚڀͱֶज़తͳݚڀ • ༏ྼͳ͍ • త͕ҟͳΓɺํ๏͕ҟͳΔ
࣮ફͱͯ͠ͷݚڀ • ϦϑϨΫςΟϒͳӳޠڭҭΛ ࢦͯ͠ɿڭࢣͷޠΓ͕͘ तۀݚڀʢͻͭ͡ॻʣ • ࣮ફͱͯ͠ͷݚڀͷํ๏ ࣄྫΛऩ ݚڀ
࣮ફͱͯ͠ͷݚڀ • ӳޠڭࢣͷͨΊͷ࣮ફݚڀ ΨΠυϒοΫʢେमؗॻళʣ • ڭҭ࣮ફΛݚڀͷܗʹ ·ͱΊΔͨΊͷࢦೆॻ ݚڀ
ֶज़తͳݚڀ ݚڀ • ݚڀՌΛશମʹؐݩ͢ΔͨΊʹ • ࣗͷݚڀͱଞͷݚڀʢઌߦݚڀʣ ͱͷؔΛ໌֬ʹࣔ͢ • ݚڀ݁Ռ͕ଞͷจ຺ʹస༻ʢԠ༻ʣ ՄೳʹͳΔͨΊͷखଓ͖Λ౿Ή
ݚڀͷ̏ཁૉ ݚڀ՝ σʔλ ղऍʢ͑ʣ
ݚڀ՝ͷઃఆํ๏ 1. ͓ΑͦͷݚڀςʔϚΛܾΊΔ 2. ڵຯɾؔ৺ɺݚڀՁɺ࣮ߦՄೳੑΛ ݕ౼͢Δ 3. ؔ࿈͢ΔݚڀʢઌߦݚڀʣΛूΊɺ ಡΈɺ·ͱΊΔ ݚڀ՝
ݚڀ՝ͷઃఆํ๏ 1. ͓ΑͦͷݚڀςʔϚΛܾΊΔ 2. ڵຯɾؔ৺ɺݚڀՁɺ࣮ߦՄೳੑΛ ݕ౼͢Δ 3. ؔ࿈͢ΔݚڀʢઌߦݚڀʣΛूΊɺ ಡΈɺ·ͱΊΔ ݚڀ՝
ݚڀ՝ͷઃఆํ๏ • ڵຯɾؔ৺ • Γ͍ͨ͜ͱ • ݚڀՁ • Δ͖͜ͱɺٻΊΒΕ͍ͯΔ͜ͱ •
࣮ߦՄೳੑ • ΕΔ͜ͱ
ݚڀ՝ͷઃఆํ๏ • ઌߦݚڀͷ·ͱΊ 1. ݚڀςʔϚ͕ͲͷΑ͏ͳΓޱͰ ѻΘΕ͖͔ͯͨ 2. Կ͕ௐࠪ͞ΕɺԿ͕໌͠ɺ Կ͕Θ͔͍ͬͯͳ͍͔ 3.
ཧతܽؕɺํ๏తͳ͍͔
ݚڀ՝ͷઃఆํ๏ • Α͍ݚڀ՝ • ઌߦݚڀͰॏཁͩͱࢦఠ͞Ε͍ͯΔ ͷʹेௐࠪ͞Ε͍ͯͳ͍ͷ • ઌߦݚڀʹ͕͋ΓɺͦΕΛ ղܾ͢Δͷ
ݚڀͷछྨ छྨ Ξϓϩʔν త ୳ࡧʗݕূ จݙݚڀ 1. ઌߦݚڀΛཧ͠༰Λݕ౼͢Δ ࣮ূݚڀ ࣭తݚڀ
1. จ຺Λߟྀͯ͠ࣄΛଊ͑Δ ୳ࡧܕ ݕূܕ 2. จ຺Λߟྀͯ͠ࢀՃऀͷม༰Λଊ͑Δ 3. ࢀՃऀͷܦݧೝࣝΛଊ͑Δ 4. ઌߦݚڀͷର֎ͷࣄΛ໌Β͔ʹ͢Δ 5. ݚڀͷ৴ጪੑΛߴΊΔ ྔతݚڀ 1. ࣄͷಛΛྔతʹهड़͢Δ 2. ࣄͷؔ࿈ੑΛଊ͑Δ 3. ࣄͷࠩҟҼՌؔΛଊ͑Δ Ӝଞʢ2016, p. 8ʣ
ݚڀͷछྨ छྨ Ξϓϩʔν త ୳ࡧʗݕূ จݙݚڀ 1. ઌߦݚڀΛཧ͠༰Λݕ౼͢Δ ࣮ূݚڀ ࣭తݚڀ
1. จ຺Λߟྀͯ͠ࣄΛଊ͑Δ ୳ࡧܕ ݕূܕ 2. จ຺Λߟྀͯ͠ࢀՃऀͷม༰Λଊ͑Δ 3. ࢀՃऀͷܦݧೝࣝΛଊ͑Δ 4. ઌߦݚڀͷର֎ͷࣄΛ໌Β͔ʹ͢Δ 5. ݚڀͷ৴ጪੑΛߴΊΔ ྔతݚڀ 1. ࣄͷಛΛྔతʹهड़͢Δ 2. ࣄͷؔ࿈ੑΛଊ͑Δ 3. ࣄͷࠩҟҼՌؔΛଊ͑Δ Ӝଞʢ2016, p. 8ʣ
୳ࡧͱݕূ • ୳ࡧܕͷݚڀ • ઌߦݚڀ͕ੵ͞Ε͍ͯͳ͍ςʔϚ • ؍ฉ͖औΓͳͲʹΑΓஸೡʹ σʔλΛऩू͠ɺͦͷத͔ΒԿΒ͔ ͷํੑΛݟग़͢͜ͱΛࢦ͢
୳ࡧͱݕূ • ݕূܕͷݚڀ • ઌߦݚڀ͕ੵ͞Εɺ݁Ռʹ͍ͭͯ ͋Δఔ༧ଌͷཱͯΒΕΔςʔϚ • ʮԾઆʯΛઃఆͯͦ͠ΕΛݕূ͢Δ
σʔλͱղऍ • ݚڀ՝ʹ߹ͬͨσʔλΛूΊΔ • σʔλͷछྨʹ߹ͬͨੳɾղऍΛ ߦ͏
σʔλऩू๏ • ࣄྫݚڀʢcase studyʣ • ؍ฉ͖औΓʹΑΓਂ͘ௐࠪ͢Δ • ௐࠪݚڀʢsurvey studyʣ •
հೖΛߦΘͣʹσʔλΛऩू͢Δ • ࣮ݧݚڀʢexperimental studyʣ • հೖΛߦ͍ͳ͕ΒσʔλΛऩू͢Δ
ੳɾղऍ • σʔλੳͱɺूΊͨσʔλΛ ղऍՄೳͳܗʹཁ͢Δ͜ͱ • σʔλͷछྨʹΑͬͯཁํ๏͕ ҟͳΔ
σʔλͷछྨ • ࣭తσʔλ • ϑΟʔϧυɾϊʔπΠϯλϏϡʔͷ จࣈى͜͠ͳͲɺԽΛΘͳ͍ ʢݴޠʣσʔλ • ྔతσʔλ •
ௐࠪରΛʹΑͬͯදͨ͠ σʔλ
σʔλͷཁ • ࣭తΞϓϩʔν • ࣭తσʔλΛҙຯతʹཁ͢Δ • ྔతΞϓϩʔν • ྔతσʔλΛ౷ܭతʹཁ͢Δ
࣭తΞϓϩʔν • σʔλੳͱղऍ • ੳͱղऍΛߦ͖དྷ͢Δ • ίʔσΟϯάͱΧςΰϦʔԽ • จ຺ͷॏࢹͱް͍هड़ ʢthick
descriptionʣ • స༻Մೳੑʢtransferabilityʣ
࣭తΞϓϩʔν • σʔλऩू๏ɺੳ๏ͱʹଟ༷ • ํ๏ͷཧղʹɺ࣮ࡍͷݚڀʹ ଟ͘৮ΕΔ͜ͱ͕ඞཁ • Ӝଞʢ2016ʣͷୈ5ষΛࢀর ʢݚڀࣄྫؚΊͨղઆ͕͋Δʣ
ྔతΞϓϩʔν • σʔλੳ • هड़౷ܭͱਪଌ౷ܭ • ແ࡞ҝநग़ɺແ࡞ҝׂͷॏཁੑ
• ͷલͷσʔλʢඪຊʣ͔ΒΑΓେ͖ͳจ຺ ʢूஂʣΛਪఆ͢Δ • ඪຊͰ؍͞ΕΔࠩɾ͕ؔɺूஂ͔Βͷ ඪຊநग़࣌ͷޡࠩͰੜ͡Δ֬ʢp ʣΛ ܭࢉ͢Δ • p
͕ج४ʢྟքʣҎԼͰ͋Εʮ༗ҙʯ Ͱ͋ΔʢޡࠩͰͳ͍ʣͱஅ͢Δ ਪଌ౷ܭʢ༗ҙੑݕఆʣ
ूஂ ඪɹຊ ਪఆ σʔλղੳ Σ, F, t, p... ूஂͱඪຊ
• ແ࡞ҝநग़ʢrandom samplingʣ • ඪຊ͔ΒूஂΛਪଌ͢Δ͜ͱ͕Մೳͳͷ ɺແ࡞ҝநग़ͷ͓͔͛ • ແ࡞ҝׂʢrandom assignmentʣ •
ෳͷάϧʔϓΛ࡞Δͱ͖ɺແ࡞ҝׂʹ Αͬͯάϧʔϓؒͷ࣭ੑΛ୲อ͢Δ ແ࡞ҝநग़ɾແ࡞ҝׂ
• • ӳޠڭҭʹ͓͚Δྔతݚڀͷେɺ ແ࡞ҝநग़Λߦ͍ͬͯͳ͍ • ࣮ࡍͷΫϥεΛ͏४࣮ݧݚڀͰɺ ແ࡞ҝׂߦΘΕͳ͍ ແ࡞ҝநग़ɾແ࡞ҝׂ
• ݚڀ݁ՌͷҰൠԽՄೳੑʢgeneralizabilityʣ ͕୲อ͞Ε͍ͯͳ͍ • ݚڀ݁ՌΛଞͷจ຺ʹస༻ʢԠ༻ʣՄೳʹ ͢Δͱ͍͏ɺֶज़తͳݚڀͷେલఏ͕ ຬͨ͞Ε͍ͯͳ͍ ແ࡞ҝநग़ɾແ࡞ҝׂ
͜͜·Ͱͷ·ͱΊ
͜͜·Ͱͷ·ͱΊ • ݚڀՌͷݸਓͷؐݩΛతͱͨ͠ ࣮ફͱͯ͠ͷݚڀ • ݚڀՌͷҰൠԽΛతͱͨ͠ ֶज़తͳݚڀ
͜͜·Ͱͷ·ͱΊ • ֶज़తͳݚڀͷ̎ͭͷύϥμΠϜ • ྔతΞϓϩʔνʢ͘ຬวͳ͘୳Δʣ • ౷ܭతཁɿҰൠԽՄೳੑ • ࣭తΞϓϩʔνʢਂ͘ࡉີʹ୳Δʣ •
ҙຯతཁɿస༻Մೳੑ
ຊͷӳޠڭҭݚڀ
ຊͷӳޠڭҭݚڀ ͜Ε·ͰԿΛߦ͖͔ͬͯͨ ຊͷӳޠڭҭݚڀ
શࠃӳޠڭҭֶձʢJASELEʣͷ߹ Mizumoto, Urano, and Maeda (2014) ຊͷӳޠڭҭݚڀ
Mizumoto et al. (2014) • શࠃӳޠڭҭֶձلཁ ARELE ͷୈ1ʙ24߸ʹ ऩ͞Εͨશจ473ຊͷ͏ͪΦϯϥΠϯͰ ެ։͞Ε͍ͯΔ450ຊΛੳ
Mizumoto et al. (2014) • ӳޠλΠτϧͱཁࢫͷΩʔϫʔυੳ • ૯ޠ79,143ɺҟޠ5,152 • ߹ܭස্Ґ300ޠΛରʹΫϥελʔੳ
• લ12߸ͱޙ12߸ʹ͖Ε͍ʹ͔ΕΔ
Mizumoto et al. (2014) • લ12߸ͱޙ12߸Λൺֱ • ςʔϚͱτϐοΫ • ݚڀख๏
• ޮՌྔͱݕఆྗ
Mizumoto et al. (2014) • લ12߸ͱޙ12߸Λൺֱ • ςʔϚͱτϐοΫ • ݚڀख๏
• ޮՌྔͱݕఆྗ
Mizumoto et al. (2014) • ݚڀͷछྨʢ࣮ূɾௐࠪɾ࣮ફɾཧʣ • ݚڀͷతʢ୳ࡧɾݕূʣ • Ξϓϩʔνʢྔɾ࣭ɾࠞ߹ʣ
• հೖʢ༗ɾແʣ
ཧݚڀ ࣮ફใࠂ ௐࠪใࠂ ࣮ূݚڀ ࢉ
߸ ߸
ͦͷଞ ݕূ ୳ࡧ ࢉʢཧݚڀຊҎ֎ܭຊʣ ߸
߸
ͦͷଞ ϛοΫε ࣭ ྔ ࢉʢཧݚڀຊҎ֎ܭຊʣ
߸ ߸
հೖͳ͠ հೖ͋Γ ܝࡌຊ ߸
߸
Mizumoto et al. (2014) • ·ͱΊ • ࣮ફใࠂ͕গͳ͍ • ୳ࡧܕ͕ଟ͍ʢ͔͠૿͍͑ͯΔʣ
• ࣭తΞϓϩʔν͕ͱͯগͳ͍ • հೖݚڀগͳ͍͕૿͍͑ͯΔ
த෦۠ӳޠڭҭֶձʢCELESʣͷ߹ Ӝଞ (2012) Ӝଞ (2012)
Ӝଞ (2012) • த෦۠ӳޠڭҭֶձلཁୈ36ʙ41߸ʹऩ ͞Εͨશจͷ͏࣮ͪূݚڀ151ຊΛੳ
Ӝଞ (2012) • ݚڀͷతʢ୳ࡧɾݕূʣ • σʔλʢྔɾ࣭ʣ • ݚڀͷ݁ʢ୳ࡧɾݕূʣ
" ཧݚڀɺจݙݚڀର͔Β֎ͨ͠ " ʻ࣮ફใࠂʼʹ͍ͭͯผ్ੳͨ͠ ํ๏ ! ҎԼͷྨදʹج͍ͮͯݸʑͷจΛྨͨ͠ ! શମͷΛѲ͢Δ͜ͱ͕తͷͨΊɺΫϩενΣοΫͷ࡞ۀলུͨ͠ λΠϓ
త σʔλ ݁ ಛྫ A ୳ࡧ ྔ ୳ࡧ Ξϯέʔτςετʹجͮ͘ύΠϩοτతݚڀ B ୳ࡧ ྔ ݕূ ֓೦ͷߏతɾૢ࡞తఆ͕ٛෆेʀԾઆ͕ෆ໌ྎͳ ͷʹྔతσʔλͰݕূΛͯ͠͠·͍ͬͯΔ C ୳ࡧ ࣭ ୳ࡧ ΠϯλϏϡʔ؍ʹجͮ͘ɺهड़తݚڀ D ୳ࡧ ࣭ ݕূ ͕݁ඈ༂͠ա͗ͷλΠϓͷݚڀ E ݕূ ྔ ୳ࡧ ݕূͷͨΊͷσʔλ͕ෆे͔ɺ ՝ͷߜΓࠐΈૢ ࡞తఆ͕ٛेͰͳͯ͘ɺ୳ࡧʹऴΘͬͨλΠϓ F ݕূ ྔ ݕূ యܕతͳԾઆݕূܕͷ࣮ূݚڀ G ݕূ ࣭ ୳ࡧ E ͱಉ༷Ͱɺ࣭తσʔλΛओͱ͢Δݚڀ H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚڀ ݁Ռ 64 70
H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚ ɽ݁Ռ (%) 42.4 6.6 11.3
1.3 2.0 27.2 1.3 0.0 7.9 64 10 17 2 3 41 2 0 12 0 10 20 30 40 50 60 70 A B C D E F G H ͦͷଞ
" ཧݚڀɺจݙݚڀର͔Β֎ͨ͠ " ʻ࣮ફใࠂʼʹ͍ͭͯผ్ੳͨ͠ ํ๏ ! ҎԼͷྨදʹج͍ͮͯݸʑͷจΛྨͨ͠ ! શମͷΛѲ͢Δ͜ͱ͕తͷͨΊɺΫϩενΣοΫͷ࡞ۀলུͨ͠ λΠϓ
త σʔλ ݁ ಛྫ A ୳ࡧ ྔ ୳ࡧ Ξϯέʔτςετʹجͮ͘ύΠϩοτతݚڀ B ୳ࡧ ྔ ݕূ ֓೦ͷߏతɾૢ࡞తఆ͕ٛෆेʀԾઆ͕ෆ໌ྎͳ ͷʹྔతσʔλͰݕূΛͯ͠͠·͍ͬͯΔ C ୳ࡧ ࣭ ୳ࡧ ΠϯλϏϡʔ؍ʹجͮ͘ɺهड़తݚڀ D ୳ࡧ ࣭ ݕূ ͕݁ඈ༂͠ա͗ͷλΠϓͷݚڀ E ݕূ ྔ ୳ࡧ ݕূͷͨΊͷσʔλ͕ෆे͔ɺ ՝ͷߜΓࠐΈૢ ࡞తఆ͕ٛेͰͳͯ͘ɺ୳ࡧʹऴΘͬͨλΠϓ F ݕূ ྔ ݕূ యܕతͳԾઆݕূܕͷ࣮ূݚڀ G ݕূ ࣭ ୳ࡧ E ͱಉ༷Ͱɺ࣭తσʔλΛओͱ͢Δݚڀ H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚڀ ݁Ռ 64 70
H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚ ɽ݁Ռ (%) 42.4 6.6 11.3
1.3 2.0 27.2 1.3 0.0 7.9 64 10 17 2 3 41 2 0 12 0 10 20 30 40 50 60 70 A B C D E F G H ͦͷଞ 61.6%
" ཧݚڀɺจݙݚڀର͔Β֎ͨ͠ " ʻ࣮ફใࠂʼʹ͍ͭͯผ్ੳͨ͠ ํ๏ ! ҎԼͷྨදʹج͍ͮͯݸʑͷจΛྨͨ͠ ! શମͷΛѲ͢Δ͜ͱ͕తͷͨΊɺΫϩενΣοΫͷ࡞ۀলུͨ͠ λΠϓ
త σʔλ ݁ ಛྫ A ୳ࡧ ྔ ୳ࡧ Ξϯέʔτςετʹجͮ͘ύΠϩοτతݚڀ B ୳ࡧ ྔ ݕূ ֓೦ͷߏతɾૢ࡞తఆ͕ٛෆेʀԾઆ͕ෆ໌ྎͳ ͷʹྔతσʔλͰݕূΛͯ͠͠·͍ͬͯΔ C ୳ࡧ ࣭ ୳ࡧ ΠϯλϏϡʔ؍ʹجͮ͘ɺهड़తݚڀ D ୳ࡧ ࣭ ݕূ ͕݁ඈ༂͠ա͗ͷλΠϓͷݚڀ E ݕূ ྔ ୳ࡧ ݕূͷͨΊͷσʔλ͕ෆे͔ɺ ՝ͷߜΓࠐΈૢ ࡞తఆ͕ٛेͰͳͯ͘ɺ୳ࡧʹऴΘͬͨλΠϓ F ݕূ ྔ ݕূ యܕతͳԾઆݕূܕͷ࣮ূݚڀ G ݕূ ࣭ ୳ࡧ E ͱಉ༷Ͱɺ࣭తσʔλΛओͱ͢Δݚڀ H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚڀ ݁Ռ 64 70
H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚ ɽ݁Ռ (%) 42.4 6.6 11.3
1.3 2.0 27.2 1.3 0.0 7.9 64 10 17 2 3 41 2 0 12 0 10 20 30 40 50 60 70 A B C D E F G H ͦͷଞ 49.0%
" ཧݚڀɺจݙݚڀର͔Β֎ͨ͠ " ʻ࣮ફใࠂʼʹ͍ͭͯผ్ੳͨ͠ ํ๏ ! ҎԼͷྨදʹج͍ͮͯݸʑͷจΛྨͨ͠ ! શମͷΛѲ͢Δ͜ͱ͕తͷͨΊɺΫϩενΣοΫͷ࡞ۀলུͨ͠ λΠϓ
త σʔλ ݁ ಛྫ A ୳ࡧ ྔ ୳ࡧ Ξϯέʔτςετʹجͮ͘ύΠϩοτతݚڀ B ୳ࡧ ྔ ݕূ ֓೦ͷߏతɾૢ࡞తఆ͕ٛෆेʀԾઆ͕ෆ໌ྎͳ ͷʹྔతσʔλͰݕূΛͯ͠͠·͍ͬͯΔ C ୳ࡧ ࣭ ୳ࡧ ΠϯλϏϡʔ؍ʹجͮ͘ɺهड़తݚڀ D ୳ࡧ ࣭ ݕূ ͕݁ඈ༂͠ա͗ͷλΠϓͷݚڀ E ݕূ ྔ ୳ࡧ ݕূͷͨΊͷσʔλ͕ෆे͔ɺ ՝ͷߜΓࠐΈૢ ࡞తఆ͕ٛेͰͳͯ͘ɺ୳ࡧʹऴΘͬͨλΠϓ F ݕূ ྔ ݕূ యܕతͳԾઆݕূܕͷ࣮ূݚڀ G ݕূ ࣭ ୳ࡧ E ͱಉ༷Ͱɺ࣭తσʔλΛओͱ͢Δݚڀ H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚڀ ݁Ռ 64 70
H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚ ɽ݁Ռ (%) 42.4 6.6 11.3
1.3 2.0 27.2 1.3 0.0 7.9 64 10 17 2 3 41 2 0 12 0 10 20 30 40 50 60 70 A B C D E F G H ͦͷଞ 30.5%
" ཧݚڀɺจݙݚڀର͔Β֎ͨ͠ " ʻ࣮ફใࠂʼʹ͍ͭͯผ్ੳͨ͠ ํ๏ ! ҎԼͷྨදʹج͍ͮͯݸʑͷจΛྨͨ͠ ! શମͷΛѲ͢Δ͜ͱ͕తͷͨΊɺΫϩενΣοΫͷ࡞ۀলུͨ͠ λΠϓ
త σʔλ ݁ ಛྫ A ୳ࡧ ྔ ୳ࡧ Ξϯέʔτςετʹجͮ͘ύΠϩοτతݚڀ B ୳ࡧ ྔ ݕূ ֓೦ͷߏతɾૢ࡞తఆ͕ٛෆेʀԾઆ͕ෆ໌ྎͳ ͷʹྔతσʔλͰݕূΛͯ͠͠·͍ͬͯΔ C ୳ࡧ ࣭ ୳ࡧ ΠϯλϏϡʔ؍ʹجͮ͘ɺهड़తݚڀ D ୳ࡧ ࣭ ݕূ ͕݁ඈ༂͠ա͗ͷλΠϓͷݚڀ E ݕূ ྔ ୳ࡧ ݕূͷͨΊͷσʔλ͕ෆे͔ɺ ՝ͷߜΓࠐΈૢ ࡞తఆ͕ٛेͰͳͯ͘ɺ୳ࡧʹऴΘͬͨλΠϓ F ݕূ ྔ ݕূ యܕతͳԾઆݕূܕͷ࣮ূݚڀ G ݕূ ࣭ ୳ࡧ E ͱಉ༷Ͱɺ࣭తσʔλΛओͱ͢Δݚڀ H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚڀ ݁Ռ 64 70
H ݕূ ࣭ ݕূ ϝλݴޠతهड़ςετߏԽ؍ʹجͮ͘ݚ ɽ݁Ռ (%) 42.4 6.6 11.3
1.3 2.0 27.2 1.3 0.0 7.9 64 10 17 2 3 41 2 0 12 0 10 20 30 40 50 60 70 A B C D E F G H ͦͷଞ 13.9%
Ӝଞ (2012) • ୳ࡧΛతͱ͢Δݚڀ͕ଟ͍ • ಛʹΞϯέʔτௐࠪΛத৺ͱͨ͠ྔతݚڀ ཱ͕ͭ • ٯʹԾઆݕূΛతͱ͢Δݚڀ͕গͳ͍ •
࣭తσʔλΛѻͬͨݚڀ͕গͳ͍
Ӝଞ (2012) • ୳ࡧܕ͕ଟ͘ɺݕূܕ͕গͳ͍ • ݚڀՌͷू͕ਐ·ͳ͍͓ͦΕ • ݕূܕݚڀ͕૿͑Δ͜ͱ͕·͍͠ • ୳ࡧܕͦͷͷʹ͕͋ΔΘ͚Ͱͳ͍
Ӝଞ (2012) • ͳͥ୳ࡧܕݚڀ͕ଟ͍ͷ͔ • ϖʔδͷ੍ݶ • ݕূՄೳͳԾઆܗ·ͰͷྲྀΕΛ࡞Εͳ͍ • ݚڀͷ࣭ͷ
• ઌߦݚڀͷੳ͕ෆे • ʮͱΓ͋͑ͣσʔλΛूΊ·ͨ͠ʯతݚڀ
Ӝଞ (2012) • ࣭తσʔλΛѻͬͨݚڀ͕গͳ͍ • ࣭తݚڀ๏͕ਁಁ͍ͯ͠ͳ͍Մೳੑ • ϖʔδͷ੍ݶ͕͔ͤʹͳ͍ͬͯΔՄೳੑ • ৹ࠪମ੍͕͍ͬͯͳ͍Մೳੑ
Ӝଞ (2012) • ·ͱΊ • దͳݚڀख๏Λબ͢Δॏཁੑ • ݕূܕݚڀΛ૿͢ඞཁੑ • ࣭తݚڀΛ૿͢ඞཁੑ
͜͜·Ͱͷ·ͱΊ
͜͜·Ͱͷ·ͱΊ • దͳݚڀ՝ͷઃఆ • ݚڀతʹ߹ͬͨσʔλऩूͱੳ
ڭҭతࣔࠦ
• ӳޠڭҭݚڀͰɺ࠷ޙʹʮڭҭతࣔࠦʯΛ ड़Δ͜ͱ͕ظ͞ΕΔ͜ͱ͕ଟ͍ • ୯Ұͷݚڀ͔ΒࣔࠦΛड़ͯΑ͍ͷ͔ ڭҭతࣔࠦ
• ྔతݚڀ • ҰൠԽՄೳੑ͕୲อ͞Ε͍ͯͳ͍߹ɺ ຊདྷͳΒݚڀ݁ՌҰൠԽͰ͖ͳ͍ • ಉ͜͡ͱΛผͷจ຺Ͱߦͬͯɺ ಉ݁͡Ռ͕ಘΒΕΔอূͳ͍ • ࠶ݱੑʢreproducibilityʣ
ڭҭతࣔࠦ
• ࠶ݱੑʢreproducibilityʣ·ͨ࠶ݱՄೳੑ ʢreplicabilityʣ • ৺ཧֶͰେنʹߦͬͨࢼݚڀͰɺ ಉ݁͡Ռ͕࠶ݱ͞Εͨͷ4ׂҎԼͩͬͨ ʢຊࣾձ৺ཧֶձใҕһձ, 2016ʣ • ӳޠڭҭͰͲ͏͔ʢߟ͑ͨ͘ͳ͍ʣ
ڭҭతࣔࠦ
• ࣭తݚڀ • ݚڀ݁Ռ͕จ຺ʹґଘ͢ΔͨΊɺ ͦͦҰൠԽߦΘͳ͍ • ް͍هड़Λߦ͏͜ͱͰɺݚڀ݁Ռ͕ผͷ จ຺Ͱͯ·Δ͔Ͳ͏͔ͷஅΛ ಡऀʹҕͶΔ ڭҭతࣔࠦ
• ӳޠڭҭݚڀʹ͓͍ͯɺ୯Ұͷݚڀ͔Β աͳҰൠԽʢڭҭతࣔࠦʣΛߦ͏ͷ ෆద • ͰͲ͏͢Δʁ ڭҭతࣔࠦ
• ݚڀͷݶքΛਅ伨ʹड͚ࢭΊɺڭҭతࣔࠦ Ͱ͖Δ͚ͩ conservative ͳͷʹཹΊΔ • େ͖ͳڭҭతࣔࠦɺݸʑͷݚڀͰͳ͘ɺ ෳͷؔ࿈ݚڀͷ݁ՌΛ·ͱΊͨܗͰߦ͏ • ݚڀͷ౷߹ʢresearch
synthesisʣͱ ϝλੳʢmeta-analysisʣ • ࢼʢreplicationʣͷॏཁੑ ڭҭతࣔࠦ
• ౷߹ɾࢼʹ͑͏ΔݚڀΛߦ͏͜ͱ͕ॏཁ • ݚڀՁͷߴ͍ςʔϚ • ࢼΛߦ͏ͨΊͷใ։ࣔ • هड़౷ܭྔͷ։ࣔ • ʢͰ͖Εʣແ࡞ҝׂͷσβΠϯ
ڭҭతࣔࠦ
͜͜·Ͱͷ·ͱΊ
͜͜·Ͱͷ·ͱΊ • ݚڀՌͷݸਓͷؐݩΛతͱͨ͠ ࣮ફͱͯ͠ͷݚڀ • ݚڀՌͷҰൠԽΛతͱֶͨ͠ज़తͳݚڀ • ͨͩ͠୯Ұͷݚڀ͔ΒͷҰൠԽ͍͠
͜͜·Ͱͷ·ͱΊ • ֶज़తͳݚڀΛߦ͏ͷ͔ͳΓେม • ӳޠڭࢣݚڀΛߦ͏ඞཁ͕͋Δͷ͔
͜͜·Ͱͷ·ͱΊ • ඞཁͳ͍ɺ͚ͩͲ… • ֶज़తͳݚڀΛ͢Δڭࢣ͕૿͑Δ͜ͱ͕ ݚڀͷੵʹͭͳ͕Γ • ͦͷ݁ՌϑΟʔϧυશମͷൃలʹߩݙͰ͖Δ
ݚڀҎ֎ͷબࢶ • ֶज़తݚڀΛߦ͏ͷ͍͠߹ɺ ࣮ફతͳݚڀʹઓͯ͠ΈΔ • ͦΕ͍͠߹ɺʮݚڀʯߦΘͣʹ ࣮ફͷهΛ͚ͭͯΈΔ • ୳ڀత࣮ફʢexploratory practiceʣ
୳ڀత࣮ફ • Allright (2003) ͕ఏএ • ղܾͰͳ͘ݱঢ়ཧղΛతͱͨ͠׆ಈ • ࣋ଓՄೳͳܗͰͷ׆ಈ •
࣮ફͷهड़ʢهʣͱͦΕʹ͍ͭͯͷল ʢϦϑϨΫγϣϯʣͷه
୳ڀత࣮ફ • ʮݚڀʯͰͳ͍ͨΊൃදػձݶΒΕΔ • த෦۠ӳޠڭҭֶձʢCELESʣͰ࣮ફใࠂ ͱͯ͠ͷ୳ڀత࣮ફ͕ใࠂ͞Ε͍ͯΔ • ࠓޙଞֶձͰ૿͑Δ͜ͱΛظ
• ͡Ίͯͷӳޠڭҭݚڀɿ ԡ͓͖͍͑ͯͨ͞ίπͱ ϙΠϯτʢݚڀࣾʣ • ݚڀͷํ๏ʹ͍ͭͯɺଟ͘ ͷ࣮ྫΛհ͠ͳ͕Βղઆ͠ ͍ͯ·͢ ओͳࢀߟจݙ
• ֎ࠃޠڭҭݚڀϋϯυϒοΫ • ࣭తɾྔతݚڀͷ྆ํʹ͍ͭ ͯஸೡʹղઆ͞Ε͍ͯ·͢ ओͳࢀߟจݙ
શମͷ·ͱΊ
શମͷ·ͱΊ • ࣮ફͱͯ͠ͷݚڀͱֶज़తͳݚڀ • ݚڀͷ̏ཁૉʢݚڀ՝ɺσʔλɺղऍʣ • ͜Ε·Ͱͷݚڀͷ֓؍ • ҰൠԽͱڭҭతࣔࠦͷ͠͞ •
ݚڀͷੵͷॏཁੑ • ୳ڀత࣮ફͷՄೳੑ Ken Urano
[email protected]
https://www.urano-ken.com/research/NGUSeminar
• Allright, D. (2003). Exploratory Practice: rethinking practitioner research in
language teaching. Language Teaching Research, 7, 113–141. https://doi.org/10.1191/1362168803lr118oa • Mizumoto, A., Urano, K., & Maeda, H. (2014). A systematic review of published articles in ARELE 1–24 : Focusing on their themes, methods, and outcomes. ARELE, 25, 33–48. https://doi.org/10.20581/arele.25.0_33 • ຊࣾձ৺ཧֶձใҕһձ. (2016). ৺ཧֶݚڀͷ࠶ݱੑʹؔ͢Δ૪. Retrieved from: https://sites.google.com/ site/jssppr/home/reproducibility • Nunan, D. (1992). Research methods in language learning. Cambridge University Press. • ཧɾਫຊಞ (ฤ). (2014). ʰ֎ࠃޠڭҭݚڀϋϯυϒοΫ: ݚڀख๏ͷΑΓྑ͍ཧղͷͨΊʹ (վగ൛)ʱ౦ژ: দദ ࣾ. • ాதɾ∁ѥرࢠɾ౻ాɾୌ༤ҰɾञҪӳथ. (2018). ʰӳޠڭࢣͷͨΊͷ࣮ફݚڀΨΠυϒοΫʱ౦ژ: େमؗॻళ. • ӜݚɾञҪӳथɾ∁ѥرࢠɾాதɾ౻ాɾຊాউٱɾཧཅҰ. (2012). ӳޠڭҭݚڀ๏ͷաڈɾݱࡏɾ ະདྷ. ୈ42ճத෦۠ӳޠڭҭֶձذෞେձɾ՝ผݚڀϓϩδΣΫτ. • ӜݚɾཧཅҰɾాதɾ౻ాɾ∁ѥرࢠɾञҪӳथ. (2016). ʰ͡Ίͯͷӳޠڭҭݚڀ: ԡ͓͖͑ͯ͞ ͍ͨίπͱϙΠϯτʱ౦ژ: ݚڀࣾ. • ٢ాୡ߂ɾۄҪ݈ɾԣߔਈҰɾࠓҪ༟೭ɾ༄ཅհ (ฤ). (2009). ʰϦϑϨΫςΟϒͳӳޠڭҭΛࢦͯ͠: ڭࢣͷ ޠΓ͕͘तۀݚڀʱ౦ژ: ͻͭ͡ॻ. Ҿ༻จݙ