基於社群偵測發掘意見領袖之二級資訊傳播模式對於提升問題導向網路合作學習成效之影響研究 - 政大學術集成

全文

(1)୯ҥࡹ‫ݯ‬εᏢკਜၗૻᆶᔞਢᏢࣴ‫܌ز‬ ᅺγፕЎ Master’s Thesis Graduate Institute of Library, Information and Archival Studies National Chengchi University. ୷‫ޗܭ‬ဂୀෳว௚ཀ‫ـ‬ሦ೑ϐΒભၗૻ໺ኞኳ ࢇ ‫ݽ‬. σ. Ᏸ. Ȇ ୽. ҳ Ԅჹ‫ܭ‬ගϲୢᚒᏤӛᆛၡӝբᏢಞԋਏϐቹៜ ࣴ‫ز‬. Ȇ. Two-step flow of communication for promoting collaborative problem-based learning performance based on community detection scheme with exploring opinion leaders. ů. Ŧų. ŪŰ. ŴŪŵ. ź. ŏŢŵ. Ţŭ. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ŷ. ࡰᏤ௲௤Ǻഋ‫ד‬ሎ റγ Adviser : Dr. Chih-Ming Chen. ࣴ‫ز‬ғǺෞ‫ے‬ᓄ ኗ Author : Zong-Lin You. ύ๮҇୯΋ԭ႟ѤԃΎД July, 2015.

(2) ᖴᜏ! ಖ‫ܭ‬Ǵ೭΋Ϻ‫ٰډ‬ΑǴම࿶΋ࡋаࣁค‫ݤ‬ቪᖴᜏΑǶ! ‫ځ‬ჴࣴ‫܌ز‬೭చၡࢂӧεᏢਔයࢂགྷ೿ؒགྷၸ‫ޑ‬Ǵ‫ډޔ‬εΟਔ‫ک‬Դৣፋᜢ ‫ܭ‬౥཰ϐࡕ‫ޑ‬٣Ǵω،‫ۓ‬၂၂࣮Ǵ‫܈‬೚ࢂԴϺჹ‫ޑך‬ኇኈǴᡣ΋ঁӧਠԋᕮද ೯‫ޑ‬ΓǴӧ௢ᅩਔഢ‫ڗ‬ಃΟǴԵ၂аಃΟӜ‫ޑ‬ԋᕮԵ຾ࡹεკᔞ‫܌‬Ƕӧ೭‫ٿ‬ԃ ‫زࣴޑ‬ВηύǴᖙ౛ਦǵЦఘ࣓ǵጰܴДǵ݅ѯ௵ǵߋࣄ϶฻‫؂‬ՏԴৣ൩Ⴝࢂ Р҆૓‫៝ྣޑ‬Ϸ௲Ꮴ‫ॺך‬Ǵ੝ձࢂࡰᏤ௲௤ഋ‫ד‬ሎԴৣǴӵ݀྽ਔؒԖா‫ޑ‬Ⴔ ᓰǴ೭ጇፕЎεཷࢂό‫ـ‬ϺВΑǴΨाᖴᖴα၂‫ہ‬঩ЦᙦᆣԴৣϷէᡉமԴ ৣǴᖴᖴգॺჹ‫ޑך‬ፕЎගрᆒ៘‫ࡌޑ‬᝼Ǵ٬‫ޑך‬ፕЎ‫׳‬укჴǴΨᖴᖴ໺ဖ Ϸܴ໥շ௲Ǵ೭‫ٿ‬ԃ೿΋‫ډڙޔ‬գॺ‫៝ྣޑ‬ǴٌधգॺΑǶ!. ࢇ ‫ ݽ‬σ. ྽ฅᗋाᖴᖴ‫ࢂޑ‬εᏢਔය‫ޑ‬੤Ꮴ݅և⩰ԴৣǴ྽߃Ӣࣁா‫ޑ‬΋ৢ၉Ǵω ᡣ‫ך‬໒௴ࣴ‫ޑز‬ғࢲǶᗋԖεᏢਔය‫ޑ‬շ௲λูǵᓉ‫ە‬ǵᚯΓǴᗨฅ౥཰ࡕΟ όϖਔӣѐ๏գॺబഞྠǴӕਔΨதத೏գॺࡩधǴՠ੿‫ޑ‬ᖴᖴգॺᜫཀ᠋‫ך‬ ӗधНǴΨᜫཀග‫ٮ‬գॺ‫ݤ࣮ޑ‬ǹௗ๱ࢂჴᡍ࠻‫ޑ‬უՔॺǴ२ӃࢂѲ΍ᏢߏǴ ‫ٿ‬ԃ‫زࣴޑ‬ғࢲύ੿‫ޑ‬๏Αࡐӭᝊ຦ཀ‫ـ‬Ǵёаᡣ‫زࣴޑך‬΋‫؁‬΋‫و؁‬ΠѐǴ ҂ٰΨाᝩុഞྠா΋ࢤਔ໔ΑǴᗋԖ߷໥Ꮲ‫ۊ‬ǴவεᏢਔය൩Ӣࣁࢂ‫ޔ‬ឦ‫ޑ‬ ᜢ߯Ǵ΋ၡаٰ੿‫ޑ‬ᔅΑ‫ߚך‬தӭԆǴࡐᄪ۩Ψࡐᠠ໹Ԗ΋Տᓬ‫ޑذ‬Ꮲ‫ۆ‬Ƕඵ บᗋԖᄪ๮Ǵᖴᖴգॺӧ຾Չჴᡍਔ‫ޑ‬εΚᔅԆǴ੝ձࢂᄪ๮Ǵࣴ‫ز‬ჹຝаϷ ਔ໔‫ڐޑ‬ፓ੿‫ޑ‬೿ഞྠாΑǴؒԖா‫ڐޑ‬ፓ೭ጇፕЎό‫ޕ‬ၰࣗሶਔংёаֹ. ҳ. Ȇ. Ȇ ୽. Ᏸ. ŴŪŵ. ź. ŏŢŵ. ů. Ţŭ. Ŧų. ŪŰ. ԋǴᗋԖऍඵǴӧൔ஦аϷα၂ਔ೿ഞྠգΑǶЎ‫ޱ‬ǵျണǵ๥໐ǴᖴᖴᏢ‫׌‬ ‫ॺۂ‬ӧα၂ਔ‫ڐޑ‬շǴ‫׆‬ఈ҂ٰգॺΨૈ໩ճ‫ޑ‬ౢрፕЎǹᖴᖴკᔞ‫܌‬ΜΖۛ. ŷ. ‫ޑ‬ӕᏢॺǴӧ‫૶ޑך‬Ꮻύ੮ΠΑు‫ޑڅ‬΋।Ǵ੝ձࢂҥരǵӧ࣭ǵ‫࣑ٵ‬ǵऩ ྼǴᗨฅ‫ࢂॺך‬όӕಔ‫ޑ‬ӕᏢǴՠ‫ډ‬നࡕϸԶࢂനዕ᛽‫ޑ‬ӕᏢǴᖴᖴգॺӧ‫ך‬ നեዊ‫ޑ‬ਔংᜫཀ՜р΋‫ע‬ЋǴၟգॺύϱӞ໭ാեᖻ‫ޑ‬ਔ໔ࢂࣴ‫ز‬ғࢲύന ‫ܫ‬᚞‫ޑ‬ਔংǶ!. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ‫ٿ‬ԃନΑ‫ۺ‬ਜቪፕЎѦǴ՞Ᏽനӭਔ໔Ǵεཷ൩ࢂӧკਜᓔॶ੤ΑǴԶӧ კਜᓔ‫ޑ‬Вη္ǴᏢ‫ډ‬ΑࡐӭᔈჹϷೀ౛٣௃‫ޑ‬Б‫ݤ‬ǴӕਔΨግ൩Αᐱ྽΋य़ ‫ૈޑ‬ΚǴाᖴᖴ࣬ӵ‫ۆ‬ǵฐ‫ۆޱ‬ǵ☰ൈ‫ۆ‬Ǵᖴᖴգॺჹ‫ך‬όჇ‫ࡰޑྠځ‬ᏤаϷ Ѝ࡭‫ך‬Ǵૈᇡ᛽ΟՏᏢ‫۩ࡐޑ੿ۊ‬ၮǶඵཧаϷะ‫ޱ‬Ǵᖴᖴգॺӧ‫ॶך‬੤ਔ΋ ‫ޔ‬ഉ‫܎ࠖך‬ǴЀ‫ࢂځ‬ะ‫ޱ‬Ǵ੿ࢂϼఁᇡ᛽‫ی‬Ǵ‫ॺך‬Ԗϼӭ၉ᚒёаಠΑǶ! ќѦाᖴᖴ‫ޑך‬ৎΓǴᖴᖴգॺ‫ޑ‬х৒ᆶЍ࡭ǴӕਔΨགᖴٗ٤࣮ࡧ‫ޑך‬ ΓǴӢࣁԖգॺǴ‫ך‬ωૈόᘐ‫ޑ‬຾‫؁‬ǶനࡕǴӧЎύؒԖග‫ॺ϶ܻޑډ‬ǴΨᖴ ᖴգॺ‫ޑ‬Ѝ࡭Ƕ! ‫ے‬ᓄ!!ᙣᇞ! ύ๮҇୯ 215 ԃ 8 Д!.

(3) ᄔा! !!!!ᒿ๱ၗૻࣽ‫ޑמ‬ว৖ǴኧՏᏢಞ‫ޑ‬ᢀ‫ۺ‬೴ᅌᑫଆǴӧΒΜ΋Шइமፓ‫ޕ‬᛽ ࿶ᔮ‫ޑ‬ϞϺǴԾЬᏢಞϷୢᚒှ،ૈΚ‫ޑ‬Ꭶԋ‫׳‬ᡉख़ाǴԶᙖҗᆛၡ຾Չୢᚒ ᏤӛӝբᏢಞǴᏢಞ‫ޣ‬ё‫׳‬Бߡ‫ޑ‬೸ၸԾЬᏢಞБԄ୻Ꭶୢᚒှ،ૈΚǶฅԶ Ꮲಞ‫ޣ‬ӧ຾ՉᆛၡӝբᏢಞ‫ޑ‬ϕ୏ය໔཮ௗԏ‫ډ‬εໆٰԾӕᏆ‫ޑ‬ၗૻǴԖ٤Ꮲ ಞ‫ޣ‬த཮Ӣࣁค‫ݤ‬ղᘐၗૻ‫҅ޑ‬ዴ‫܄‬ǴԶค‫ݤ‬Ԗਏᒧ᏷ǵղᘐǵϩ‫݋‬ᆶ᏾ӝ‫܌‬ ᕇள‫ޑ‬ၗૻǴ຾ԶᢀఈӕᏆ‫ࢂ܈‬ཀ‫ـ‬ሦ೑‫ޑ‬ཀ‫ـ‬ǶӢԜǴҁࣴ‫ز‬ճҔᏢಞ‫ޣ‬ӧ ୢᚒᏤӛᆛၡӝբᏢಞᐕำύ‫܌‬ౢғ‫཮ޗޑ‬ᆛၡϕ୏ၗ਑ǴճҔࠔ፦ R ‫ڄ‬ኧ่ ӝ୷Ӣᆉ‫ݤ‬຾Չ‫ޗ‬ဂ௖୎Ǵ٠མଛ QbhfSbol ᄽᆉ‫פݤ‬൨р‫ޗঁ؂‬ဂύ‫ޑ‬ၨཀ‫ـ‬ ሦ೑Ǵ௖૸௦Ҕ௲ৣ‫ޔ‬ௗ຾Չၗૻ໺ኞ‫ޑ‬΋ભၗૻ໺ኞኳԄᆶ೸ၸ‫ޗ‬ဂཀ‫ـ‬ሦ ೑຾Չၗૻ໺ኞ‫ޑ‬Βભၗૻ໺ኞኳԄჹ‫ܭ‬Ꮲಞ‫ޑޣ‬Ꮲಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏Ϸ იᡏᏉᆫΚ‫ޑ‬ቹៜǶԜѦǴΨ௖૸௦Ҕ೭‫ٿ‬ᅿၗૻ໺ኞኳԄ‫ޑ‬όӕ‫܄‬ձϷόӕ Γ਱੝፦Ꮲಞ‫ޑޣ‬Ꮲಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ϷიᡏᏉᆫΚࢂց‫ڀ‬Ԗᡉ๱ৡ౦Ƕ!. ҳ. ࢇ ‫ ݽ‬σ. !!!!ࣴ‫่݀ز‬ว౜Ǻ)1*ӧୢᚒᏤӛᆛၡӝբᏢಞᕉნΠǴ௦Ҕวթૻ৲๏ཀ‫ـ‬. Ȇ ୽. Ᏸ. ሦ೑ϐΒભၗૻ໺ኞኳԄ‫ޑ‬ჴᡍಔᏢಞ‫ޣ‬ǴӧᏢಞԋਏ΢ᡉ๱ᓬ‫ܭ‬௲ৣ೸ၸᆛ ઠϦ֋ϐ΋ભ໺ኞኳԄ‫ޑ‬௓‫ڋ‬ಔᏢಞ‫ޣ‬ǹ)2*ӧୢᚒᏤӛᆛၡӝբᏢಞᕉნΠǴ. Ȇ. ௦Ҕวթૻ৲๏ཀ‫ـ‬ሦ೑ϐΒભၗૻ໺ኞኳԄ‫ޑ‬ჴᡍಔζ‫܄‬Ꮲಞ‫ޣ‬ǴӧᏢಞԋ ਏ΢ᡉ๱ᓬ‫ܭ‬೸ၸ௲ৣᆛઠϦ֋ϐ΋ભၗૻ໺ኞኳԄ‫ޑ‬௓‫ڋ‬ಔζ‫܄‬Ꮲಞ‫ޣ‬Ǵՠ ‫ٿ‬ಔ‫܄ت‬Ꮲಞ‫ޣ‬ϐ໔߾คᡉ๱ৡ౦ǹ)3*ӧୢᚒᏤӛᆛၡӝբᏢಞᕉნΠǴ௦Ҕ วթૻ৲๏ཀ‫ـ‬ሦ೑ϐΒભၗૻ໺ኞኳԄ‫ޑ‬ჴᡍಔᏢಞ‫ޣ‬Ǵӧߦ຾ӕᏆϕ୏ԋ. ŴŪŵ. ź. ŏŢŵ. ŪŰ. Ŧų. ਏ΢ᡉ๱ᓬ‫ܭ‬௲ৣ೸ၸᆛઠϦ֋ϐ΋ભ໺ኞኳԄ‫ޑ‬௓‫ڋ‬ಔᏢಞ‫)<ޣ‬4*೸ၸࠔ፦ R ‫ڄ‬ኧ่ӝ୷Ӣᄽᆉ‫ݤ‬ୀෳ‫ޗ‬ဂǴаϷ٬Ҕ QbhfSbol ‫פ‬൨‫ޗ‬ဂཀ‫ـ‬ሦ೑ϐБ ‫ݤ‬Ǵૈᆒዴ‫ڐޑ‬շ௲ৣ‫ډפ‬ୢᚒᏤӛᆛၡӝբᏢಞ‫ޗ‬ဂϐཀ‫ـ‬ሦ೑Ƕ!. ů. Ţŭ. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ŷ. !!!!നࡕǴਥᏵࣴ‫่݀ز‬Ǵҁࣴ‫ز‬ගр௲ᏢჴࡼϷ҂ٰࣴ‫ز‬Бӛࡌ᝼Ǵ‫ុࡕٮ‬ ࣴ‫ୖز‬Եа຾Չ‫׳‬ుΕ‫ޑ‬௖‫ز‬Ƕ! ! ᜢᗖӷǺ‫ޗ‬ဂ௖୎ǵ‫཮ޗ‬ᆛၡϩ‫݋‬ǵୢᚒᏤӛᏢಞǵӝբᏢಞ!. !. !. i.

(4) Abstract The concept of e-learning gradually emerges with the development of information technology. In the 21st century when knowledge economy is emphasized, the cultivation of self-directed learning and problem-solving ability becomes more important. Learners with problem-based cooperative learning through networks can more conveniently cultivate the problem-solving ability with self-directed learning. Nonetheless, learners would receive large amount of peer information during the network cooperative learning interaction; some learners therefore could not effectively select, judge, analyze, and integrated the acquired information by judging the accuracy of information to further observe the opinions of peers or opinion leaders. For this reason, learners’ social network interaction data generated in the problem-based network cooperative learning process are proceeded community mining by combining quality function Q and genetic algorithm, and PageRank algorithm is applied to search. ҳ. ࢇ ‫ ݽ‬σ. for the opinion leader in each community in order to discuss the effects of teachers. Ȇ. Ȇ ୽. Ᏸ. directly proceeding first-order information communication model and second-order information communication model through community opinion leaders on learners’ learning outcome, social network interaction, and group cohesiveness. Furthermore, the effects of such two information communication models on learning outcome, social network interaction, and group cohesiveness of learners with different genders and. ź. ŏŢŵ. ŴŪŵ. personality traits are also investigated.. ů. Ţŭ. Ŧų. ŪŰ. The research findings show (1) learners in the experimental group with secondorder information communication model by distributing information to opinion leaders, under the problem-based network cooperative learning environment, significantly outperform learners in the control group with first-order communication model through network announcement on the learning outcome; (2) female learners in the experimental group with second-order information communication model by distributing information to opinion leaders, under the problem-based network. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ŷ. cooperative learning environment, present remarkably better learning outcome than female learners in the control group with first-order information communication model through network announcement, while no significant difference appears between male learners in both groups; (3) learners in the experimental group with second-order information communication model by distributing information to opinion leaders, under the problem-based network cooperative learning, notably show better peer interaction effectiveness than learners in the control group with first-order communication model through network announcement; and (4) combining quality function Q with genetic algorithm to detect community and applying PageRank to ii.

(5) search for community opinion leaders could accurately assist teachers in finding out the problem-based network cooperative learning community opinion leaders. Finally, suggestions for teaching practice and future research, according to the research results, are proposed in this study for successive research.. Key words: community mining, social network analysis, problem-based learning, cooperative learning. ҳ. ࢇ ‫ ݽ‬σ. Ȇ. Ȇ ୽. Ᏸ. ů. Ŧų. ŪŰ. ŴŪŵ. ź. ŏŢŵ. Ţŭ. ġń. ũŦůŨŤũŪġ. ! !. iii. Ū ů Ŗ. ŷ.

(6) Ҟԛ! ᄔा.................................................................................................................................i! Bctusbdu........................................................................................................................ii! Ҟԛ...............................................................................................................................iv! კԛ...............................................................................................................................vi! ߄ԛ..............................................................................................................................vii! ಃ΋ക!ᆣፕ.................................................................................................................. 1! !. ಃ΋࿯!ࣴ‫ز‬ङඳᆶ୏ᐒ .............................................................................................1!. !. ಃΒ࿯!ࣴ‫ز‬Ҟ‫ ޑ‬.........................................................................................................4!. !. ಃΟ࿯!ࣴ‫ز‬ୢᚒ .........................................................................................................5!. !. ಃѤ࿯!ࣴ‫ز‬ጄൎᆶज़‫ ڋ‬.............................................................................................5!. !. ಃϖ࿯!Ӝຒှញ .........................................................................................................6!. ҳ. ࢇ ‫ ݽ‬σ. Ȇ ୽. Ᏸ. ಃΒക!Ў᝘௖૸.......................................................................................................... 8!. Ȇ. !. ಃ΋࿯!ୢᚒᏤӛᏢಞ .................................................................................................8! ಃΒ࿯!‫཮ޗ‬ᆛၡϩ‫݋‬ᆶ‫ޗ‬ဂ௖୎ ...........................................................................11!. !. ಃΟ࿯!Βભၗૻ໺ኞ౛ፕ .......................................................................................14!. !. ಃѤ࿯!იᡏਏૈᆶიᡏᏉᆫΚ ...............................................................................18!. Ţŭ. Ŧų. ŪŰ. ŴŪŵ. ź. ŏŢŵ. !. ŷ. ů. ಃΟക!ࣴ‫ز‬೛ीᆶჴࡼ............................................................................................ 21! !. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ಃ΋࿯!ࣴ‫ࢎز‬ᄬ .......................................................................................................21!. !. ಃΒ࿯!ࣴ‫ز‬Б‫ ݤ‬.......................................................................................................24!. !. ಃΟ࿯!ࣴ‫ز‬ჹຝ .......................................................................................................25!. !. ಃѤ࿯!ჴᡍ೛ी .......................................................................................................26!. !. ಃϖ࿯!ࣴ‫ز‬π‫ ڀ‬.......................................................................................................27! ! ಃϤ࿯!ၗ਑ೀ౛ᆶϩ‫ ݋‬...........................................................................................35! ಃΎ࿯!ࣴ‫؁ز‬ᡯ .......................................................................................................36!. !. ಃѤക!ჴᡍ่݀ϩ‫݋‬................................................................................................ 38! !. ಃ΋࿯!ࣴ‫ز‬ჹຝ୷ҁၗ਑ .......................................................................................38!. !. ಃΒ࿯!‫ޗ‬ဂϷཀ‫ـ‬ሦ೑ୀෳԋਏ ...........................................................................38!. !. ಃΟ࿯!‫ٿ‬ಔᏢಞ‫ޣ‬Ꮲಞԋਏϩ‫ ݋‬...........................................................................39!. !. ಃѤ࿯!‫ٿ‬ಔᏢಞ‫཮ޗޣ‬ᆛၡϕ୏ϩ‫ ݋‬...................................................................45! iv.

(7) ಃϖ࿯!‫ٿ‬ಔᏢಞ‫ޣ‬იᡏᏉᆫΚৡ౦ϩ‫ ݋‬...............................................................50!. !. ! ಃϤ࿯!ೖፋၗ਑ϩ‫ ݋‬...............................................................................................52! ಃΎ࿯!ჴᡍ่݀༼᏾ᆶ૸ፕ ...................................................................................55!. !. ಃϖക!่ፕᆶࡌ᝼.................................................................................................... 58! !. ಃ΋࿯!่ፕ...............................................................................................................58!. !. ಃΒ࿯!௲Ꮲჴࡼࡌ᝼ ...............................................................................................59!. !. ಃΟ࿯!҂ٰࣴ‫ز‬Бӛ ...............................................................................................61!. ୖԵЎ᝘...................................................................................................................... 62!! ߕᒵ΋!იᡏᏉᆫΚໆ߄............................................................................................ 69! ߕᒵΒ!Big-Five Mini-Markers ໆ߄ ......................................................................... 70!. ‫ݽ‬ ࢇ ߕᒵΟ!ೖፋεᆜ........................................................................................................ 72! σ ҳ Ȇ. Ȇ ୽. Ᏸ. ů. Ŧų. ŪŰ. ŴŪŵ. ź. ŏŢŵ. Ţŭ. ġń. ũŦůŨŤũŪġ. v. Ū ů Ŗ. ŷ.

(8) კԛ! კ 3.1!ࣴ‫ࢎز‬ᄬკ..................................................................................................... 25! კ 3.2!ୢᚒᏤӛᆛၡӝբᏢಞѳѠᆛઠ२।......................................................... 31! კ 3.3!CPBL ᆛઠ२।фૈ...................................................................................... 32! კ 3.4 Ꮲಞ‫ޣ‬фૈϟय़.............................................................................................. 33! კ 3.5 ΋ঁӳ‫ޑ‬ဂ...................................................................................................... 34! კ 3.6 ᔈҔࠔ፦ Q ‫ڄ‬ኧᆶ୷Ӣᄽᆉ‫ݤ‬຾Չ‫ޗ‬ဂୀෳၮᆉࡕ‫ ่݀ޑ‬..................... 35! კ 3.7 ࣴ‫ز‬ჴࡼำ‫ׇ‬ᆶࢬำ...................................................................................... 40! კ 4.1 ӝբᏢಞ‫ޗ‬ဂϐϕ୏ᜢ߯ંତ...................................................................... 45! კ 4.2 ௦Ҕόӕၗૻ໺ኞኳԄಔձᏢಞ‫ޣ‬ӧୢᚒᏤӛᆛၡӝբᏢಞѳѠ‫ޑ‬ϕ୏ ᜢ߯.............................................................................................................................. 48! კ 4.3 ჴᡍಔӚ‫ޗ‬ဂϐ‫཮ޗ‬ᆛၡ่ᄬᜢ߯.............................................................. 49! ! !. ҳ. ࢇ ‫ ݽ‬σ. Ȇ. Ȇ ୽. Ᏸ. !. ů. Ŧų. ŪŰ. ŴŪŵ. ź. ŏŢŵ. Ţŭ. ġń. ũŦůŨŤũŪġ. vi. Ū ů Ŗ. ŷ.

(9) ߄ԛ! ߄ 3.1 ୢᚒᏤӛᏢಞӚ໘ࢤϐჴᡍ೛ी.................................................................. 29! ߄ 4.1 ࣴ‫ز‬ჹຝΓኧ಍ी߄...................................................................................... 38! ߄ 4.2 ௦Ҕόӕၗૻ໺ኞ฼ౣჴᡍಔᆶ௓‫ڋ‬ಔᏢಞ‫ޣ‬ϐ߃‫ۈ‬Ӄഢ‫ޕ‬᛽ᐱҥኬҁ t ᔠ‫่݀ۓ‬...................................................................................................................... 40! ߄ 4.3 ჴᡍಔᆶ௓‫ڋ‬ಔ‫ܭ‬Ѥ໘ࢤୢᚒᏤӛᏢಞϐӚ໘ࢤ೯ၸΓኧᆶ೯ၸ౗...... 41! ߄ 4.4 ჴᡍಔᆶ௓‫ڋ‬ಔӚ໘ࢤԋᕮϐᐱҥኬҁ t ᔠ‫ ่݀ۓ‬.................................. 41! ߄ 4.5 ‫ٿ‬ಔಔϣόӕ‫܄‬ձᏢಞ‫ޣ‬ϐᏢಞԋਏᐱҥኬҁ t ᔠ‫ ่݀ۓ‬...................... 42! ߄ 4.6 ‫ٿ‬ಔಔ໔όӕ‫܄‬ձᏢಞ‫ޣ‬Ꮲಞԋਏϐᐱҥኬҁ t ᔠ‫ ่݀ۓ‬...................... 42! ߄ 4.7 ჴᡍಔѤঁ‫ޗ‬ဂ௶ॊ಍ी.............................................................................. 43! ߄ 4.8 ჴᡍಔѤঁ‫ޗ‬ဂᏢಞԋਏϐ ANOVA ϩ‫݋‬.................................................... 43! ߄ 4.9 ჴᡍಔᆶ௓‫ڋ‬ಔᏢಞ‫ޣ‬Γ਱੝፦಍ी߄...................................................... 43! ߄ 4.10 ჴᡍಔϷ௓‫ڋ‬ಔ໒‫܄ܫ‬Γ਱᏾ᡏᏢಞԋਏϐᐱҥኬҁ t ᔠ‫ ่݀ۓ‬........ 44! ߄ 4.11 ‫ٿ‬ಔಔϣόӕ‫܄‬ձᏢಞ‫ޣ‬Ꮲಞԋਏϐᐱҥኬҁ t ᔠ‫ ่݀ۓ‬.................... 44! ߄ 4.12 ‫ٿ‬ಔಔ໔όӕ‫܄‬ձᏢಞ‫ޣ‬Ꮲಞԋਏϐᐱҥኬҁ t ᔠ‫ ่݀ۓ‬.................... 44! ߄ 4.13 ௦Ҕόӕၗૻ໺ኞኳԄ‫ٿ‬ಔᏢಞ‫ޣ‬ϐ᏾ᡏ‫཮ޗ‬ᆛၡৡ౦ϩ‫่݀݋‬........ 46!. ҳ. ࢇ ‫ ݽ‬σ. Ȇ ୽. Ᏸ. Ȇ. ߄ 4.14 ჴᡍಔᆶ௓‫ڋ‬ಔიᡏᏉᆫΚ߻ෳϷࡕෳϩኧϐᐱҥኬҁ t ᔠ‫ ۓ‬............ 50! ߄ 4.15 ჴᡍಔᆶ௓‫ڋ‬ಔიᡏᏉᆫΚ߻ෳϷࡕෳϩኧϐԋჹኬҁ t ᔠ‫ ۓ‬............ 50! ߄ 4.16 ‫ٿ‬ಔᏢಞ‫ޣ‬იᡏᏉᆫΚ߻ࡕෳϐ௶ॊ಍ी................................................ 51! ߄ 4.17 ‫ٿ‬ಔᏢಞ‫ޣ‬იᡏᏉᆫΚ߻ࡕෳϐӅᡂኧӕ፦‫܄‬ᔠ‫ۓ‬ᄔा߄.................... 51! ߄ 4.18 ‫ٿ‬ಔᏢಞ‫ޣ‬იᡏᏉᆫΚ߻ࡕෳϐൂӢηӅᡂኧϩ‫݋‬ᄔा߄.................... 51!. ŴŪŵ. ź. ŏŢŵ. ů. Ţŭ. Ŧų. ŪŰ. ߄ 4.19 ჴᡍ่݀᏾౛߄਱........................................................................................ 57! ! !. ġń. ũŦůŨŤũŪġ. vii. Ū ů Ŗ. ŷ.

(10) ಃ΋ക!!ᆣፕ! ҁകЬाᇥܴҁࣴ‫ز‬ϐࣴ‫ز‬ङඳᆶ୏ᐒǵࣴ‫ز‬Ҟ‫ޑ‬ǵࣴ‫ز‬ୢᚒаϷࣴ‫ز‬ጄ ൎᆶज़‫ڋ‬Ǵനࡕଞჹҁࣴ‫࣬ز‬ᜢϐ஑ԖӜຒ຾Չࣚ‫ۓ‬ᆶှញǶ!. ಃ΋࿯!!ࣴ‫ز‬ङඳᆶ୏ᐒ! ᒿ๱‫ޑ཮ޗ‬ᡂᎂаϷၗૻࣽ‫ޑמ‬ว৖ǴΓॺ‫ૻޑ‬৲໺ሀᆶྎ೯ኳԄౢғΑ ‫ׯ‬ᡂǴவ໺಍य़ჹय़‫ૻޑ‬৲໺ሀᆶྎ೯΋‫ޔ‬ᄽᡂ‫ډ‬౜Ϟ೸ၸᆛၡ‫ޗޣ܈‬ဂᆛઠ ຾Չૻ৲໺ሀᆶྎ೯Ƕ੝ձࢂ߈ԃٰᒿ๱ Web2.0 ᆛၡπ‫ޑڀ‬ว৖ᆶ౰ՉǴΓሞ. ࢇ ‫ ݽ‬σ. ϐ໔‫ޑ‬ϕ୏ᜢ߯໒‫ډڙۈ‬ख़ຎǴςԖ೽ϩᆛઠ೛ीԖճ‫ܭ‬٬Ҕ‫ޣ‬۶Ԝϕ୏‫ޑ‬ᐒ. ҳ. ‫ڋ‬Ǵаߦ٬‫׳‬ӭ٬Ҕ‫ୖޣ‬ᆶᆛၡࢲ୏)஭◖࣓Ǵ2012)Ƕ‫཮ޗ‬ᆛၡ(social networks). Ȇ ୽. Ᏸ. ࢂҗ΋ဂΓǵಔᙃ‫཮ޗ܈‬იᡏᙖҗ϶ፉǵӝբᜢ߯‫܈‬੝‫ۓ‬Ҟ‫ޑ‬฻ϕ୏ೱ่ᜢ߯ ಔԋ(Wasserman & Faust,1994)ǶԶ‫཮ޗ‬ᆛၡϩ‫ޑ݋‬ขᗺӧ‫ܭ‬௖૸Γǵಔᙃǵ‫ރ‬. Ȇ. ᄊ‫ঁ཮ޗ܈‬ᡏύ‫ޑ‬ᜢᖄ‫܄‬ኬԄ(Jamali & Abolhassani,2006)ǴЬाҞ‫ޑ‬ӧ‫ܭ‬வ‫ޗ‬. ŏŢŵ. ŴŪŵ. ź. ཮ᆛၡ่ᄬ(structure)ύࡩ௚рҞ኱໔‫ޑ‬ᜢ߯ǴԶ೭٤ᜢ߯ၗ਑ёа೸ၸႝတ‫מ‬. Ŧų. ŪŰ. ೌϩ‫཮ޗ݋‬ᆛၡϕ୏‫ݩރ‬Ƕ!. Ţŭ. ů. ŷ Ū ġ ů ԜѦǴӢࣁႝတ‫ޑ‬දϷаϷᆛሞᆛၡ‫ޑ‬ว৖Ǵ߈൳ԃٰӧᏢಞኳԄ΢ΨԖ ńũ ŦůŨŤũŪġŖ. ೚ӭ‫ޑ‬ബཥᡂॠǴኧՏᏢಞ‫ޑ‬ᢀ‫ۺ‬೴ᅌᑫଆǴӵՖஒၗૻࣽ‫מ‬ᑼΕ‫ډ‬௲Ꮲᕉ࿯ ύᇶշᏢಞ‫ޣ‬Ǵςԋࣁ౜ӧ዗ߐ‫زࣴޑ‬᝼ᚒǶ໺಍௲ᏢςԖ೚ӭ௲ৣ௦Ҕӝբ Ꮲ ಞ ‫ ܭ‬ፐ ୸ ௲ Ꮲ ΢ Ǵ ߈ ԃ ٰ ߾ ่ ӝ ၗ ૻ ᆶ ೯ ૻ ࣽ ‫(מ‬information and communication technology, ICT)ว৖ࣁႝတЍජӝբᏢಞ‫ޑ‬ኳԄǴᏇఈ೸ၸႝတ ᆶᆛၡࣽ‫ޑמ‬൞ӝǴว৖ᓬ‫੿ܭ‬ჴᏢಞᕉნύय़ჹय़ϕ୏຾ՉӝբᏢಞ‫ޑ‬ᆛၡ ӝբᏢಞኳԄǶߠࡹֻǵஞფ๩)2013*‫ࡰزࣴޑ‬рǴᏢғ೸ၸᆛၡ຾ՉୢᚒᏤ ӛӝբᏢಞ(problem-based learning, PBL)‫ޑ‬ᇡ‫߄ޕ‬౜ᓬ‫ܭ‬໺಍य़ჹय़຾Չӝբୢ ᚒᏤӛᏢಞ‫ޑ‬ᏢғǴᡉҢճҔႝတЍජӝբᏢಞ‫ڀ‬Ԗว৖ወΚǴॶள௢৖ǶЬ ा‫ޑ‬চӢࣁᆛሞᆛၡК໺಍य़ჹय़ϕ୏‫׳‬Ԗճ‫ܭ‬໺ኞૻ৲‫ޑ‬ջਔ੝‫܄‬Ǵૈ୼ቚ 1.

(11) ຾Ꮲಞ‫ޣ‬۶Ԝϐ໔‫ޑ‬ϕ୏Ǵ຾Զߦ຾‫׳‬Ԗਏ‫ޑ‬ӝբᏢಞǶӧΒΜ΋Шइமፓ‫ޕ‬ ᛽࿶ᔮ‫ޑ‬ϞϺǴԾЬᏢಞϷୢᚒှ،БԄ‫׳‬ᡉख़ाǴӢԜᔈவλ୻Ꭶୢᚒှ، БԄǴԶᙖҗᆛၡ຾ՉୢᚒᏤӛӝբᏢಞǴᏢಞ‫ޣ‬ё‫׳‬Бߡ‫ޑ‬೸ၸԾЬᏢಞှ ،ୢᚒǶ! ฅԶ‫؂‬ՏᏢಞ‫ૈޣ‬Κό΋ǴԶᏢಞ‫ޣ‬ӧ຾ՉᆛၡӝբᏢಞ‫ޑ‬ϕ୏ය໔཮ௗ ԏ‫ډ‬εໆٰԾӕᏆ‫ޑ‬ၗૻǴԖ٤Ꮲಞ‫ޣ‬த཮Ӣࣁค‫ݤ‬ղᘐၗૻ‫҅ޑ‬ዴ‫܄‬ǴԶค ‫ݤ‬Ԗਏᒧ᏷ǵղᘐǵϩ‫݋‬ᆶ᏾ӝ‫܌‬ᕇள‫ޑ‬ၗૻǶ߮୯Ꮲ‫ ޣ‬Vygotsky(1978)ᇡࣁᏢ ಞ‫ޑ‬ว৖ቫԛϩࣁჴሞว৖ቫԛᆶወӧว৖ቫԛǴჴሞว৖ቫԛࣁӧό࿶җ‫ڐ‬. ࢇ ‫ ݽ‬σ. շΠૈᐱԾֹԋҺ୍‫ૈޑ‬ΚǴԶወӧว৖ቫԛ߾ࢂӧӝբ‫ڐ܈‬շϐΠǴૈ୼ှ. ҳ. ،ୢᚒ‫ૈޑ‬ΚǴ‫ޣٿ‬໔‫ޑ‬ৡຯ Vygotsky ᆀࣁȨ߈ୁว৖୔໔ȩ(the Zone of. Ȇ ୽. Ᏸ. Proximal DevelopmentǴZPD)ǶऩᏢಞ‫ޣ‬໔ ZPD ৡ౦ၸεǴӧ૸ፕྎ೯΢཮೷ ԋష໶ǴёૈᏤठӝբᏢಞਏ݀ό٫ǶӢԜǴᏢಞ‫ޣ‬த཮൨‫؃‬ӕᏆ‫܈‬ᢀఈཀ‫ـ‬. Ȇ. ሦ೑(Opinion Leader)‫ޑ‬ཀ‫ـ‬Ƕ1940 ԃж໺ኞᏢӃ០‫ ޣ‬Lazarsfeld ฻Γ(1944)ӧ΋. ŏŢŵ. ŴŪŵ. ź. ໨ऍ୯ᕴ಍ᒧᖐ‫زࣴޑ‬ύว౜Ǵཀ‫ـ‬ሦ೑Кε౲໺ኞჹᒧ҇‫׫‬౻ᄊࡋ‫ޑ‬ቹៜ‫׳‬. ů. Ţŭ. Ŧų. ŪŰ. εǴӢԜගрΑΒભ໺ኞ౛ፕ(Two-step flow of communication)ǶԶࡕ Katz ᆶ. ŷ. Lazarsfeld ӧ 1955 ԃΞ‫׳‬຾΋‫؁‬ӧಂьკࣴ‫(ز‬Decatur Study)ύаѱ൑੃຤ǵਔ. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. းǵϦӅ٣୍ǵႝቹ฻ࣁჹຝǴϩ‫݋‬җཀ‫ـ‬ሦ೑ౢғၗૻ‫ޑ‬ቹៜਏ݀(Katz & Lazarsfeld,1955)Ǵ่݀ᡉҢӧѱ൑੃຤ǵႝቹϷਔး฻ғࢲ᝼ᚒ΢Ǵཀ‫ـ‬ሦ೑ ‫ޑ‬ቹៜࣗ‫ܭ‬ε౲໺ኞǶவа΢ࣴ‫ز‬ё‫ޕ‬Ǵཀ‫ـ‬ሦ೑‫܌‬ගр‫ޑ‬ཀ‫ـ‬ӧიᡏύ‫ޑ‬ዴ ၨ৒ܰ೏௢஖٠ௗ‫ڙ‬ǴΨӢԜჹ‫ܭ‬ၗૻ‫ޑ‬໺ኞ೷ԋᜢᗖ‫ޑ܄‬ቹៜǶ! ᆕӝа΢‫܌‬ॊǴᆛၡӝբᏢಞό໻ࢂᖿ༈Ǵ٠Ъ‫ڀ‬Ԗຬຫਔ໔ϷӦᗺ‫ߡޑ‬ ճ‫܄‬ǵԖշ‫ܭ‬ቚ຾ӕᏆ໔ϕ୏ǵቚуࡘԵᐒ཮ǵቚуᏢಞ୏ᐒǵ୓Ӝ‫܄‬ǵၗ਑ ܰ‫ߥܭ‬ӸϷ٬Ҕ฻ᓬ‫ܭ‬໺಍य़ჹय़ӝբᏢಞ‫ޑ‬ᓬᗺ)৊ࡾӧǵ࣑݅ӵǴ2007ǹ Swigger & BrazileǴ1997ǹJohnson & JohnsonǴ1985ǹJohnson & JohnsonǴ1986)Ǵ ԶၮҔΒભ໺ኞ౛ፕǴ‫פ‬рᆛၡӝբᏢಞύཀ‫ـ‬ሦ೑‫܌‬຾Չ‫ޑ‬ၗૻ໺ኞǴࢂց 2.

(12) Ԗշ‫ܭ‬ගϲᆛၡӝբᏢಞԋਏǴЇวҁࣴ‫୏زࣴޑز‬ᐒǶӢԜǴҁࣴ‫ز‬ճҔᏢ ಞ‫ޣ‬ӧୢᚒᏤӛᆛၡӝբᏢಞᐕำύ‫܌‬ౢғ‫཮ޗޑ‬ᆛၡϕ୏ၗ਑Ǵ຾Չ‫ޗ‬ဂ௖ ୎(community detection)Ǵ‫פ‬рব٤Ꮲಞ‫ޣ‬ឦ‫ܭ‬ϕ୏ၨࣁᆙஏ‫ޗޑ‬ဂ‫سࢴ܈‬Ǵӆ ೸ၸ PageRank ϩኧ‫פ‬р೭ঁ‫ޗ‬ဂύቹៜΚനε‫ޑ‬ཀ‫ـ‬ሦ೑Ǵ຾Զ჋၂๱೸ၸཀ ‫ـ‬ሦ೑ٰ໺ሀၗૻቹៜ‫ځ‬дᏢಞ‫ޑޣ‬БԄǴ‫׆‬ఈૈԖਏගϲᆛၡӝբᏢಞԋਏǶ! ! ! ! ! !. ҳ. !. !. Ȇ ୽. ź. ŴŪŵ. Ţŭ. ů. !. ŪŰ. !. Ŧų. !. ŏŢŵ. !. Ȇ. !. Ᏸ. !. ࢇ ‫ ݽ‬σ. ġń. ũŦůŨŤũŪġ. ! ! ! ! ! ! ! ! 3. Ū ů Ŗ. ŷ.

(13) ಃΒ࿯!!ࣴ‫ز‬Ҟ‫!ޑ‬ !. ҁࣴ‫ز‬ԑӧ௖૸ӧୢᚒᏤӛᆛၡӝբᏢಞᕉნΠǴ೸ၸ‫ޗ‬ဂ௖୎Ϸࠔ፦. R ‫ڄ‬ኧ‫פ‬рឦ‫ܭ‬ӕ΋‫ޗ‬ဂԋ঩ǴаϷ QbhfSbol ‫פ‬൨၀‫ޗ‬ဂന‫ڀ‬ԖቹៜΚ‫ޑ‬ཀ‫ـ‬ ሦ೑Ǵ٠ᏵԜКၨ໺಍‫ޔ‬ௗ໺ኞૻ৲๏‫܌‬ԖᏢಞ‫ޑޣ‬΋ભ໺ኞኳԄǴаϷ೸ၸ วթૻ৲๏ཀ‫ـ‬ሦ೑ϐΒભၗૻ໺ኞኳԄǴࢂցӧୢᚒᏤӛᆛၡӝբ௃ნΠ‫ځ‬ Ꮲಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ቫय़ǵ ǵიᡏᏉᆫΚ΢‫ڀ‬Ԗᡉ๱ৡ౦ǶᏵԜǴҁࣴ‫ޑز‬ ࣴ‫ز‬Ҟ‫ޑ‬ӵΠǺ! ΋ǵҁࣴ‫ޑز‬Ҟ‫ޑ‬ӧ‫ܭ‬ၮҔΒભၗૻ໺ኞᔈҔ‫ܭ‬ୢᚒᏤӛᏢಞύǴࢂցё‫ׯ‬๓. ࢇ ‫ ݽ‬σ ϕ୏ǵიᡏᏉᆫΚԖਏගϲǶ! ҳ. ΋ભၗૻ໺ኞ‫ޑ‬ၗૻ໺ሀਏ౗եပୢᚒǴ٬Ꮲಞ‫ޑޣ‬Ꮲಞԋਏǵ‫཮ޗ‬ᆛၡ. Ȇ ୽. Ᏸ. Βǵҁࣴ‫ز‬ஒ຾΋‫؁‬ϩ‫݋‬όӕ‫܄‬ձǵΓ਱੝፦฻Ꮲಞ‫ޣ‬ӧόӕၗૻ໺ኞኳԄΠ ჹᏢಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ǵიᡏᏉᆫΚᡂ໨‫ޑ‬ቹៜǶ!. Ȇ. Οǵҁࣴ‫ز‬ճҔ‫཮ޗ‬ᆛၡϩ‫פ݋‬൨ӝբიᡏύሦᏤ‫ޣ‬Ǵ٠೸ၸሦᏤ‫ޣ‬໺ኞቹៜ. !. ź. ŴŪŵ Ŧų. Ţŭ. ů. !. ŪŰ. !. ŏŢŵ. ӕᏆǴ‫ׯ‬๓௲ৣ௲ᏢኳԄᆶਏૈǶ!. ġń. ũŦůŨŤũŪġ. ! ! ! ! ! ! ! !. 4. Ū ů Ŗ. ŷ.

(14) ಃΟ࿯!!ࣴ‫ز‬ୢᚒ! !. ୷‫ܭ‬΢ॊࣴ‫ز‬Ҟ‫ޑ‬Ǵҁࣴ‫ز‬ϐࣴ‫ز‬ୢᚒӵΠǺ!. ΋ǵӧୢᚒᏤӛᆛၡӝբᏢಞᕉნΠǴ௦ҔΒભ໺ኞኳԄᆶ΋ભ໺ኞኳԄ‫ٿޑ‬ ಔᏢಞ‫ޣ‬ǴӧᏢಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ǵიᡏᏉᆫΚ΢ࢂց‫ڀ‬Ԗᡉ๱ৡ ౦ǻ! ΒǵӧୢᚒᏤӛᆛၡӝբᏢಞᕉნΠǴ௦ҔΒભ໺ኞኳԄᆶ΋ભ໺ኞኳԄ‫ٿޑ‬ ಔόӕ‫܄‬ձᏢಞ‫ޣ‬ǴӧᏢಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ǵიᡏᏉᆫΚ΢ࢂց‫ڀ‬Ԗ ᡉ๱ৡ౦ǻ!. ࢇ ‫ ݽ‬σ ಔόӕΓ਱੝፦Ꮲಞ‫ޣ‬ǴӧᏢಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ǵიᡏᏉᆫΚ΢ࢂց ҳ. ΟǵӧୢᚒᏤӛᆛၡӝբᏢಞᕉნΠǴ௦ҔΒભ໺ኞኳԄᆶ΋ભ໺ኞኳԄ‫ٿޑ‬. Ȇ. Ȇ ୽. !. Ᏸ. ‫ڀ‬Ԗᡉ๱ৡ౦ǻ!. ಃѤ࿯!!ࣴ‫ز‬ጄൎᆶज़‫!ڋ‬. ź. ŏŢŵ. ŴŪŵ. ҁࣴ‫ڙز‬ज़‫ܭ‬ਔ໔аϷࣴ‫ز‬ΓΚ฻ӢનǴࣚ‫زࣴۓ‬ጄൎᆶࣴ‫ز‬ज़‫ڋ‬ӵΠǺ!. ů. Ţŭ. Ŧų. ŪŰ. ΋ǵҁࣴ‫ز‬аࣁਲ༜ѱࢌ୯λϖԃભᏢғࣁࣴ‫ز‬ჹຝǴࣴ‫ࢂ่݀ز‬ցёа௢ፕ. ġń. Ū ů Ŗ. ŷ. Կ‫ځ‬дόӕԃសቫᏢಞ‫ޣ‬Ǵό‫ە‬բၸࡋ‫ޑ‬௢ፕǴሡा଺຾΋‫ޑ؁‬ᡍ᛾Ƕ!. ũŦůŨŤũŪġ. Βǵҁࣴ‫زࣴޑز‬ჹຝӧୢᚒᏤӛᆛၡӝբᏢಞѳѠ΢຾ՉᏢಞǴᏵԜ‫׎‬ԋ‫ޑ‬ Ꮲಞ‫ޗ‬ဂࣁ΋࠾ഈԄᏢಞ‫ޗ‬ဂǴࣴ‫ࢂ่݀ز‬ցૈ௢ፕԿ‫ځ‬дᆛၡӝբᏢಞ ᕉნ‫ځ܈‬д‫ޗ‬ဂǴό‫ە‬բၸࡋ௢ፕǶ! Οǵҁࣴ‫ز‬௲Ꮲჴᡍ‫܌‬೛ीϐୢᚒᏤӛӝբᏢಞҺ୍ࣁႝηၗྍճҔǴࣴ‫่ز‬ ݀ࢂցૈ௢ፕԿ‫ځ‬дୢᚒᏤӛᏢಞҺ୍Ǵό‫ە‬բၸࡋ௢ፕǶ! ! ! ! ! 5.

(15) ಃϖ࿯!!Ӝຒှញ! ΋ǵ ୢᚒᏤӛᏢಞ! ୢᚒᏤӛᏢಞ(Problem-Based Learning,ᙁᆀ PBL)ࢂаᏢғࣁύЈ‫ޑ‬௲ᏢБ ‫ݤ‬Ǵ௲ৣ‫فޑ‬Յࢂߦ຾ᏢғᏢಞǴԶόࢂࢌঁᏢࣽ஑ৎȐHmelo-Silver,2004; Savery & Duffy, 1995ȑ ǶԶၸѐࣴ‫ز‬Ψᇡࣁ PBL ૈᔅշᏢғࡌᄬଆቶ‫ݱ‬ǵԖቸ‫܄‬ ‫ޕޑ‬᛽୷ᘵǵว৖Ԗਏ౗‫ޑ‬ୢᚒှ،‫מ‬ѯǵว৖ԾЬᏢಞᆶಖ‫ي‬Ꮲಞ‫מޑ‬ѯǵ Ԗਏ౗‫ޑ‬ᆶдΓӝբǴаϷวԾϣЈ‫ޑ‬Ꮲಞ฻੝ᗺ(Barrows & Kelson,1995)Ƕ! ҁࣴ‫ز‬٬ҔϐୢᚒᏤӛᏢಞѳѠ߯җࡹ‫ݯ‬εᏢკਜၗૻᆶᔞਢᏢࣴ‫ޑ܌ز‬. ‫ݽ‬ ࢇ ኧՏკਜᓔᄤኧՏᏢಞჴᡍ࠻‫܌‬໒วǴ၀ѳѠ٬Ҕσ Chen Ϸ Chang(2014)ගрϐ ҳ Ȩ‫ޕ‬ȩ ǵȨՉ΋ȩǵȨՉΒȩϷȨࡘȩѤ໘ࢤӝբୢᚒှ،ᏢಞࢬำǴֹԋ௲ৣࡰ Ȇ ୽. Ᏸ. ‫ۓ‬ϐୢᚒᏤӛᏢಞҺ୍Ƕ௲Ꮲ‫ޣ‬೸ၸ೛ीѤ໘ࢤୢᚒှ،ᏢಞᡳࢎǴ٠ଏۚಃ. Ȇ. ΒጕᏼҺᏢಞၸำ‫ޑ‬ຑໆϷЇᏤ‫فޣ‬ՅǴճҔѤ໘ࢤ‫س‬಍ϯୢᚒှ،Ꮲಞ‫ޑ‬ᡳ ࢎϷำ‫ׇ‬ǴЇᏤᏢಞ‫ޣ‬ଞჹୢᚒ຾Չ૸ፕǵၗ਑ᇆ໣Ǵ຾Զှ،௲ৣ೛‫ۓ‬ሡֹ. ź. ŏŢŵ. ŪŰ. ŴŪŵ. ԋϐᏢಞҺ୍ǶӢࣁୢᚒᏤӛᏢಞ߯а໘ࢤ‫܄‬ၸᜢБԄ೴‫؁‬ှ،੿ჴ௃ნύ‫ޑ‬. ů. Ţŭ. Ŧų. ୢᚒǴࡺૈߦ٬Ꮲಞ‫ޣ‬຾ՉԾว‫ޑ܄‬ᏢಞǴΨӢமፓЬ୏ᑈཱུᏢಞǴӢԜࢂ΋. ġń. ᅿёаᐟᓰᏢಞ‫ޑޣ‬ԖਏᏢಞ฼ౣǶ! !. ũŦůŨŤũŪġ. Ū ů Ŗ. ŷ. ҁѳѠѤঁ໘ࢤࣣԖӚԖҺ୍Ǵಃ΋໘ࢤࣁ‫פ‬рΟঁ࣬ᜢႝηၗྍ٠຾. ՉᙁϟǴऩ໻‫פ‬р΋ঁၗྍࣁ 71 ϩǴ‫ ࣁঁٿ‬81 ϩǴΟঁࣁ 91 ϩǹಃΒ໘ࢤ ԿϿ໪ӈрΟঁન‫׷‬٠຾ՉᙁϟǴऩ໻ӈр΋ঁન‫ ࣁ׷‬71 ϩǴ‫ ࣁঁٿ‬81 ϩǴ Οঁࣁ 91 ϩǹಃΟ໘ࢤࣁᛤᇙЈඵკǴ௲ৣೕጄΟঁЬᚒѸ໪ᛤᇙǴऩ໻ฝ р΋ঁೕጄЬᚒࣁ 71 ϩǴ‫ ࣁঁٿ‬81 ϩǴΟঁࣁ 91 ϩǹಃѤ໘ࢤࣁдຑᆶԾ ຑǶӚ໘ࢤຎ‫ځ‬ϣ৒ᙦ൤ࡋϷኧໆଜໆу෧ϩኧǴϩኧ 71 ϩа΢ջ೯ၸ၀໘ ࢤǴ71 ϩаΠᏢಞ‫ޣ‬ሡख़ཥբเǶ! ! ! 6.

(16) Βǵ იᡏਏૈᆶიᡏᏉᆫΚ! !. !. იᡏਏૈ)Collective Efficacy*ࢂࡰიᡏԋ঩ჹ‫܌‬ឦიᡏय़ᖏ΋໨੝‫ۓ‬Һ. ୍ਔǴࢂցёаԋфӦֹԋӅӕҞ኱ϐӅ٦ߞ‫)ۺ‬Bandura,1997*ǹԶიᡏᏉᆫ Κ)Group Cohesiveness*߾ࢂიᡏ‫܌‬ౢғ‫ޑ‬΋ᅿค‫֎׎‬ЇΚǴаߦ٬ԋ঩ӧიᡏ ύ᝺ளԾӧ٠ԖᘜឦགǴӢԶౢғᇡӕགǴᡣԋ঩‫׳‬ᜫཀ੮ӧიᡏύ (Yalom,1995)Ƕҁࣴ‫܌ز‬٬Ҕ‫ޑ‬იᡏᏉᆫΚໆ߄ஒ‫ځ‬բࣁຑ՗௦Ҕ΋ભᆶΒભၗ ૻ໺ኞ฼ౣ‫ޑ‬ୢᚒᏤӛᆛၡӝբᏢಞλಔიᡏᏉᆫΚৡ౦ϐҔǶ! !. ࢇ ‫ ݽ‬σ Lazarsdeld ฻Γ‫ ܭ‬1940 ԃऍ୯ᕴ಍εᒧਔЬ࡭‫ޑ‬΋໨ࣴ‫ز‬ว౜Ǵჹࡹ‫ݯ‬ᑫ ҳ. Οǵ Βભ໺ኞ!. Ȇ ୽. Ᏸ. ፪ε‫ޑ‬ᒧ҇ॺ‫ݯࡹޑ‬໼ӛεӭ೿ςԖҥ൑ǴࡐϿ‫ڙ‬ε౲໺൞‫ޑ‬ቹៜǴՠჹࡹ‫ݯ‬ ᑫ፪λ‫ޑ‬ᒧ҇ॺϸԶ཮ܴᡉ‫ډڙ‬ཀ‫ـ‬ሦ೑(Opinion Leader)‫ޑ‬ቹៜ(Lazarsfeld et. Ȇ. al.,1944ǹશ‫ذ‬ฐǴ2008)Ƕдॺ‫ۓزࣴޑ‬ကၗૻӃவཀ‫ـ‬ሦ೑ӆ໺‫཮ޗډ‬ε౲‫ޑ‬. ŴŪŵ. ź. ŏŢŵ. ኳԄࣁΒભ໺ኞ)Two-step flow of Communication*Ƕ٠Ъ೸ၸཀ‫ـ‬ሦ೑໺ኞ‫ޑ‬Β. ŪŰ. ᄊࡋǶ!. Ţŭ. Ŧų. ભ໺ኞǴ೏ᇡࣁК೸ၸ‫ޔ‬ௗаε౲໺൞຾Չ‫ޑ‬΋ભ໺ኞǴ‫׳‬ёૈ‫ׯ‬ᡂ᎙᠋‫ޑޣ‬. ů. Ūŷ ů ӧୢᚒᏤӛӝբᏢಞ௃ნΠǴҁࣴ‫ز‬టճҔ‫ޗ‬ဂୀෳаϷ‫཮ޗ‬ᆛၡϩ‫מ݋‬ ũŦůŨŤũŪġŖ. ġń. ೌǴ‫פ‬р‫ޗ‬ဂύᜢᗖ‫ޑ‬ཀ‫ـ‬ሦ೑ٰ຾ՉୢᚒᏤӛᏢಞҺ୍ᆶֹԋБ‫ݤ‬฻௲Ꮲၗ ૻ‫ޑ‬Βભ໺ኞǴ٠௖૸Βભ໺ኞࢂցၨ΋ભ໺ኞ‫׳‬Ԗշ‫ܭ‬ගϲᏢಞ‫ޑޣ‬ୢᚒᏤ ӛᆛၡӝբᏢಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏аϷიᡏᏉᆫΚǶ!. 7.

(17) ಃΒക!Ў᝘௖૸! !. ҁകଞჹᆶҁࣴ‫࣬ز‬ᜢϐЎ᝘຾Չӣ៝ᆶ᏾౛Ǵಃ΋࿯௖૸ୢᚒᏤӛᏢ. ಞ‫ޑ‬ኳԄᆶ࣬ᜢࣴ‫ز‬ǹಃΒ࿯௖૸‫཮ޗ‬ᆛၡϩ‫ޗܭ݋‬ဂ௖୎ϐБ‫ݤ‬ϷᔈҔӧᆛ ၡᏢಞϐ࣬ᜢࣴ‫ز‬ǹಃΟ࿯௖૸Βભၗૻ໺ኞ౛ፕϐϣ఼Ϸ࣬ᜢࣴ‫ز‬ǹಃѤ࿯ ௖૸იᡏਏૈᆶიᡏᏉᆫΚཀကᆶ࣬ᜢЎ᝘Ƕ!!. ಃ΋࿯!ୢᚒᏤӛᏢಞ! ΋ǵୢ ୢᚒᏤӛᏢಞ‫ۓޑ‬ကᆶว৖!. ࢇ ‫ ݽ‬σ. ୢᚒᏤӛᏢಞ)Problem-based learning-ᙁᆀ PBL*ࢂ߈жଯ฻௲‫ػ‬Ꮲಞ‫ڂޑ‬. ҳ. ጄϷዊࢬǴྍ‫ ܭ‬1920 ԃж୘཰ᆅ౛‫ޑ‬λಔᏢಞ୻૽௲‫ػ‬౛‫ۺ‬Ǵ٠‫ ܭ‬1950 ԃж. Ȇ ୽. Ᏸ. ᖏ‫׉‬௲ਢаε୸௤ፐ‫׎ޑ‬Ԅр౜ӧᙴᏢ௲‫ػ‬Ǵ‫ ډޔ‬1960 ԃжύයωҗу৾εӼ εౣ࣪)Ontario*‫ ޑ‬McMaster εᏢᆕӝΑа΢‫ޑ‬Βᅿ‫׎‬Ԅᑼ‫ک‬ว৖рȨаᏢғࣁ. Ȇ. ύЈȩ ǵȨаୢᚒࣁ௲‫׷‬ȩǵ ȨаλಔࣁኳԄȩ ǴаϷȨа૸ፕࣁᏢಞȩ‫ޑ‬ୢᚒᏤӛ. ź. ŏŢŵ. ŴŪŵ. ᏢಞኳԄǴ٠Ъቶ‫ݱ‬೏ᔈҔ‫ܭ‬ᙴᏢ௲‫) !ػ‬ᜢຬฅǴ2009*ǶԾԜǴ೚ӭᙴᏢଣ೴. ů. Ţŭ. Ŧų. ŪŰ. ᅌճҔୢᚒᏤӛᏢಞᡣᏢғ຾ՉᏢಞǴّϞςࢬՉ‫ܭ‬ଯ฻௲‫کػ‬೚ӭᏢࣽሦୱ. ġń. ύ)Boud & Feletti, 1997;Duch et al., 2001*Ƕ!. ũŦůŨŤũŪġ. Ū ů Ŗ. ŷ. ᗨฅୢᚒᏤӛᏢಞӧ 1960 ԃжջ೏ගрٰǴՠ‫ ډޔ‬1980 ԃ Barrows! ᆶ Tamblyn ωܴዴ‫ۓ‬ကୢᚒᏤӛᏢಞࢂ΋ᅿᙖҗΑှᆶှ،ୢᚒၸำ‫ޑ‬ᏢಞБ‫ݤ‬Ǵ ӧᏢಞၸำύୢᚒࢂᏢғᏢಞ‫ޑ‬ख़Ј)Barrows & Tamblyn,1980*ǶԶࡕ Barrows ගрୢᚒᏤӛᏢಞ‫ڀ‬ԖϤε੝‫܄‬Ǻ΋ǵаᏢғࣁύЈ‫ޑ‬ᏢಞǺᏢғ‫ޑ‬Ꮲಞࢂࣁ ԾρॄೢǹΒǵλಔᏢಞБԄǺλಔԋ঩ኧҞεऊࢂѤԿΖΓǴᙖҗϕ୏Ꮲಞ ӵՖᆶдΓԖਏ‫ޑ‬πբǹΟǵ௲ৣࢂ‫ڐ‬շ‫܈ޣ‬ЇᏤ‫ޣ‬Ǻ௲ৣѝࢂЇᏤᏢғӵՖ ୢԾρୢᚒ)question*Ǵ٠ЪుΕΑှୢᚒ)problem)ǹѤǵୢᚒ‫׎‬ԋಔᙃขᗺ٠ ‫ڈ‬ᐟᏢಞǺаЎӷ‫ځ܈‬д൞ᡏ߄ၲБԄஒୢᚒև౜рٰǴԋࣁ಍᏾ӚᏢࣽ‫ޑ‬ข. 8.

(18) ᗺǴӕਔୢᚒΨࢂᇆ໣ཥૻ৲‫ޑ‬ጕ઩ǹϖǵୢᚒࢂୢᚒှ،‫מ‬ѯ‫ޑ‬ाનǺୢᚒ ाஒ੿ჴғࢲ‫ޑ‬ୢᚒዴჴᡉҢрٰǹϤǵᏢғаԾ‫ך‬ᏤӛᏢಞ‫ޑ‬БԄٰᕇளཥ ૻ৲ǺᏢғ΋ଆ૸ፕǵϩ‫݋‬ᆶ៏ፕᏢಞϣ৒)Barrows,1996*ǶDelisle! )1997*ᇡ ࣁୢᚒᏤӛᏢಞࢂஒᏢғ‫ܫ‬Ε΋ঁ௲ৣ೛ी‫ޑ‬Ꮲಞ௃ნϐύǴҔୢᚒٰЇᏤᏢ ғှ،ୢᚒ‫ޑ‬Б‫ݤ‬ǴԶόࢂଓ‫؃‬΋ঁ҅ዴှเǴᏢғё೸ၸᏢಞ‫ޑ‬ၸำࡌҥࡘ Եှ،ୢᚒ‫ૈޑ‬ΚǶ݅᜽ী)2002*ᇡࣁୢᚒᏤӛᏢಞமፓი໗ӝբᆒઓǴ೸ၸ λಔ૸ፕёаϸࢀόӕᏢಞࠠᄊǴ‫׳‬ёҬඤཀ‫ـ‬Ǵᡍ᛾Ծρჹୢᚒ‫ޑ‬ᕕှǶ! ᆕӝа΢Ꮲ‫ݤ࣮ޑޣ‬ǴୢᚒᏤӛᏢಞࢂаᏢಞ‫ࣁޣ‬ύЈ‫ޑ‬΋ᅿӝբԄᏢಞ. ࢇ ‫ ݽ‬σ. ฼ౣǴᏢғӧ௲ৣ೛ी‫ޑ‬Ꮲಞ௃ნύЬ୏‫ޑ‬ᏢಞϷှ،ୢᚒǴ٠Ь୏ୖᆶ૸. ҳ. ፕǴᙖҗ௲ৣவ਒‫ڐ‬շΠаᕇளཥ‫ޕ‬᛽Ǵՠനख़ा‫ࢂޑ‬ाЇᏤᏢғࡌҥࡘԵှ. Ȇ ୽. Ᏸ. ،ୢᚒ‫ૈޑ‬ΚǶ!. ǵႝတᇶշୢᚒᏤӛᆛၡӝբᏢಞ࣬ᜢࣴ‫!ز‬ Βǵ. Ȇ. ୢᚒᏤӛᏢಞԾ 1960 ԃжว৖аٰǴςԖόϿᏢࣽሦୱ௦Ҕ೭ᅿ௲ᏢБ. ź. ŏŢŵ. !. Ŧų. ŪŰ. ŴŪŵ. ‫ߦݤ‬຾Ꮲғ୻Ꭶୢᚒှ،ૈΚǶฅԶᒿ๱ਔж‫ޑ‬ᄽ຾Ǵᆛሞᆛၡ‫ޑ‬ᑫଆѺઇ໺ ಍௲‫ػ‬य़ჹय़‫ޑ‬ᏢಞኳԄǴ೸ၸᆛၡ຾Չ‫ޑ‬ᆛၡᏢಞࢲ୏Ǵёаँઇਔ໔ᆶ‫ޜ‬. Ţŭ. ů. ŷ Ū ġ ů ໔‫ޑ‬ज़‫ڋ‬Ǵ٠ว৖рᆛၡӝբᏢಞኳԄ)Chan et al., 1992*ǴӢԶୢᚒᏤӛᏢಞ ńũ ŦůŨŤũŪġŖ. Ψ‫ډڙ‬೭ިዊࢬ‫ޑ‬ቹៜǴԶ໒‫ۈ‬р౜ႝတЍජୢᚒᏤӛᆛၡӝբᏢಞ‫࣬ޑ‬ᜢࣴ ‫ز‬ǴԜᅿ೸ၸᆛၡ‫׎܌‬ԋ‫ޑ‬ᏢಞኳԄǴё຾Չၠ୔ǵၠЎϯ‫ޑ‬ջਔ‫ߚ܈‬ջਔ‫ޑ‬ϕ ୏Ǵ҅ӳߦ٬ ႝတЍජୢᚒᏤӛᆛၡӝբᏢಞኳԄ‫ޑ‬ว৖ ȐෞӀࡿǵጰᅽᑫǴ. 2001ȑǶ! !. Ҟ߻୯ϣѦςԖ೚ӭႝတЍජୢᚒᏤӛᏢಞ‫࣬ޑ‬ᜢࣴ‫ز‬ǴNaidu! ᆶ. Oliver)1996*ଞჹᙴៈ௲‫ػ‬೛ीΑаୢᚒᏤӛᏢಞࣁ௲Ꮲ฼ౣ‫ޑ‬ႝတᇶշፐำǴ ่݀ᡉҢᆶႝတᏢಞᕉნ่ӝϐୢᚒᏤӛᏢಞኳԄǴ‫ޑ‬ዴԖշ‫ܭ‬ቚ຾Ꮲಞ‫ޑޣ‬ ࡘԵϷୢᚒှ،ૈΚǶᑵ‫ە‬ᆗ)2003*‫ࡰزࣴޑ‬рǴႝတЍජୢᚒᏤӛᏢಞёߦ 9.

(19) ٬Ꮲಞ‫ޣ‬ᆶᏢಞ‫ޣ‬۶Ԝϐ໔຾Չ‫ؼ‬ӳ‫ޑ‬ϕ୏ǴΨૈᐟวᏢಞ‫ޑޣ‬Ꮲಞ୏ᐒᆶග ϲୖᆶ૸ፕ౗Ǵ٠Ъӧᆛၡπ‫ޑڀ‬ᇶշΠǴᏢಞ‫ޣ‬ౢғԾ‫ך‬Ꮴӛ‫ޑ‬Ꮲಞ੝ቻǴ ΨӢࣁᏢಞ‫ޣ‬ёӧֹӄ‫ޑ‬ӧᆛ।‫ޜ‬໔ύឍॊԾρ‫ޑ‬ཀ‫ـ‬ǴӢԜ‫׳‬኷‫ܭ‬ᆶ‫ځ‬дᏢ ಞ‫ޣ‬ᑈཱུϕ୏Ƕܰ୯‫)ؼ‬2005*ࡰрӧᆛၡᕉნ‫ࢂ܈‬໺಍௲࠻ᕉნჴࡼୢᚒᏤӛ ௲Ꮲ฼ౣ೿‫ڀ‬ԖගϲᏢಞԋਏ‫ޑ‬ਏ੻ǴԶЪᆛၡӝբᏢಞᕉნᡉ๱ᓬ‫ܭ‬໺಍௲ ࠻ӝբᏢಞᕉნǶLiu฻Γ)2010*ࣴ‫ز‬΋঺፾ӝλᏢғ‫ޑ‬ୢᚒᏤӛᏢಞѳѠǴ่ ݀ᡉҢλᏢғ‫מزࣴޑ‬ѯǵ،฼ǵ୺Չᆶຑ՗ૈΚᡉ๱ගϲǶHung฻Γ)2012* ‫زࣴޑ‬ว౜ӧୢᚒᏤӛᏢಞᕉნύǴ‫ڀ‬Ԗ࿶ᡍ‫ޑ‬Ꮲಞ‫ૈޣ‬ԖਏᔅշཥЋ຾ՉᏢ ಞǴаၲ‫ډ‬ӝբᏢಞҞ‫ޑ‬Ƕ!. ҳ. ࢇ ‫ ݽ‬σ. ᘜયа΢ࣴ‫ز‬ǴୢᚒᏤӛᏢಞ࣬྽፾ӝᔈҔ‫ܭ‬ᆛၡ௲ᏢǴό໻፾Ҕ‫ܭ‬஑཰. Ȇ ୽. Ᏸ. ሦୱǴΨ፾ӝԃសቫၨե‫ޑ‬ύλᏢᏢಞ‫ޣ‬຾ՉᏢಞǶҁࣴ‫ز‬а஭ৎԋ)2008*ว ৖‫ޑ‬ୢᚒᏤӛᏢಞ‫س‬಍ࣁ୷ᘵǴ୷‫ܭ‬Ȩ‫ޕ‬ȩǵȨՉȩǵȨࡘȩϐୢᚒᏤӛᏢಞኳԄǴ. Ȇ. ᡣ௲Ꮲ‫ޣ‬೛ीୢᚒှ،ᏢಞᡳࢎǴ‫ڐ‬շᏢಞ‫ޣ‬٩ྣᡳࢎᏢಞࢬำֹԋୢᚒှ،. ŏŢŵ. ŴŪŵ. ź. ‫؁‬ᡯǶԜѦǴҁࣴ‫ܭز‬Ԝ΋ୢᚒᏤӛѳѠ΢୷‫཮ޗܭ‬ᆛၡϩ‫݋‬ว৖‫ޗ‬ဂୀෳᐒ. ů. Ţŭ. Ŧų. ŪŰ. ‫ڋ‬Ǵ٠ᙖҗ‫פ‬р‫ޗঁ؂‬ဂύPageRankॶനଯ‫ޑ‬ཀ‫ـ‬ሦ೑Ǵ௖૸௲ৣ‫ܭ‬ୢᚒᏤӛ. Ū ů Ŗ. ŷ. ᆛၡӝբᏢಞ௲Ꮲᕉნύ٬Ҕ୷‫ޗܭ‬ဂύཀ‫ـ‬ሦ೑‫ޑ‬Βભ໺ኞኳԄǴٰᔅշ௲. ġń. ũŦůŨŤũŪġ. ৣ໺เୢᚒᏤӛᏢಞҺ୍ᆶБ‫ݤ‬Ǵࢂցᓬ‫ܭ‬΋૓໺಍‫ޔ‬ௗ೸ၸ௲ৣ໺ၲୢᚒᏤ ӛᏢಞҺ୍ᆶБ‫ݤ‬๏Ꮲಞ‫ޑޣ‬΋ભ໺ኞኳԄǶҁࣴ‫زࣴޑز‬ԋ݀Ԗёૈว৖р ୢᚒᏤӛᆛၡӝբᏢಞ௲Ꮲᕉნύ‫׳‬Ԗਏ‫ޑ‬ၗૻ໺ኞኳԄǴ٠‫׳‬຾΋‫؁‬ගϲୢ ᚒᏤӛᆛၡӝբᏢಞԋਏǶ! ! ! !. 10.

(20) ಃΒ࿯!‫཮ޗ‬ᆛၡϩ‫݋‬ᆶ‫ޗ‬ဂ௖୎! ΋ǵ‫཮ޗ‬ᆛၡϩ‫!݋‬. !. Wasserman ‫ ک‬Faust)1994*‫ۓ‬က!ȸ‫཮ޗ‬ᆛၡϩ‫཮ޗܭ୷ࢂ݋‬Ꮲ‫زࣴޑ‬Б‫ݤ‬Ǵ Ѭёа೏Ҕٰϩ‫ୖ݋‬ᆶ‫ޣ‬ϐ໔ϕ୏ኳԄ‫ޑ‬ᜢ߯ȹǶ‫཮ޗ‬ᆛၡϩ‫ࢂ݋‬Ҕٰ௖૸‫ޗ‬ ဂύঁᡏ໔‫ޑ‬ᜢ߯ǴаϷঁΓ໔ᜢ߯‫׎܌‬ԋ‫่ޑ‬ᄬᆶཀ఼)Wellman & Berkowitz, 1988*ǴΨ൩ࢂᇥ‫཮ޗ‬ᆛၡࢂΓᆶΓϐ໔ᖄᛠ‫཮ޗޑ‬ᜢ߯ᆛၡ)Sccott, 2000*Ƕ߈ ԃٰ‫཮ޗ‬ᆛၡϩ‫ޑ݋‬Б‫کݤ‬ෳໆς೏ቶ‫ݱ‬ᔈҔ‫ܭ‬ӵ‫཮ޗ‬Ꮲǵᆅ౛Ꮲǵ୘཰ǵғ ‫ނ‬Ꮲ‫ک‬ၗૻ‫ೌמ‬฻όӕ‫ޑ‬ሦୱǴ߈ԃٰӧ௲‫ػ‬ሦୱ‫ޑ‬ᔈҔΨ೴ᅌᑫଆǶ!. ࢇ ‫ ݽ‬σ ӧ‫཮ޗ‬ᆛၡύஒঁᡏຎࣁ࿯ᗺ)node*ǴԶঁᡏ໔‫ޑ‬ᜢ߯೏ຎࣁጕ)edge*Ǵ ҳ. Ȇ ୽. Ᏸ. ೱ่ᜢ߯Ϸ‫่ځ‬ᄬᆶၸำࣁ‫཮ޗ‬ᆛၡ‫୷ޑ‬ᘵཷ‫)ۺ‬Mitchell,1969*Ƕ Mitchell)1969*ᇡࣁाᄬԋ‫཮ޗ‬ᆛၡԿϿᔈ‫ڀ‬ഢаΠΟঁाનǺ!. Ȇ. 1ǵ!. Չ୏‫)ޣ‬actors*!. Ţŭ. ź. ŴŪŵ Ŧų. ᜢ߯)relationship*!. ŪŰ. 2ǵ!. ŏŢŵ. ‫཮ޗ‬ᆛၡ‫ޑ‬ЬᡏǴёаࢂঁΓǵ٣ǵ‫܈ނ‬ಔᙃǶ!. ŷ. ů. Չ୏‫ޣ‬۶Ԝϐ໔‫ޑ‬ᜢ߯ೱ่ࢂ೸ၸ‫ނ‬፦‫ނߚ܈‬፦ၗྍᙯ౽‫ࢬ܈‬೯‫ޑ‬ᆅၰǴ. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ӧᆛၡ่ᄬύёаჹঁᡏՉ୏ග‫ٮ‬ᐒ཮‫܈‬ज़‫ঁڋ‬ᡏՉ୏Ǵࢂ΋ᅿՉ୏‫ޣ‬໔࡭ុ ‫܄‬ᜢ߯ȐWasserman & Faust ,1994ȑ ǶԜѦǴՉ୏‫ޣ‬۶Ԝϐ໔཮Ӣࣁࢌ٤ᜢ߯)ӕ ੤ǵ࣬ӕᑫ፪฻*Զౢғϕ୏ᜢ߯Ƕ! 3ǵ!. ೱ่)linkages*‫܈‬ᖄᛠ)ties*!. ΋ঁՉ୏‫ޣ‬གྷ‫ځک‬дՉ୏‫ࡌޣ‬ҥࢌᅿ‫׎‬Ԅ‫ޑ‬ᜢ߯ਔǴሡ೸ၸ೼৩)path*‫ޔ‬ ௗ‫܈‬໔ௗӦࡌҥᜢ߯Ƕೱ่ନΑёаϩࣁൂᚈӛаϷԖคѦǴᗋёа٩ᏵՉ୏ ‫ޣ‬໔‫ޑ‬ϕ୏ᓎ౗،‫ۓ‬۶Ԝᜢ߯‫ޑ‬மࡋǴΨёҗೱ่‫ޑ‬ம১‫פ‬рᆛၡϣ‫ޑ‬ԛભი ᡏ)Subgroup*‫ۚ܈‬ύ‫ޑ‬ύϟ‫)ޣ‬Brokers*)Wasserman & Faust ,1994ǹ೚ӄՙǴ 2004ȑǶೱ่ё຾΋‫୔؁‬ϩࣁமೱ่)strong ties*аϷ১ೱ่)weak ties*Ǵமೱ่. 11.

(21) ߄Ңঁᡏϕ୏ၨᓎᕷǴᜢ߯ၨᆙஏǴԶ১ೱ่߄Ңঁᡏ໔ၨϿௗ᝻Ǵᜢ߯ၨ౧ ᚆ)Granovetter,1973ǹ৪ችԋǴ2003*Ƕ! ԜѦǴᖄᛠமࡋόӕஒ཮ౢғόӕ‫཮ޗޑ‬ᆛၡ่ᄬǴӧ‫཮ޗ‬ᆛၡϩ‫݋‬ύǴ Ԗ൳ঁ࿶த٬Ҕ‫ޑ‬ෳໆ‫ೌמ‬Ǵхࡴѳ֡നอၡ৩ߏࡋ)Average Shortest Path Length* ǵ ᆛ ၡ ‫ ޔ‬৩ )Diameter* ǵ ᆛ ၡ ஏ ࡋ )Density* ǵ ೕ ኳ )Size* ǵ ύ Ј ‫܄‬ )Centrality*ǵำࡋύЈ‫)܄‬Degree Centrality*ǵௗ߈ύЈ‫)܄‬Closeness Centrality*ǵ ύϟύЈ‫)܄‬Betweenness!Centrality*ǵ໣ᆫ߯ኧ)Clustering!Coefficient*฻!)Ting et al.,2013*Ƕӧ೭٤‫཮ޗ‬ᆛၡϩ‫ࡰ݋‬኱ύǴΞаೕኳǵᆛၡஏࡋǵύЈ‫܄‬നத ೏ᔈҔǴᇥܴӵΠǶ!. !. ҳ. ࢇ ‫ ݽ‬σ. 2/ ᆛၡೕኳǺࡰ‫཮ޗࢂޑ‬ᆛၡύՉ୏‫ޑޣ‬ᕴኧǴᕴኧຫӭೕኳຫεǶ!. Ȇ ୽. Ᏸ. 3/ ᆛၡஏࡋǺ‫཮ޗ‬ᆛၡஏࡋࢂࡰӧ‫཮ޗ‬ᆛၡύჴሞᜢ߯ᆶ‫܌‬Ԗёૈวғᜢ ߯‫ޑ‬К‫)ٯ‬৪ችԋǴ2003*Ǵࢂᆛၡύԋ঩࣬ϕᖄᛠ‫ޑ‬ำࡋ)Dozier! et!. Ȇ. al/-1987*ǴᆛၡஏࡋຫεǴ߄Ңԋ঩໔ᜢ߯ຫᆙஏǶ!. ŏŢŵ. ŴŪŵ. ź. 4/ ύЈ‫܄‬ǺFreeman)1979*ගр‫ޑ‬ำࡋύЈ‫܄‬ǵௗ߈ύЈ‫܄‬ǵύϟύЈ‫܄‬. ů. Ţŭ. Ŧų. ŪŰ. ࢂ‫཮ޗ‬ᆛၡϩ‫݋‬ύനத೏٬Ҕ‫ޑډ‬ύЈࡋǶ‫ځ‬ύำࡋύЈ‫܄‬ຫεж߄၀. Ū ů Ŗ. ŷ. ࿯ᗺຫख़ाǴຫૈ‫ک‬дΓᜢᖄǴӧ౜ჴғࢲύǴѝԖϿኧ࿯ᗺ‫ڀ‬Ԗଯำ. ġń. ũŦůŨŤũŪġ. ࡋύЈ‫܄‬ǹௗ߈ύЈ‫ࢂޑࡰ܄‬࿯ᗺ‫ܭ‬ᆛၡύ‫ޑ‬໺ኞቹៜำࡋǴҭࡰ࿯ᗺ ‫ܭ‬ᆛၡύ‫ځ‬д࿯ᗺ‫ޑ‬നอຯᚆߏࡋ‫܈‬ௗ߈ำࡋǴ྽ௗ߈ύЈ‫܄‬ຫଯж߄ ၀࿯ᗺ‫ޔ‬ௗቹៜ‫ځ‬д࿯ᗺ‫ޑ‬ೲࡋΨຫமਗ਼ǹύϟύЈ‫ࢌࢂޑࡰ܄‬੝‫ۓ‬࿯ ᗺ‫ܭ‬ᆛၡύҺཀ‫ٿ‬࿯ᗺ໔നอຯᚆ‫ޑ‬ύϟำࡋǴ྽࿯ᗺύϟύЈ‫܄‬ຫଯǴ ж߄၀࿯ᗺຫԖёૈ‫ת‬ᄽᐏఉ‫فޑ‬ՅǶ! ! ! ! ! 12.

(22) ΒǵᔈҔ‫཮ޗ‬ᆛၡϩ‫ޗܭ݋‬ဂ௖୎! Ҟ߻୯ϣѦςԖ೚ӭᔈҔ‫཮ޗ‬ᆛၡϩ‫زࣴޑ݋‬ǴՖ჏ඁǵယ‫ػ‬և)2007*‫ޑ‬ ࣴ‫ࡰز‬рǴ࣬՟ࡋຫଯ‫཮ޗޑ‬ᆛၡԋ঩཮Ꮙᆫӧ΋ଆǴౢғኧঁη‫ޗ‬ဂǴԶη ‫ޗ‬ဂ໔཮ӢࣁߞҺำࡋৡ౦Զቹៜ‫ޕ‬᛽ϩ٦ཀᜫǶ໳ߪғ)2009*ճҔ‫཮ޗ‬ᆛၡ ϩ‫ࡌ݋‬ҥ΋঺ࡹ۬‫۔‬঩ᙍ୍ௗඹΓᒧႣෳ‫س‬಍ǶDaly)2010*߾ࢂճҔ‫཮ޗ‬ᆛၡ ౛ፕ‫ܭ‬௲‫ׯػ‬ॠ΢ǶBayer ฻Γ)2012*ճҔ‫཮ޗ‬ᆛၡϩ‫݋‬ႣෳрᏢғ፷Ꮲ‫ޑ‬ёૈ ‫܄‬Ǵ٠Ъ‫ڀ‬Ԗ࣬྽όᒱ‫ޑ‬ႣෳྗዴࡋǶPalazuelos ฻Γ)2013*ΨᔈҔ‫཮ޗ‬ᆛၡϩ ‫݋‬ᆶၗ਑௖୎‫ܭ‬ኧՏᏢಞ΢Ǵдॺ‫ࡰزࣴޑ‬р೭ᅿ‫ೌמ‬Ԗշ‫ܭ‬௲ৣඓඝᏢғ‫ޑ‬ Ꮲಞ߄౜Ƕ!. ҳ. ߈ԃٰ GbdfcpplǵUxjuufsǵQmvslǵCmph ฻‫ޗ‬ဂᆛઠ‫ޑ‬ว৖Ǵ֎ЇΑε. Ᏸ. Ȇ ୽. !. ࢇ ‫ ݽ‬σ. ໆ٬Ҕ‫ޣ‬ӧᆛઠ΢຾Չ‫ޗ‬Ҭࢲ୏Ǵ೚ӭࣴ‫ز‬Ψ઱઱ճҔ‫཮ޗ‬ᆛၡϩ‫פٰ݋‬൨ᆛ ၡύ‫ޗޑ‬ဂ‫܈‬იᡏǶUjoh ฻Γ)2013*ճҔ‫཮ޗ‬ᆛၡϩ‫݋‬Ϸᆛ।௖୎‫ٰೌמ‬ว௚. Ȇ. Ϝ࡟იᡏǶBcefmcbsz ᆶ Fm.Lpsboz)2013*٬Ҕ‫ڙ‬ज़࣒ᅟ૟ୗᐒ)Sftusjdufe!. ŏŢŵ. ŴŪŵ. ź. Cpmu{nboo!Nbdijof-!SCN*‫פ‬൨‫ޗ‬ဂύ‫ޑ‬ЬᚒǴ٠ճҔЬᚒ‫פ‬൨‫ޗ‬ဂǶMbncb ᆶ. ů. Ţŭ. Ŧų. ŪŰ. Obsbzbobn)2013*!ࣗԿؒԖ٬Ҕ‫཮ޗډ‬ᆛၡ੝ቻǴ໻ճҔ‫ޗ‬Ҭ୮‫ޑ‬ၗૻаϷ኱. Ū ů Ŗ. ŷ. ᠸջёୀෳрიᡏǶDifo ᆶ Diboh)2014*ճҔ‫཮ޗ‬ᆛၡϩ‫݋‬ᔅշᏢಞ‫ډפޣ‬ӝ. ġń. ũŦůŨŤũŪġ. ፾‫ޑ‬ᏢಞუՔǴаගϲୢᚒᏤӛᆛၡӝբᏢಞԋਏ!Ƕ! ᆕӝа΢Ǵ‫཮ޗ‬ᆛၡϩ‫݋‬ёаϩ‫ঁ݋‬ᡏ໔ϕ୏аϷ௖୎рᏢಞიᡏǴऩૈ ຾΋‫פ؁‬рᏢಞიᡏύ‫ޑ‬ཀ‫ـ‬ሦ೑ٰߦ຾ᆛၡӝբᏢಞԋਏǴ཮ࢂ΋ঁ‫ڀ‬Ԗ௖ ૸ሽॶ‫زࣴޑ‬᝼ᚒǶӢԜǴҁࣴ‫ز‬ճҔ‫཮ޗ‬ᆛၡϩ‫݋‬མଛ୷Ӣᄽᆉ‫ݤ‬Ǵаന٫ ϯ‫ޑ‬БԄ௖୎рୢᚒᏤӛᆛၡӝբᏢಞᕉნύ‫ޑ‬ᏢಞიᡏǴ٠ᙖҗ PageRank ‫פ‬ ‫ډ‬၀იᡏύ‫ޑ‬ཀ‫ـ‬ሦ೑Ǵ‫׆‬ఈᢀჸ௲ৣ೸ၸཀ‫ـ‬ሦ೑໺ၲୢᚒᏤӛᆛၡӝբᏢ ಞҺ୍ᆶБ‫ޑݤ‬Βભ໺ኞኳԄǴࢂցК௲ৣ໺಍௦Ҕ‫ޑ‬΋ભ໺ኞኳԄǴ‫׳‬৒ܰ ගϲୢᚒᏤӛᆛၡӝբᏢಞԋਏᆶᏢಞ‫ޣ‬۶Ԝϐ໔‫ޑ‬ϕ୏Ƕ!. 13.

(23) ಃΟ࿯!Βભၗૻ໺ኞ౛ፕ! Βભၗૻ໺ኞ)two.step!flow!of!communication*Ξᆀࣁ‫ٿ‬ભၗૻ໺ኞǴጔ ଆ‫ ܭ‬Lazasfeld ฻Γ‫ ܭ‬1940 ԃჹऍ୯ᕴ಍εᒧ‫ޑ‬΋໨ࣴ‫ز‬Ǵࢂε౲໺ኞᏢԐය ‫ޑ‬౛ፕϐ΋ǴΨࢂਏ݀Ԗज़౛ፕϐ΋Ƕ‫ځ‬Ьा‫ࣁۺཷޑ‬ၗૻ໺ሀࢂҗε౲൞ᡏ ໺ሀ๏ཀ‫ـ‬ሦ೑Ǵϐࡕӆҗཀ‫ـ‬ሦ೑໺ሀ๏ε౲ǶӢԜǴΒભ໺ኞኳԄύ‫ޑ‬ཀ ‫ـ‬ሦ೑‫ת‬ᄽΑε౲൞ᡏᆶ҇౲ϐ໔‫ޑ‬ύϟ‫ޣ‬ǴӕਔΨࢂၗૻ໺ሀ‫ޑ‬ᜢᗖΓ‫ނ‬Ƕ!! ΋ǵཀ‫ـ‬ሦ೑੝፦!. ࢇ ‫ ݽ‬σ. ཀ‫ـ‬ሦ೑)opinion leadership*ࢂҗ Lazarsfeld ฻Γӧ 1940 ԃගр‫ཷޑ‬. ҳ. ‫ۺ‬Ǵ‫!ډޔ‬1944 ω҅Ԅගрཀ‫ـ‬ሦ೑΋ຒ)Lazarsfeld!et!al.-1944*Ƕдॺᇡ. Ȇ ୽. Ᏸ. ࣁε౲໺ኞ൞ϟቹៜΚόӵཀ‫ـ‬ሦ೑Ǵஒૻ৲໺ၲ๏ཀ‫ـ‬ሦ೑Ǵӆ࿶җཀ ‫ـ‬ሦ೑ஒૻ৲໺ၲ๏ၟᒿ‫཮ޣ‬ၨૈቹៜӕ΋‫ޗ‬ဂ‫ޑޣ‬ᄊࡋǶ!. Ȇ. ŴŪŵ. ź. ŏŢŵ. ᜢ ‫ ܭ‬ཀ ‫ ـ‬ሦ ೑ ‫ ۓ ޑ‬က Ǵ ೚ ӭ Ꮲ ‫ ޣ‬Ԗ ό ӕ ‫ ݤ ࣮ ޑ‬Ǵ Rogers! ‫!ک‬. ŪŰ. Ŧų. Shoemaker)1971*ᇡࣁٗ٤࿶தӦǵߚ҅ԄӦර‫܌ځ‬టϐБӛǴቹៜдΓᄊ ࡋᆶѦᡉՉࣁ‫ޑ‬Γᆀϐࣁཀ‫ـ‬ሦ೑ǶEngelǵBlackwell!ᆶ!Kollat)1995*!ᇡ. Ţŭ. ů. ŷ Ū ġ ů ńũ ࣁٗ٤ૈ୼ԖਏቹៜдΓ‫ޑ‬Γᒏϐཀ‫ـ‬ሦ೑ǹCorey)1971*! ߾ᇡࣁཀ‫ـ‬ሦ ŦůŨŤũŪġŖ. ೑ࢂӧიᡏύ೏ߞҺЪ೏ᇡࣁࢂ੃৲ᡫ೯‫ޑ‬ΓγǴдॺ‫ޑ‬ཀ‫ڀـ‬Ԗж߄‫܄‬Ǵ ٠Ъдॺ཮࿶த᠋ǵ࿶த᎙᠐࣬ᜢ൞ᡏǴӆஒ‫܌‬ள‫ޑޕ‬ၗૻǴ໺ଌϷቹៜ ‫ڬځ‬ᎁ‫ޑ‬ΓǹMancuso! )1969*ᇡࣁٗ٤࿶த೏‫ځ‬дΓፎ௲ᆶቻ၌ཀ‫ޑـ‬ΓǴ ൩ᆀϐࣁཀ‫ـ‬ሦ೑ǶLazarsfeld฻Γ)1944*ᘜયཀ‫ـ‬ሦ೑೯த‫ڀ‬ԖаΠϖঁ Ьा੝ቻǺ! )1*‫཮ޗ‬Ϸ࿶ᔮӦՏၨଯǹ! )2*௲‫ػ‬ำࡋၨଯǹ! )3*ኪ៛‫܈‬ௗ᝻ε౲൞ϟ‫ޑ‬ᓎ౗ၨଯǹ!. 14.

(24) )4*ჹѦٰૻ৲֎ԏ‫ޑ‬ᆅၰၨӭǹ! )5*ᆶཥ٣‫ނ‬ж౛Γௗ᝻ၨӭǴᢀ‫ۺ‬ၨ໒ܴǴՉࣁཥዊ)ഋܿ༜฻Ǵ 2004*ǶԶጰऍྻȐ1995ȑ༊᏾Αၸѐ‫ٿ‬ભၗૻ໺ኞ౛ፕ‫࣬ޑ‬ᜢࣴ‫ز‬Ǵගр ཀ‫ـ‬ሦ೑‫ڀ‬Ԗ‫ޑ‬Ѥ໨੝‫܄‬Ǻ! )1*!੃຤‫ޣ‬ёа೏୔ϩԋȨሦ೑ȩᆶȨଓᒿ‫ޣ‬ȩΒᜪǶ! )2*ཀ‫ـ‬ሦ೑ନΑ‫ڀ‬ഢଓᒿ‫܌ޣ‬ལख़‫ޑ‬஑཰‫ޕ‬᛽ѦǴᗋѸ໪ࣁଓᒿ‫܌ޣ‬ ௗ‫ߞ܈ڙ‬ҺǶ! )3*ཀ‫ـ‬ሦ೑ௗ᝻‫ܭ‬ε౲൞ϟ‫ޑ‬ᓎ౗Ǵ཮ၨଓᒿ‫ࣁޣ‬ӭǶ!. ࢇ ‫ ݽ‬σ. )4*ཀ‫ـ‬ሦ೑ᆶ‫ځ‬ଓᒿ‫࣬ࡐޣ‬՟Ǵ٠Ъதឦ‫ܭ‬ӕ΋ঁიᡏǶ!. ҳ. Weimann! )1994*ஒ‫ڀ‬Ԗਔ໔ǵ‫ޗ‬Ҭጄൎǵቹៜ໼ӛ฻ཀ‫ـ‬ሦ೑‫܌‬ᏱԖ. Ȇ ୽. Ᏸ. ‫ޑ‬ቹៜ੝፦ᘜયӵΠǺ!. )1*Γ਱੝፦Ǻബཥ‫ޑ‬ǹᑼΕ‫ޑ཮ޗ‬ǹჹ‫ࢌܭ‬΋Ьᚒ‫܈‬ሦୱԖᑫ፪ǵԖ. Ȇ. ‫ـ‬᛽ǵ஑ᆒ‫ޑ‬ǹჹ‫ࢌܭ‬΋ЬᚒੋΕำࡋଯǹ‫ـ‬᛽ቶᗡ‫ޣ‬Ƕ!. ŏŢŵ. ŴŪŵ. ů. Ţŭ. Ŧų. ŪŰ. ‫ޑ‬Ƕ!. ź. )2*‫཮ޗ‬੝፦Ǻӝဂǹ‫ޗ‬Ҭࢲ៌ǹ‫ޗ‬Ҭᆛ๎ύЈǹ৒ܰᒃ߈ǹёߞҺ. Ū ů Ŗ. ŷ. )3*‫཮ޗ‬Γα಍ी੝፦Ǻᒿ๱Ьᚒ‫܈‬ሦୱԶόӕǹӢЎϯ‫཮ޗ܈‬ᕉნԶ. ġń. ũŦůŨŤũŪġ. όӕǹᒿ๱ਔжԶόӕǹቹៜᆶ‫ڙ‬ቹៜ‫ޑ‬໼ӛ࣬՟Ƕ! !. !. ৊஖ကȐ2006ȑ‫زࣴޑ‬ᘜયᆛၡཀ‫ـ‬ሦ೑‫ڀ‬ԖаΠѤঁᄬय़‫ޑ‬੝‫܄‬Ǻ! !. ! !. !. !. )1*੝፦य़Ǻᒃ‫ک‬Κǵࡉᓨགǵ௵᎒ࡋǵ‫׫‬Εၨߏ‫ޑ‬ਔ໔‫ܭ‬೽ပ਱. !. ΢Ƕ!. !. )2*ϣ৒य़Ǻග‫ޑٮ‬ϣ৒ሡᙦ൤ЪϷਔǵ‫ـ‬ှሡᐱ੝ǵඓඝჹ‫ޑ‬ਔ. !. ᐒᗺว߄ЎകǶ!. !. )3*‫ޗ‬ဂय़Ǻᆶ᠐‫ޣ‬ϕ୏ǵ‫׎‬ԋα࿞ǵ‫ޗܭ‬ဂύࡌҥଆΓሞᆛၡǶ!. ᆕӝ΢ॊǴ‫ॺך‬ёаள‫ޕ‬ཀ‫ـ‬ሦ೑ჹ‫ܭ‬ε౲‫ڀ‬Ԗख़ε‫ޑ‬ቹៜΚǴ٠Ъཀ‫ـ‬ 15.

(25) ሦ೑೯த೿཮‫ډڙ‬ε౲‫ߞޑ‬ᒘǶԶᒿ๱ࣽ‫מ‬຾‫؁‬Ǵ໺ኞΨ຾ΕᆛၡШжǴཀ‫ـ‬ ሦ೑ӧᆛၡ΢ᝩុ‫ת‬ᄽ๱ख़ा‫ف‬ՅǴ࡭ុวචቹៜΚǶ! ၸѐࣴ‫ز‬ၨத٬ҔхࡴԾ‫ך‬ᇡ‫ݤۓ‬Ȑself.designating!methodȑ ǵ‫཮ޗ‬ीໆ‫ݤ‬ Ȑsociometric! methodȑǵᜢᗖΓ‫ނ‬ຑ᠘‫ݤ‬Ȑkey! informants! methodȑǵᢀჸ‫ݤ‬ Ȑservationȑ฻ٰ‫פ‬൨ཀ‫ـ‬ሦ೑)ጰᅤܹǴ2011*ǴฅԶ೭٤Б‫ݤ‬೿ࢂа໺಍໺ ኞᕉნࣁЬǴᆶ౜Ϟᆛၡ໺ኞԖ೚ӭৡ౦Ƕ౜Ϟ‫ޑ‬ᆛၡ໺ኞǴӢࣁԖΑႝတ‫ޑ‬ ᇶշу΢‫ޗ‬ဂᆛઠ‫ޑ‬ᑫଆǴёаவ٬Ҕ‫ޣ‬ϕ୏‫ރ‬ᄊǵૻ৲Ҭࢬ฻࣬ᜢ੝ቻॶύ ‫פ‬൨рཀ‫ـ‬ሦ೑Ǵ‫ٯ‬ӵ೸ၸ Pagerankǵindegree ॶ฻)Yu!et!al/-2010*Ƕ! ΒǵΒભၗૻ໺ኞ࣬ᜢᔈҔ!. ҳ. ࢇ ‫ ݽ‬σ. ၗૻ໺ኞӧғࢲ΢ᆶε౲৲৲࣬ᜢǴ೚ӭዊࢬ཮ᒿ๱໺ኞ൞ᡏၟ๱วᐨ‫܈‬. Ȇ ୽. Ᏸ. ଏᐨǴ‫ٯ‬ӵ 2014 ԃමӧѠ᡼आཱུ΋ਔ‫ޑ‬ӇఏࡷᏯǴߡࢂӧ൞ᡏ‫ޑ‬໺ኞΠِೲԋ. Ȇ. ࣁ዗ߐ၉ᚒǴ௖‫ځز‬চӢǴЬाࢂӢࣁӜΓ‫ୖޑ‬ᆶ٬ள೭໨ࢲ୏ഢ‫ڙ‬ᢋҞǴԶ. ŴŪŵ. ź. ŏŢŵ. ೭٤ӜΓӧ‫཮ޗ‬΢ёаᆉࢂ΋ᅿཀ‫ـ‬ሦ೑Ƕ!. ů. Ţŭ. Ŧų. ŪŰ. ԜѦǴΒભၗૻ໺ኞࣴ‫ز‬த೏ᔈҔӧВதғࢲᒧ᏷аϷᒧᖐ᝼ᚒ΢ǶKatz. ŷ. ᆶ Lazarsfeld)1955*‫زࣴޑ‬ว౜Ǵཇԃᇸ‫஁ޑ‬ζཇёૈԋࣁȨਔሰѺ‫ת‬ȩϷȨᒧ. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ࣮ቹТȩ‫ঁٿ‬᝼ᚒ΢‫ޑ‬ཀ‫ـ‬ሦ೑ǹ‫ڬ‬Ўฐ)2002*‫زࣴޑ‬௖૸໺ኞௗ᝻ᆅၰჹ੃ ຤‫ޣ‬ӧᖼວՉ୏ႝ၉ਔ‫ޑ‬ቹៜǴ่݀ᡉҢৎΓჹ੃຤‫཮ޗޣ‬ϯ‫ޑ‬ቹៜКܻ϶‫ת‬ ᄽ๱‫ࣁ׳‬ख़ा‫فޑ‬ՅǴ೭ᆶ Childers!Ϸ Rao!)1992*‫زࣴޑ‬ว౜࣬಄ӝǶഋඁঙ )2004*‫زࣴޑ‬௖૸Βભၗૻ໺ኞჹ‫੃ܭ‬຤‫ޣ‬ᖼວॿ኷೽཮঩᛾‫ޑ‬ቹៜǴ่݀ᡉ ҢନΑӧᒧᖐ᝼ᚒǵҶ໕ਓၯՉࣁǵВதғࢲᒧ᏷΢Ԗቹៜਏ݀ѦǴӧҶ໕଼ ‫ॿي‬኷೽ҭԖ‫ځ‬ቹៜਏ੻ǶRobinson ‫زࣴޑ‬Ψ᛾ჴཀ‫ـ‬ሦ೑ჹ‫ܭ‬ᒧ҇‫ڀ‬Ԗቹៜ Κ)Robinson-1976*ǴՠًቼᎩ)1994*ӧፓࢗ҇୯ΖΜΟԃѠ᡼࣪Ӛᑜѱ᝼঩Ϸ ໂᙼѱߏᒧᖐ‫زࣴޑ‬ύǴ٠҂ว౜ཀ‫ـ‬ሦ೑ӸӧǶЦഌᆶଭӵᗪ)2009*ࣴ‫ز‬ཀ ‫ـ‬ሦ೑ӧኧՏᏢಞ΢ࢂց‫ڀ‬Ԗ‫ځ‬ख़ाӦՏǴࣴ‫่݀ز‬ᡉҢӧᏢಞᕉნύ཮Ԗཀ 16.

(26) ‫ـ‬ሦ೑‫ޑ‬ౢғǴӕਔཀ‫ـ‬ሦ೑Ψ‫ת‬ᄽΑᔅշᏢಞ‫فޑ‬ՅǶ! ᗨฅΒભၗૻ໺ኞ౛ፕᔈҔӧ໺ኞሦୱࡐத‫ـ‬ǴՠᔈҔ‫ܭ‬௲ᏢǴࣗԿࢂኧ ՏᏢಞ΢‫࣬ޑ‬ᜢࣴ‫ߚࠅز‬தϿǴࢂ΋ঁߚதॶள௖૸‫زࣴޑ‬᝼ᚒǶӢԜǴҁࣴ ‫ܭ୷ز‬Βભၗૻ໺ኞ౛ፕǴ௖૸Ԝᅿၗૻ໺ኞኳԄǴࢂցᓬ‫ܭ‬໺಍΋ભၗૻ໺ ኞኳԄǴૈԖਏගϲୢᚒᏤӛᆛၡӝբᏢಞ‫ޑޣ‬ᏢಞԋਏǶ! ! ! !. Ȇ ź. ŴŪŵ. ů. Ţŭ. !. Ŧų. ŪŰ. !. ŏŢŵ. !. Ȇ ୽. !. Ᏸ. !. ҳ. ࢇ ‫ ݽ‬σ. ġń. ũŦůŨŤũŪġ. ! ! ! ! ! ! 17. Ū ů Ŗ. ŷ.

(27) ಃѤ࿯!იᡏਏૈᆶიᡏᏉᆫΚ! ΋ǵიᡏਏૈ! იᡏਏૈ)collective! efficiacy*߯ࡰიᡏԋ঩ჹ‫܌‬ឦიᡏࢂցёԋфӦֹԋ ੝‫ۓ‬Һ୍‫ޑ‬Ӆ٦ߞ‫)ۺ‬Bandura-! 1997*ǴᏱԖၨଯიᡏਏૈϐი໗Ǵӧय़ჹ֚ᜤ ਔၨૈ࡭ុոΚаၲԋҞ኱Ǵ຾Զ৖౜ᓬ౦‫߄ޑ‬౜ȐGeorge!'!Feltz-1995ȑǶᆛ ၡࣽ‫מ‬຾৖‫ז‬ೲ‫ޑ‬ϞϺǴᏢಞ‫ޑޣ‬ᏢಞӦᗺΨவ໺಍௲࠻Ꮲಞᙯ౽Կᆛၡ΢຾ ՉᏢಞǴΨӢࣁᆛၡ‫ޑ‬੝‫܄‬ǴᆛၡӝբᏢಞΨ೴ᅌ‫ډڙ‬ख़ຎǴᆛၡӝբᏢಞࢂ Ԗਏၲ‫ډ‬იᡏҞ኱‫ޑ‬ёՉБԄϐ΋Ƕ!. ࢇ ‫ ݽ‬σ ‫཮ޗ‬ᇡ‫ޕ‬౛ፕᏢ‫ ޣ‬Bandura)1977*ගрԾ‫ך‬ਏૈ)Self.efficacy*‫ۺཷޑ‬Ǵ྽ ҳ. Ȇ ୽. Ᏸ. ਔࣴ‫ز‬ख़ЈЬा‫ܫ‬ӧࣴ‫ঁز‬ᡏՉࣁ୏ᐒǴϐࡕ Bandura)1986*Ξගр‫཮ޗ‬ᇡ‫ޕ‬ፕ )Social! Cognitive! Theory*Ǵග‫ঁډ‬ᡏ‫ۺߞޑ‬ǵՉࣁᆶᕉნࢂ࣬ϕቹៜ‫ޑ‬Ƕ. Ȇ. Bandura)1986*ࡰрǴঁᡏ‫ޑ‬Ծ‫ך‬ਏૈԖѤᅿЬाૻ৲ٰྍǺ!. ŴŪŵ. ź. ŏŢŵ. )1*ঁΓԋ൩߄౜Ȑperformance! accomplishmentȑǺঁΓၸѐԋф‫܈‬Ѩ௳‫ޑ‬. Ţŭ. Ŧų. ŪŰ. ࿶ᡍࣁԾ‫ך‬ਏૈ‫ޑ‬നЬाٰྍǴၸѐ‫ޑ‬ԋф࿶ᡍૈ᛾ܴԾρ‫ૈޑ‬ΚǴ٠ஒගϲ. ŷ. ů. Ծ‫ך‬ਏૈགǴԶѨ௳‫ޑ‬࿶ᡍஒफ़եԾ‫ך‬ਏૈǶฅԶ΋ѿ࡭ុ‫ޑ܄‬ԋфቚமΑᏢ. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ಞ‫ޑޣ‬Ծ‫ך‬ਏૈਔǴջ٬ଽᎁ਋௳ǴΨόठ‫ܭ‬ჹঁᡏ‫ޑ‬Ծ‫ך‬ਏૈགౢғϼε‫ޑ‬ ཞ্Ƕ! )2*ඹж࿶ᡍȐvicarious!experienceȑ Ǻ྽дΓԋфӦֹԋԾρջஒय़ᖏ‫ޑ‬π բਔǴஒૈᇥܺԾρૈӵӕдΓ΋૓Ӧլܺ၀Һ୍Ǵ຾ԶගϲԾρ‫ޑ‬Ծ‫ך‬ਏ ૈǴԶ྽ིኳᆶԾρ‫ૈޑ‬Κ‫܈‬ङඳཇ࣬՟ਔǴ߾ਏ݀ཇ٫Ƕ! )3*αᇟᇥܺȐverbal!persuasionȑ Ǻᇥܺ‫قޑ܄‬ᇟёа٬Γ࣬ߞԾρ‫ڀ‬ഢԋ фၲԋҺ୍‫܌‬ሡ‫ૈޑ‬ΚǴ୤ԜᅿБԄ‫܌‬Їଆ‫ޑ‬ਏૈག೯தࢂ༾১Ъอኩ‫ޑ‬Ǵ΋ ѿঁᡏय़ᖏѨ௳ਔǴࡌҥଆ‫ޑ‬ਏૈགஒ೏ᄛ྄Ƕ! )4*௃ᆣϸᔈȐemotional!arousalȑ ǺΓॺத೸ၸ௃ᆣ‫܈‬ғ౛‫ޑ‬ϸᔈٰղᘐԾ. 18.

(28) ‫ࢂي‬ց‫ڀ‬ഢ፾ᔈᕉნ‫ૈޑ‬ΚǴԶόඍ‫ޑז‬௃ᆣϸᔈȐӵขቾǵࢬԠǵЈၢу ‫ז‬ȑஒቹៜԾ‫ך‬ਏૈ‫ޑ‬ຑ՗Ǵ᝽ӵᓐภջёૈࢂեਏૈ‫ޑ‬ቻӂ)‫ׅ‬റᛄǵᒘम ীǴ2007*Ƕ! ӧԾ‫ך‬ਏૈ୷ᘵ΢ Bandura)1986*‫׳‬຾΋‫؁‬ගрΑიᡏਏૈ‫ۺཷޑ‬Ǵаှញ იᡏ‫ޑ‬Չࣁᆶߞ‫ۺ‬ǴΨගрიᡏਏૈࣁიᡏԋ঩ӧय़ᖏ΋໨੝‫ۓ‬Һ୍ਔǴԋ঩ ჹ‫܌‬ឦიᡏࢂցૈԋфֹԋҺ୍‫ޑ‬Ӆ٦ߞ‫)ۺ‬Bandura-1997*Ƕიᡏਏૈ‫ۺཷޑ‬ வԾ‫ך‬ਏૈ‫ۯ‬՜ԶٰǴ೿‫ڀ‬Ԗ࣬ӕ‫ޑ‬ਏૈߞ‫ྍٰۺ‬Ǵ൩ቹៜԾ‫ך‬ਏૈ‫ޑ‬Ѥঁૻ ৲ٰྍύǴаၸѐ‫ޑ‬ԋ൩߄౜നࣁख़ाǴԶԜ΋Ӣનჹ‫ܭ‬იᡏਏૈԶ‫ق‬Ǵӕኬ. ࢇ ‫ ݽ‬σ. Ψࢂന‫ڀ‬ቹៜΚ‫ૻޑ‬৲ٰྍ)George!'!Feltz-1995*ǶGoddard ߾ᇡࣁიᡏਏૈ‫ޑ‬. ҳ. ߞ‫ྍۺ‬Ծ‫ܭ‬იᡏԋ঩ჹ‫܌ځ‬ឦიᡏֹԋҺ୍ϐёՉ‫܄‬ຑ՗)Goddard-2001*Ƕ. Ȇ ୽. Ᏸ. Bandura)1997*‫ࡰزࣴޑ‬рෳໆიᡏਏૈ‫ޑ‬Б‫ݤ‬ЬाԖ‫ٿ‬ᅿǺಃ΋ᅿࢂԋ঩ჹԾ ρֹԋიᡏҺ୍‫ޑ‬Ծ‫ך‬ਏૈຑሽ‫ޑ‬уᕴǹಃΒᅿ߾ࢂԋ঩ჹҁიᡏֹԋიᡏҺ. Ȇ. ୍᏾ᡏຑሽ‫ޑ‬уᕴǶ!. Ţŭ. Ŧų. ŪŰ. ŴŪŵ. ź. ŏŢŵ. ΒǵიᡏᏉᆫΚ!. ŷ. ů. იᡏᏉᆫΚ)group! cohesion*߯ࡰიᡏϣԋ঩ᆙஏ‫่ޑ‬ӝӧ΋ଆǴ೯தૈϸ. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ࢀрიᡏӧଓ‫؃‬ಔᙃҞ኱ϷҞ‫ޑ‬ၸำύǴӵՖ೸ၸᏉᆫၲ‫ډ‬იᡏ‫ޑ‬Ҟ኱аϷᅈ ‫ى‬ԋ঩໔‫ޑ‬௃གሡ‫)؃‬Carron-!Brawley!'!Widmeyer-!1998*ǶԐයიᡏᏉᆫΚࣴ ‫ز‬а‫཮ޗ‬Γሞᜢ߯ࣁਡЈǴஒიᡏᏉᆫΚຎࣁൂ΋Ӣન຾Չ௖૸)Festinger! et! al-1950<!Lott!'!Lott-!1965*ǴϐࡕԖ೚ӭࣴ‫ޣز‬ஒიᡏᏉᆫΚયΕҺ୍य़ӛǶ Shaw)1981*ࡰрიᡏᏉᆫΚԿϿԖΟᅿόӕ‫ۺཷޑ‬Ǻ)1*იᡏ‫֎ޑ‬ЇΚǴх֖ ‫לܢ‬ᚆ໒იᡏϐΚໆǹ)2*იᡏԋ঩‫ޑ‬γ਻‫୏܈‬ᐒǹ)3*იᡏԋ঩‫ޑ‬ոΚǶ೚ӭ ࣴ‫ޣز‬ஒᏉᆫΚຎࣁԋ঩ట੮ӧიᡏϣϐำࡋ)Cartwright! '! Zander-! 1968<! Hogg-!1992<!Shaw-!1981*Ƕ! Mikalachki)1969*നԐගрიᡏᏉᆫΚёϩࣁ‫཮ޗ‬ᏉᆫΚ)social! cohesion* 19.

(29) аϷҺ୍ᏉᆫΚ)task!cohesion*‫ঁٿ‬य़ӛǴ‫཮ޗ‬ᏉᆫΚࡰ‫ࢂޑ‬იᡏԋ঩໔ᏱԖ‫ؼ‬ ӳ‫ޑ‬Γሞᜢ߯ᆶ϶ፉǴ‫཮ޗ‬ᏉᆫΚх֖ԋ঩ྎ೯ǵӝբᆶЍ࡭‫ޑ܄‬Չࣁ)Carless! '! De! Paola-2000*ǹԶҺ୍ᏉᆫΚࡰ‫ࢂޑ‬λಔԋ঩‫׫‬ΕֹԋიᡏҺ୍Ҟ኱ϐཀ ᜫǴϸࢀрიᡏԋ঩΋ଆπբǴаֹԋӅӕҞ኱‫ޑ‬ำࡋ)González! et! al/-2003*Ƕ Carron)1982*ᇡࣁᏉᆫΚࢂი໗ԋ঩ᆙஏ่ӝӧ΋ଆǴ٠ଓ‫؃‬ӅӕҞ኱ᆶҞ‫ޑ‬ ‫୏ޑ‬ᄊၸำǴ٠ЪᇡࣁȨ‫཮ޗ‬ᏉᆫΚȩᆶȨҺ୍ᏉᆫΚȩᜤаൂᐱӸӧ‫ܭ‬იᡏ ύǶ! იᡏᏉᆫΚ‫ޑ‬ෳໆБԄЬाԖΟᅿǺ!)1*!‫཮ޗ‬ीໆ‫)ݤ‬sociometry*Ǻх֖. ࢇ ‫ ݽ‬σ. ೚ӭΓሞᜢ߯௖૸ϐπ‫ڀ‬Ǵᙖҗԋ঩ୖᆶࢲ୏ࡕӣเᙁเԄୢᚒǴஒเਢ຾Չ. ҳ. ϩ‫)݋‬ֆ‫ڂݓ‬Ǵ1979*ǹ)2*ᢀჸ‫ݤ‬Ǻᢀჸԋ঩ӧიᡏ૸ፕύ٬ҔȨ‫ॺך‬ȩ‫ک‬. Ȇ ୽. Ᏸ. Ȩ‫ך‬ȩ‫ޑ‬ᓎ౗ǴаϷ໣཮૸ፕਔ‫ޑ‬рৢ౗฻БԄаᕕှიᡏᏉᆫำࡋ )shaw-1981*ǹ)3*Ծ༤Ԅໆ߄Ǻ೛ीᚒҞᡣԋ঩ឍॊ‫ځ‬Һ୍‫܄‬ᆶ‫܄཮ޗ‬ϕ୏Ǵ. Ȇ. аෳໆԋ঩໔‫ޑ‬Һ୍‫׫‬ΕำࡋᆶΓሞᜢ߯)Carron!'!Brawley-1985*Ƕ!. ŏŢŵ. ŴŪŵ. ź. ᆕӝ΢ॊࣴ‫ز‬Ǵ‫ॺך‬ёаள‫ޕ‬Ԗ‫ؼ‬ӳ‫ޑ‬იᡏਏૈаϷიᡏᏉᆫΚჹ‫ܭ‬იᡏ. ů. Ţŭ. Ŧų. ŪŰ. ᆙஏ‫ڀ܄‬ԖቹៜΚǶҁࣴ‫ز‬ϩձа΋ભၗૻ໺ኞᆶΒભၗૻ໺ኞ‫ٿ‬ᅿόӕၗૻ. Ū ů Ŗ. ŷ. ໺ኞ฼ౣኳԄǴӧୢᚒᏤӛᆛၡӝբᏢಞѳѠ΢຾Չ௲ৣ‫ۓࡰ܌‬ୢᚒᏤӛᏢಞ. ġń. ũŦůŨŤũŪġ. Һ୍ᆶֹԋБ‫ૻޑݤ‬৲໺ኞǴаᢀჸόӕၗૻ໺ኞኳԄჹ‫ܭ‬იᡏਏૈᆶიᡏᏉ ᆫΚёૈౢғ‫ޑ‬ቹៜǶ! ! ! ! ! ! 20.

(30) ಃΟക!!ࣴ‫ز‬೛ीᆶჴࡼ! ҁക൩ҁࣴ‫ز‬ϐȨࣴ‫ࢎز‬ᄬȩ ǵȨࣴ‫ز‬Б‫ݤ‬ȩ ǵ Ȩࣴ‫ز‬ჹຝȩ ǵ Ȩࣴ‫ز‬π‫ڀ‬ȩ ǵ Ȩࣴ‫ز‬೛ीᆶೀ౛ȩ ǵȨၗ਑ೀ౛ᆶϩ‫݋‬ȩ ǵȨࣴ‫؁ز‬ᡯȩ຾Չ၁ಒᇥܴ!. ಃ΋࿯!!ࣴ‫ࢎز‬ᄬ! !. ҁࣴ‫ز‬ԑӧ௖૸ӧୢᚒᏤӛᆛၡӝբᏢಞኳԄΠǴ௲ৣӧวթӚ໘ࢤୢ. ᚒᏤӛᏢಞҺ୍ϷၲԋҺ୍ϐБ‫ݤ‬ਔǴа௲ৣวթ๏‫܌‬ԖᏢಞ‫ޣ‬ϐ΋ભၗૻ໺. ࢇ ‫ ݽ‬σ ᚒᏤӛᏢಞҺ୍ϷၲԋҺ୍Б‫ݤ‬ϐΒભၗૻ໺ኞኳԄǴჹ‫ܭ‬Ꮲಞԋਏǵ‫཮ޗ‬ᆛ ҳ. ኞኳԄǴаϷวթ๏୷‫ޗܭ‬ဂୀෳϷ‫཮ޗ‬ᆛၡϩ‫פ݋‬рཀ‫ـ‬ሦ೑຾ՉӚ໘ࢤୢ. ၡϕ୏ǵიᡏᏉᆫΚаϷ૸ፕቫԛࢂց‫ڀ‬Ԗᡉ๱ৡ౦ǶԜѦǴΨ຾΋‫؁‬௖૸ό. Ȇ ୽. Ᏸ. ӕ‫܄‬ձᆶᇡ‫ޕ‬॥਱Ꮲಞ‫ޣ‬ӧୢᚒᏤӛᆛၡӝբᏢಞኳԄΠǴ௲ৣа΋ભၗૻ໺. Ȇ. ኞኳԄᆶΒભၗૻ໺ኞኳԄวթӚ໘ࢤୢᚒᏤӛᏢಞҺ୍ϷၲԋҺ୍ϐБ‫ݤ‬Ǵ. !. ź. ŴŪŵ. Ţŭ. ů. !. ŪŰ. !. Ŧų. !. ŏŢŵ. ჹ‫ܭ‬Ꮲಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ǵიᡏᏉᆫΚࢂցԖᡉ๱ৡ౦Ƕ!. ġń. ũŦůŨŤũŪġ. ! ! ! ! ! ! ! ! 21. Ū ů Ŗ. ŷ.

(31) ΋ǵġ ࣴ‫ࢎز‬ᄬკ! ҁࣴ‫ز‬ϐࣴ‫ࢎز‬ᄬӵკ 3.1 ‫܌‬ҢǴଞჹࣴ‫ࢎز‬ᄬύ‫ޑ‬Ӛᡂ໨ᇥܴӵΠǶ!. Ꮲಞԋਏ! Ѥ໘ࢤୢᚒᏤӛ Ꮲಞϩኧ!. ၗૻ໺ሀኳԄ! วթ๏‫܌‬ԖᏢಞ‫ޣ‬ϐ ΋ભၗૻ໺ኞኳԄ!. ‫཮ޗ‬ᆛၡϕ୏!. ࢇ ‫ ݽ‬σ. ҳ. วթ๏ཀ‫ـ‬ሦ೑ࡕӆ җཀ‫ـ‬ሦ೑໺ၲ๏ӕ. ᆛၡஏࡋ! Ꮙᆫ௃‫!׎‬ ύЈࡋ!. Ȇ ୽. Ᏸ. ΋‫ޗ‬ဂᏢಞ‫ޣ‬ϐ! Βભၗૻ໺ኞኳԄ!. ź. Ȇ. ŏŢŵ. ů. ġń. ũŦůŨŤũŪġ. Ŧų. ŪŰ. Ţŭ. ŴŪŵ. ङඳᡂ໨! ‫܄‬ձ! ঁΓ੝፦!. იᡏᏉᆫΚ!. Ū ů Ŗ. ŷ. ! კ 3.1!ࣴ‫ࢎز‬ᄬკ! Βǵġ ჴᡍᡂ໨ᇥܴ! Ȑ΋ȑġ Ծᡂ໨! ҁࣴ‫ز‬ϐԾᡂ໨ࣁӧୢᚒᏤӛᆛၡӝբᏢಞኳԄΠǴ௲ৣวթӚ໘ࢤ ୢᚒᏤӛᏢಞҺ୍ϷၲԋҺ୍Б‫ݤ‬ϐၗૻ໺ሀኳԄǴϩձࣁ໺಍௲ৣวթ ๏‫܌‬ԖᏢಞ‫ޣ‬ϐ΋ભၗૻ໺ኞኳԄǴаϷวթ๏‫ޗ‬ဂύཀ‫ـ‬ሦ೑ӆҗཀ‫ـ‬ ሦ೑໺ၲ๏ӕ΋‫ޗ‬ဂᏢಞ‫ޣ‬ϐΒભၗૻ໺ኞኳԄǶ! 22.

(32) ȐΒȑġ ٩ᡂ໨! ҁࣴ‫ز‬௖૸ϐ٩ᡂ໨ӵΠǺ! 1/ᏢಞԋਏǺᏢಞԋਏ߯ࡰ௦Ҕόӕၗૻ໺ኞኳԄಔձᏢಞ‫ޣ‬ӧୢᚒ ᏤӛᆛၡӝբᏢಞѳѠѤ໘ࢤᏢಞҺ୍‫ޑ‬Ꮲಞԋਏ߄౜Ƕ! 2/‫཮ޗ‬ᆛၡϕ୏Ǻ‫཮ޗ‬ᆛၡϕ୏߯ࡰ௦Ҕόӕၗૻ໺ኞኳԄಔձᏢಞ ‫ ׎ ܌ ޣ‬ԋ ‫ ཮ ޗ‬ᆛ ၡ ϐ ᆛ ၡ ஏ ࡋ )Density* ǵ ဂ ᆫ ߯ ኧ )Clustering! Coefficient* ǵ ำ ࡋ ύ Ј ࡋ )Degree! Centrality* ǵ ! ۚ ໔ ύ Ј ࡋ )Betweenness!centrality*ǵௗ߈ύЈࡋ)Closeness!Centrality*Ƕ!. ࢇ ‫ ݽ‬σ ໺ኞኳԄಔձᏢಞ‫ޣ‬ၮҔȨიᡏᏉᆫΚໆ߄ȩ‫܌‬ளϐࡼෳϩኧǶ! ҳ. 3/იᡏᏉᆫΚϷიᡏਏૈǺიᡏᏉᆫΚϷიᡏਏૈ߯ࡰ௦Ҕόӕၗૻ. ȐΟȑġ ङඳᡂ໨!. Ȇ ୽. Ᏸ. ҁࣴ‫ز‬ङඳᡂ໨ࣁ‫܄‬ձаϷᏢಞ‫ঁޑޣ‬Γ੝፦Ǵ‫ځ‬ύঁΓ੝፦೽ϩ௦. Ȇ. Ҕ njoj.nbslfst ϖεΓ਱੝፦ໆ߄Ǵஒࣴ‫ز‬ჹຝ୔ϩѦӛ‫܄‬ǵ໒‫܄ܫ‬ǵ. ź. ŏŢŵ. ௃ᆣᛙ‫܄ۓ‬ǵᝄᙣ‫܄‬аϷ‫ە‬Γ‫܄‬฻ϖᅿόӕΓ਱੝፦Ƕ!. Ŧų. ŪŰ. ŴŪŵ. ȐѤȑġ ௓‫ڋ‬ᡂ໨!. ௦Ҕόӕၗૻ໺ኞኳԄಔձᏢಞ‫ࣣޣ‬ӧӧୢᚒᏤӛᆛၡӝբᏢಞѳѠ. Ţŭ. ů. ŷ Ū ġ ů )collaborative!problem.based!learning!system-!ᙁᆀ CPBL*ѳѠ΢ֹԋ ńũ ŦůŨŤũŪġŖ. ࣬ӕ‫ޑ‬ୢᚒᏤӛᏢಞҺ୍ǴќѦᏢಞਔ໔Ψ࣬ӕǴ֡ࣁѤ‫ڬ‬ǴԶ΋ભ. ᆶΒભၗૻ໺ኞኳԄ߾௦Ҕ CPBL ѳѠ΢‫ૻޑ‬৲ύЈ຾Չ໺ሀǴCPBL ѳѠ΢‫ૻޑ‬৲ύЈёаଞჹ੝‫ۓ‬ჹຝวଌૻ৲Ƕ! ! ! ! ! !. 23.

(33) ಃΒ࿯!!ࣴ‫ز‬Б‫!ݤ‬ ΋ǵ Ў᝘ϩ‫!ݤ݋‬ ҁࣴ‫ز‬௦ҔЎ᝘ϩ‫ݤ݋‬ᘜયр೸ၸୢᚒᏤӛᆛၡӝբᏢಞѳѠᇶշᏢ ಞ‫׎܌‬ԋ‫཮ޗޑ‬ᆛၡಔԋाનǵ௖୎‫ޗ‬ဂᄽᆉ‫ݤ‬Ϸ‫཮ޗ‬ᆛၡϩ‫݋‬ෳࡋ฻Ǵ բࣁҁࣴ‫ࠔܭ୷ز‬፦ Q ‫ڄ‬ኧ‫פ‬൨ന፾ӝ‫ޗ‬ဂϐ٩ᏵǴ٠٩Ᏽ PageRank ϩ ኧ‫פ‬൨Ӛ‫ޗ‬ဂϐཀ‫ـ‬ሦ೑ǶᏵԜǴ‫׆‬ఈ௖‫࣬ز‬ၨ‫ܭ‬΋ભၗૻ໺ኞኳԄǴᙖ җཀ‫ـ‬ሦ೑໺ሀၗૻϐΒભၗૻ໺ኞኳԄǴࢂցԖշ‫ܭ‬ගϲୢᚒᏤӛᆛၡ. ࢇ ‫ ݽ‬σ. ӝբᏢಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ǵიᡏᏉᆫΚǶ!. ҳ. Βǵ ྗჴᡍࣴ‫!ݤز‬. Ȇ ୽. Ᏸ. ҁࣴ‫ز‬аਲ༜ѱࢌ୯λϤԃભ‫ঁٿ‬όӕ੤ભᏢғࣁࣴ‫ز‬ჹຝǴӧ. Ȇ. CPBL ѳѠ΢೸ၸୢᚒ૸ፕᆶϕ୏຾ՉୢᚒᏤӛᆛၡӝբᏢಞҺ୍Ƕҁࣴ‫ز‬. ŴŪŵ. ź. ŏŢŵ. ୷‫ྗܭ‬ჴᡍࣴ‫ݤز‬ஒ‫ঁٿ‬όӕ੤ભᒿᐒϩࢴࣁ௦ҔΒભၗૻ໺ኞኳԄ฼ౣ. ŪŰ. Ŧų. ‫ޑ‬ჴᡍಔǴаϷ௦Ҕ΋ભ໺ኞኳԄ฼ౣ‫ޑ‬௓‫ڋ‬ಔǶ‫ځ‬ύୢᚒᏤӛᆛၡӝբ ᏢಞѳѠஒୢᚒᏤӛᏢಞҺ୍ϩࣁȨ‫ޕ‬ȩǵȨՉ΋ȩǵǵȨՉΒȩаϷȨࡘȩ. Ţŭ. ů. ŷ Ū ġ ů Ѥঁ໘ࢤ຾ՉǴ௲Ꮲ‫ܭޣ‬ńCPBL ũ Ŧ ůࡕᆄ೛ीѤ໘ࢤҺ୍аϷୢᚒᏤӛᏢಞᡳ ŨŤũŪġŖ. ࢎǴᏢಞ‫ޣ‬аߚӕ‫؁‬БԄ຾ՉӝբᏢಞǴှ،௲ৣ೛‫ۓ‬ϐѤ໘ࢤᏢಞҺ୍Ǵ ᏾ঁᏢಞၸำ࡭ុѤ‫ڬ‬Ƕ! ҁࣴ‫ز‬аѤ໘ࢤୢᚒᏤӛᆛၡӝբᏢಞҺ୍ύȨ‫ޕ‬ȩ໘ࢤ‫ޑ‬௲ৣຑϩǴ բࣁᔠෳ‫ٿ‬ಔᏢಞ‫ޣ‬ӧటှ،ϐୢᚒᏤӛᏢಞҺ୍΢‫ޑ‬Ӄഢ‫ޕ‬᛽ࢂց‫ڀ‬Ԗ ᡉ๱ৡ౦‫ޑ‬٩ᏵǶௗ๱௲ৣ཮຾ՉୢᚒᏤӛᏢಞҺ୍ᇥܴǴ٠ЇᏤᏢಞ‫ޣ‬ ֹԋѤ໘ࢤ‫ޑ‬ୢᚒᏤӛᏢಞҺ୍Ǵ‫ځ‬ύಃ΋໘ࢤ཮ӃᡣᏢಞ‫ޣ‬ӧୢᚒᏤӛ ᆛၡӝբᏢಞѳѠ΢ճҔջਔૻ৲‫ྎޑ‬೯БԄ຾Չϕ୏ᆶ૸ፕǴᡣѳѠᇆ ໣‫୼ىډ‬Ꮲಞ‫ޣ‬۶Ԝϐ໔‫཮ޗޑ‬ϕ୏ၗૻǴௗ๱ӆճҔ୷Ӣᄽᆉ‫ݤ‬མଛࠔ 24.

(34) ፦ Q ‫ڄ‬ኧٰ،‫ۓ‬ന٫‫ޗޑ‬ဂϩಔǴӆ೸ၸ PageRank ϩኧଯեٰ،‫ޗঁ؂ۓ‬ ဂ‫ޑ‬ཀ‫ـ‬ሦ೑ǹಃΒ໘ࢤ໒‫ۈ‬ԿಃѤ໘ࢤǴ‫ٿ‬ಔ‫ڙ‬၂‫ޣ‬໒‫ۈ‬௦Ҕόӕၗૻ ໺ኞኳԄௗ‫ڙ‬௲ৣวթӚ໘ࢤୢᚒᏤӛᏢಞҺ୍ϷၲԋҺ୍Б‫ݤ‬Ǵ٠ᏵԜ ‫ٿ‬ᅿၗૻ໺ኞኳԄᕇளૻ৲຾Չ૸ፕᆶϕ୏Ǵ‫ڙ‬၂‫ޣ‬ӧૻ৲୔‫ޑ‬ϕ୏૸ፕ ௃‫׎‬ஒ೏ѳѠइᒵǶҁࣴ‫ز‬ஒаಃΒԿಃѤ໘ࢤ‫ޑ‬ୢᚒှ،ᏢಞҺ୍ϐ௲ ৣຑϩԋᕮբࣁᏢಞԋਏຑໆ‫ޑ‬٩ᏵǶ! Οǵ ୢ‫ڔ‬ፓࢗ‫!ݤ‬ ҁࣴ‫ز‬ӧ CPBL ѳѠ΢຾ՉѤ໘ࢤᏢಞҺ୍ǴࣁඓඝѳѠύό‫ޑى‬೽. ࢇ ‫ ݽ‬σ. ϩǴ‫ܭ‬ಃΒϷಃѤ໘ࢤ่‫ࡕ״‬೛ीȨჴᡏϕ୏ᓎ౗ୢ‫ڔ‬ȩ Ǵаຑ՗Ꮲಞ‫ޣ‬ӧ. ҳ. CPBL ѳѠаѦ‫ޑ‬ϕ୏ᓎ౗Ƕ!. Ȇ ୽. Ᏸ. Ѥǵ ೖፋ‫!ݤ‬. Ȇ. ࣁᕕှࣴ‫ز‬ჹຝࢂցӢཀ‫ـ‬ሦ೑໺ኞၗૻǴԶගϲ‫ځ‬ୢᚒᏤӛᆛၡᆛ. ŏŢŵ. ŴŪŵ. ź. ၡӝբᏢಞԋਏǵϕ୏ᜢ߯ǵიᡏᏉᆫΚǴаϷКၨόӕ໺ኞኳԄಔձ‫ޑ‬. ů. Ţŭ. ‫࠼ڀ׳่݀ز‬ᢀ‫܄‬Ƕ! !. Ŧų. ŪŰ. ৡ౦Ǵҁࣴ‫ز‬ҭଞჹࣴ‫ز‬ჹຝ຾Չъ่ᄬ‫܄‬ೖፋǴаᙶమୢᚒ᝛่Ǵ٬ࣴ. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. ŷ. ಃΟ࿯!!ࣴ‫ز‬ჹຝ! !. ҁࣴ‫ز‬ϐࣴ‫ز‬ჹຝࣁਲ༜ѱࢌ୯λϖԃભ‫ٿ‬੤Ӆ 59 ΓǴҁࣴ‫ز‬ஒ‫ٿ‬੤ᒿ. ᐒϩࢴࣁ௦Ҕ೸ၸཀ‫ـ‬ሦ೑໺ၲ௲ৣวթӚ໘ࢤୢᚒᏤӛᏢಞҺ୍ϷၲԋҺ୍ Б‫ݤ‬ϐΒભၗૻ໺ኞኳԄ຾ՉୢᚒᏤӛᆛၡӝբᏢಞ‫ޑ‬ჴᡍಔǴаϷ௦Ҕ௲ৣ ஒӚ໘ࢤୢᚒᏤӛᏢಞҺ୍ϷၲԋҺ୍Б‫ݤ‬วթ๏ӄ੤ϐ΋ભၗૻ໺ኞኳԄ຾ ՉୢᚒᏤӛᆛၡӝբᏢಞ‫ޑ‬௓‫ڋ‬ಔǶ! !. 25.

(35) ಃѤ࿯!!ჴᡍ೛ी!! !. ҁࣴ‫ز‬ԑӧ௖૸ӧୢᚒᏤӛᆛၡӝբᏢಞᕉნΠǴၮҔ΋ભᆶΒભ‫ٿ‬ᅿ. όӕၗૻ໺ኞኳԄჹ‫ٿܭ‬ಔᏢಞ‫ޑޣ‬Ꮲಞԋਏǵ‫཮ޗ‬ᆛၡϕ୏ࡰ኱ǵიᡏᏉᆫ Κࢂց‫ڀ‬Ԗᡉ๱ৡ౦ǶаΠଞჹჴᡍ೛ीаϷჴᡍࢬำ຾ՉᇥܴǶ! ΋ǵ ჴᡍኳԄ! ҁࣴ‫ز‬ೕჄѤ໘ࢤୢᚒᏤӛᏢಞҺ୍ǴӚ໘ࢤϐჴᡍ೛ीӵ߄ 3.1 ‫܌‬ҢǶ! ߄ 3.1!ୢᚒᏤӛᏢಞӚ໘ࢤϐჴᡍ೛ी! ୢᚒᏤӛᏢಞ໘ࢤ!!. ಔձ! !. ჴᡍኳԄ!!. ಃ΋໘ࢤ! !. ჴᡍಔ!. Ꮲಞ‫ޣ‬ӧୢᚒᏤӛᆛၡӝ. ҳ ௓‫ڋ‬ಔ!. բᏢಞѳѠ΢ճҔ૸ፕ݈. ࢇ ‫ ݽ‬σ. Ȇ ୽. Ᏸ. Ϸૻ৲ύЈ຾ՉԾҗ૸ፕ ҬࢬǶ! ௦ҔΒભၗૻ໺ኞวթ௲. Ѥ໘ࢤ!. ৣӧ೭Οঁ໘ࢤୢᚒᏤӛ. ŏŢŵ. ź. Ȇ. ಃΒ໘ࢤǵಃΟ໘ࢤǵಃ ჴᡍಔ!. ௓‫ڋ‬ಔ!. ů. Ţŭ. ġń. ௦Ҕ΋ભၗૻ໺ኞวթ௲. Ŧų. ŪŰ. ŴŪŵ. ᏢಞҺ୍ϷၲԋҺ୍Б‫!ݤ‬. ũŦůŨŤũŪġ. Ū ů Ŗ. ŷৣӧ೭Οঁ໘ࢤୢᚒᏤӛ ᏢಞҺ୍ϷၲԋҺ୍Б‫!ݤ‬. Βǵ ჴᡍࢬำ! ҁࣴ‫ز‬ೕჄϐჴᡍࢬำх֖Οঁ໘ࢤǴаΠଞჹ೭Οঁ໘ࢤ‫ޑ‬ჴᡍ೛ी຾ Չ၁ಒ‫ޑ‬ᇥܴǺ! )΋*. !߻ෳ໘ࢤ!. ࣁዴߥჴᡍ໩ճ຾ՉǴҁࣴ‫ز‬ӧ҅Ԅ຾Չ௲Ꮲჴᡍϐ߻Ǵ཮ӛ‫ٿ‬ಔ‫ڙ‬ ၂ჹຝᇥܴ᏾ঁჴᡍ‫ࢬޑ‬ำǴ٠ӛ‫ٿ‬ಔ‫ڙ‬၂‫ޣ‬ҢጄӵՖ٬Ҕ CPBL ѳѠǶ ٠‫ܭ‬ჴᡍ໒‫߻ۈ‬Ǵፎ‫ڙ‬၂‫ޣ‬༤ቪიᡏᏉᆫΚໆ߄ϷΓ਱੝፦ୢ‫ڔ‬Ǵаᕕှ. 26.

(36) ‫ٿ‬ಔ‫ڙ‬၂‫ޑޣ‬Γ਱੝፦ৡ౦Ƕ! )Β*. ၗૻ໺ኞኳԄჴᡍ!. ୖᆶჴᡍ‫ٿޑ‬ಔ‫ڙ‬၂‫ܭޣ‬ჴᡍၸำύࣣӧ CPBL ѳѠ΢຾ՉѤঁ໘ࢤӝ ीѤ‫ޑڬ‬ୢᚒᏤӛӝբᏢಞࢲ୏Ǵಃ΋‫ڬ‬ჴᡍಔϷ௓‫ڋ‬ಔӧ CPBL ѳѠ΢ ຾ՉԾҗ૸ፕǴѳѠ཮૶ᒵ‫ڙ‬၂‫ޣ‬ϕ୏௃‫ݩ‬ǴಃΒ‫ڬ‬ଆ཮٩Ᏽ‫ڙ‬၂‫ޣ‬ӧѳ Ѡ΢ϕ୏‫ݩރ‬ǴճҔࠔ፦ Q ‫ڄ‬ኧམଛ୷Ӣᄽᆉ‫ݤ‬ଞჹჴᡍಔᏢಞ‫ޣ‬຾Չ‫ޗ‬ ဂ௖୎ǶԜѦǴΨ཮வჴᡍಔύ‫פ‬൨‫ޗ‬ဂλಔύ PageRank ϩኧനଯ‫ڙޑ‬၂ ‫ޣ‬բࣁཀ‫ـ‬ሦ೑ǴԶ௓‫ڋ‬ಔ߾όբ‫ޗ‬ဂ௖୎ᆶ‫פ‬൨ཀ‫ـ‬ሦ೑Ǵ‫܌‬Ԗ‫ૻޑ‬৲. ࢇ ‫ ݽ‬σ. ໺ሀ֡೸ၸ௲ৣჹӄ੤‫ޔ‬ௗวթ‫ޑ‬БԄ຾ՉǶ!. ҳ. ಃΒ‫ڬ‬ଆԿಃѤ‫཮ڬ‬ଞჹόӕಔձ௦‫ڗ‬όӕၗૻ໺ኞ฼ౣǴჴᡍಔ௦. Ȇ ୽. Ᏸ. ‫ڗ‬Βભၗૻ໺ኞኳԄǴஒၗૻ໺ሀ๏ཀ‫ـ‬ሦ೑Ǵӆҗཀ‫ـ‬ሦ೑໺ሀ๏ӕ΋ ‫ޗ‬ဂ‫ځޑ‬дΓǹ௓‫ڋ‬ಔ߾௦‫ڗ‬΋ભၗૻ໺ኞኳԄǴ‫ޔ‬ௗҗ௲ৣஒၗૻ໺ሀ. Ȇ. ๏‫܌‬ԖᏢಞ‫ޣ‬Ǵ‫ٿ‬ಔᏢಞ‫ޣ‬ନ٬Ҕ໺ኞኳԄ฼ౣόӕѦǴ‫ځ‬Ꭹచҹࣣ΋. ࡕෳ໘ࢤ!. ź. ŴŪŵ. ů. Ţŭ. Ŧų. ŪŰ. )Ο*. ŏŢŵ. ठǶ!. ġń. Ū ů Ŗ. ŷ. ჴᡍ่‫ࡕ״‬Ǵஒፎ‫ٿ‬ಔ‫ڙ‬၂‫ޣ‬༤ቪიᡏᏉᆫΚໆ߄Ǵ٠ଞჹ‫܌‬ளϐჴ. ũŦůŨŤũŪġ. ᡍኧᏵ຾Չၗ਑᏾౛ᆶ಍ीϩ‫݋‬Ǵനࡕᘜયࣴ‫่݀ز‬Ƕ! !. ಃϖ࿯!!ࣴ‫ز‬π‫!ڀ‬ !. ҁࣴ‫ز‬௦Ҕ‫زࣴޑ‬π‫ڀ‬хࡴȨୢᚒᏤӛᆛၡӝբᏢಞѳѠȩ ǵ ȨUCINET. ‫཮ޗ‬ᆛၡϩ‫݋‬π‫ڀ‬ȩ ǵ ȨიᡏᏉᆫΚໆ߄ȩ ǵȨჴᡏϕ୏ᓎ౗ୢ‫ڔ‬ȩ ǵ ȨCig-Five mini-markers ϖεΓ਱੝፦ໆ߄ȩаϷȨъ่ᄬԄೖፋεᆜȩ ǶаΠϩձ၁ಒϟ ಏ೭٤ࣴ‫ز‬π‫ڀ‬Ǻ! ! 27.

(37) ΋ǵୢ ୢᚒᏤӛᆛၡӝբᏢಞѳѠ! ҁࣴ‫ز‬٬ҔϐୢᚒᏤӛᆛၡӝբᏢಞѳѠ)collaborative! problem.based! learning!system-!ᙁᆀ CPBL*߯җࡹ‫ݯ‬εᏢკਜၗૻᆶᔞਢᏢࣴ‫ޑ܌ز‬ኧՏ კਜᓔᄤኧՏᏢಞჴᡍ࠻‫܌‬໒วǴ೸ၸԜ΋ѳѠᏢಞ‫ޣ‬ё೸ၸߚӕ‫؁‬БԄ ຾Չୢᚒှ،ᏢಞǴΨёаϕ୏૸ፕБԄ຾ՉӝբᏢಞǶ! Ԝ΋ѳѠҗӝբԄୢᚒᏤӛᏢಞ‫س‬಍ǵ‫཮ޗ‬ᆛၡ௖୎ኳಔаϷΟঁၗ ਑৤ಔԋǴ྽Ꮲಞ‫ޣ‬຾ΕѳѠ຾ՉୢᚒᏤӛҺ୍ᏢಞਔǴ‫س‬಍ߡ཮Ծ୏૶ ᒵᏢಞ‫ޑޣ‬Ꮲಞ຾ࡋǵ૸ፕૻ৲Ϸϕ୏Ҭࢬǵբ཰इᒵ฻Ꮲಞᐕำၗ਑Ƕ. ࢇ ‫ ݽ‬σ. ٬Ҕ‫ޣ‬ϟय़фૈࢂа໘ቫԄᒧൂБԄև౜Ǵӵკ 3.2 ‫܌‬ҢǶаΠϩձᇥܴ CPBL ‫س‬಍ϟय़фૈǶ. ҳ. Ȇ. Ȇ ୽. Ᏸ Ŧų. ŪŰ. ŴŪŵ. ź. ŏŢŵ. Ţŭ. ŷ. ů. კ 3.2!ୢᚒᏤӛᆛၡӝբᏢಞѳѠᆛઠ२।! !. ġń. ũŦůŨŤũŪġ. Ū ů Ŗ. Ȑ΋ȑġ CPBL ᆛઠ२।фૈ! Ꮲಞ‫ޣ‬຾ΕୢᚒᏤӛᆛၡӝբᏢಞѳѠࡕǴ२।х֖཮঩ฦᒵǵ‫س‬಍ ϕ୏Ϸ‫ޕ‬᛽৤ϟय़Ǵӵკ 3.3 ‫܌‬ҢǶ!. 28. !.

(38) ! კ 3.3!CPBL ᆛઠ२।фૈ!!. ‫س‬಍ϕ୏Ǻх֖നཥ੃৲ǵ‫ي‬ϩ៾ज़ǵೖ࠼੮‫ق‬ǵ࿤ԃ᏿Ϸೖ࠼. Ᏸ. 3/. ࢇ ‫ ݽ‬σ CPBL ѳѠ຾ՉᏢಞǶ! ҳ. ཮঩ฦᒵǺᏢಞ‫ޣ‬٬ҔѳѠ߻໪ӃຏнǴуΕ཮঩ࡕωૈฦΕ. Ȇ ୽. 2/. ಍ी฻фૈǶ!. Ȇ. 4/. ‫ޕ‬᛽৤Ǻϟಏᜢ‫ܭ‬ҁઠ‫س‬಍ࢎᄬǴаϷୢᚒᏤӛᏢಞȨ‫ޕ‬ȩ ǵ. ŴŪŵ. ź. ŏŢŵ. ȨՉȩǵ Ȩࡘȩ‫ޑ‬೛ी౛‫ۺ‬ǴᏢಞ‫ޣ‬ё೸ၸԜ΋ᙁϟჹ‫ܭ‬ѳѠԖ߃. ů. Ţŭ. Ŧų. ŪŰ. ‫ޑ؁‬ᇡ᛽Ƕ!. ġń. ȐΒȑġ Ꮲಞ‫ޣ‬ϟय़фૈ!. ũŦůŨŤũŪġ. Ū ů Ŗ. ŷ. Ꮲಞ‫ޣ‬Ьा‫ޑ‬фૈх֖Ꮲಞ‫يޣ‬ϩǵፐำᏢಞǵᏢಞᇶշǵ‫س‬಍π‫ڀ‬ ᆶ‫཮ޗ‬ᆛၡǴӵკ 3.4 ‫܌‬ҢǴᇥܴӵΠǺ! 2/. Ꮲಞ‫يޣ‬ϩǺԜ΋фૈ཮૶ᒵх֖຾ઠਔ໔ǵฦΕइᒵǵঅ‫ׯ‬ၗ ਑ǵฦр฻Ꮲಞᐕำၗ਑Ǵග‫ٮ‬Ꮲಞ‫ޣ‬ΑှঁΓ‫ޑ‬ᏢಞइᒵǶ!. 3/. ፐำᏢಞǺୢᚒᏤӛᆛၡӝբᏢಞѳѠஒୢᚒှ،ᐕำϩࣁ Ȩ‫ޕ‬ȩ ǵ ȨՉ΋ȩǵ ȨՉΒȩ ǵ ȨࡘȩѤঁ໘ࢤǴᏢಞ‫ޣ‬໪٩ྣ௲Ꮲ‫ޣ‬ ӧ‫ঁ؂‬ୢᚒᏤӛᏢಞ໘ࢤ‫܌‬೛ी‫ޑ‬ᏢಞҺ୍຾Չୢᚒှ،Ǵ٠٩ Ᏽ௲ৣ೛ीᡳࢎֹԋ၀໘ࢤϐୢᚒှ،Ꮲಞԋ݀ൔ֋Ǵֹԋ၀໘ ࢤҺ୍ࡕωૈ຾ՉΠ΋໘ࢤҺ୍Ƕ! 29.