- 1. (Data Mining) (2000-2008 ) 837 1514 (level) 9702 9708 1028
1544 (On-Line Analytical Processing) (1) (2) (3) (4) 47
2. Explore piano students' common performing behaviours by
analyzing the pass rate associated with the pianorepertoire in the
Taiwan United Music Grade Test byYu Tai SuAssistant Professor of
the Music Department Chinese Culture UniversityAbstract This
research employed data mining technology to discover how Taiwanese
piano students present their learning outcomes during the Taiwan
United Music Grade Test, and find out their common performing
behaviours. The database was retrieved from the Music Education
Institute at the Chinese Culture University with a total of 837
candidates and 1514 registrations respectively in 9 levels during
2000-2008. Among these, we obtained the grading of 1028 examinee
with 1544 comments from the judges in 2008. We analyzed the pass
rate associated with the level, the comments from judges, and the
selection of piano repertoire including required and elective
pieces. The on-line analytical process (OLAP) introduced in this
study is to emphasize on (1)distribution of number of piano
registration by each individual level, team, age segment and pass
rate; (2)distribution of number of piano repertoire selection by
level, team, pass rate difference and other multi-dimension and
increase/decrease variances; (3)distribution of piano repertoire
selection associated with comments and level, andother
multi-dimensionand increase/decrease variances; (4)distribution of
judge comments associated with level, repertoires and other
multi-dimension and increase/decrease variances. Keywords piano,
repertoire selection, evaluation, on-line analyticalprocessing,
data mining48 3. 1. 2000 (Taiwan United Music Grade Test)
(Burrack,2002; Goolsby, 1999; Nielsen, 2008) (Goolsby) (formative
assessment) (placementassessment) (diagnostic 49 4. assessment)
(summative assessments) (Goolsby,1999) 1 (Burrack) (Burrack, 2002)
(Persellin) (Persellin, 2000) (Reynolds) (Reynolds, 2000)
(Apfelstadt, 2000) (Scott, 2004) (creating a rubric) (Victorian
Curriculum and Assessment Authority) 2006-2010 (1) 1 50 5.
(Accuracy and control) (2) (Technical skill,dexterity and security)
(3) (Tone quality,articulation and phrasing) (4) (Flexibility)
(Data mining) (J. Roiger, 2003) (Hornel, 2004) (NeuralNetworks)
(voice leading) (Cheng Fa Tsai et al., 2009) (data clustering
method) (On-Line AnalyticalProcessing; OLAP) 51 6. 2. (knowledge
discovery and datamining; KDD) (Fayyad, 1996) 1 89 97 ( 89 97 )
(datapreprocessing) (data cleaning) MicrosoftAnalysis Services
Excel OLAP 1 1 8908~9702 (stu_applystu) 5125 (sch_apply) 5231
(stu_applydetail) 12834 13 (dimension) 1. (stu_list) 2. (stu_cat)
3. (stu_item) 4. (stu_level) 5. (stu_music) 6. (stu_zip) 7.
(stu_city) 8. (stu_area)9. (stu_student)10. (stu_teacher)11.
(stu_yt) 12. (stu_oldrange) 13. (stu_score) (stu_applystu) 52 7.
(stu_apply) (stu_applydetail) (stu_list) (stu _cat) ( ) (stu_item)
(stu_level)(stu_musictype) (stu_music) (stu_zip) (stu_city)
(stu_area) (stu_student) (stu_teacher) (stu_yt)
(stu_oldrange)(stu_range) (stu_score) 1 53 8. 189-97
stu_applystu5125 stu_apply 5231 stu_applydetail 12834 stu_list1079
stu_cat 4 stu_item15 stu_level 10 stu_musictype 16 stu_music 988
stu_zip 371 stu_city18 stu_area4 stu_student 2740 stu_teacher 770
stu_yt17 stu_oldrange80 stu_range 8 stu_score 8 54 9. 155 10. ( ) 2
2 I-- 1 II-- 2 III-- 3 IV-- 4 56 11. 2000 2008 9702 9708 3
3(9702~9708) stu_musicscore_p 1028 stu_musicskill_p 1544
stu_mskillcat3 stu_mskill 8 stu_mscore 4 129 (stu_musicscore_p) 4
1028 (stu_musicskill_p) (Scott, 2004) (rubric) (VictorianCurriculum
and Assessment Authority) 2006-2010 B970909001 B B3 B970908003 A 57
12. A2 B B1 1544 4 (stu_mskillcat) A. (Performing Skill) B.
(Interpretation)C. (Stage Presence) (stu_mskill) 8 A1 A2 A3 B1 B2
B3 C1 C2 (stu_mscore) 4 4 1 2 3 4 5 A. A1 Performing Posture Skill
A2 techniques / coordination ok A3 rhythm/duration B. B1
Interpretation phrasing / articulation B2 structure /continuity B3
stylistic awarenessXX C. C1 Stageconfident Presence C2 presentation
58 13. 3. (On-Line AnalyticalProcessing) (1) (2) (3) (4) (OLAP)
(roll-up) (drill-down) (slice) (dice) (pivot) (data cube) 3.1 OLAP
5581 1,918 34% 1,541 28% 2 5 OLAP (slice) (dice) (roll-up)
(drill-down) (pivot) Microsoft Excel 2 3 4 9702~9708 5 9702~9708 59
14. 2 Cube 3 Cube 60 15. 49702~9708 Cube 59702~9708 Cube 61 16. 4.
4.1 ( 30 ) 6 1 9 ( ) 9 56.25% 41.67%( ) 77.46% 64.23%( ) 89.71%
80%( ) 62 17. 6 (drill-down) 0-10 11-20 21 7 30 ( ) ( ) 1 2 10% 3 4
24% 57 33% 8 42% 9 7 0-1011-20 7:10, 7:10, 20:20, 24:23 11-20 11 21
63 18. 7 A B 4 8 4 79.78% 6 71.43% B 1 2 3 5 7 8 9 A AB 1345 AB B
64 19. B A B CD 8 AB 9 A B A B 65 20. 9 AB 4.2 A B 2 3 A B 10
10.34% A B Op.100 , Op.68 No.8 Op.43 No.1 57.58% Op.37a BI150
53.64%66 21. 10 11 30 Op.100 30 ) Op.100 62 30 Op.39 58 47 23 43
(9702~9708 ) 11 56.25% 90% ( 30 )67 22. 11 () Burgmuller: Pastoral
(Op. 100)(30)90.00% 1 B.Bartok: Children at play 14100.00% Bi
Kuang, Tang/ Ching Chi, Chen: Liuyang River 13100.00% Burgmuller:
Innocence Op.100(62)88.52% 2 Beethoven: Scottish Salute(30)90.00%
M. Seiber: Jazz - Etudiette 8 71.43% D.Shostakovich: Barrel-Organ
Waltz 24 83.33% 3 TchaikovskySweet Fantasy 4100.00%
G.P.TelemannSongs2100.00% TchaikovskyNeapolitan Dance
Op.39(58)76.79% 4 Ying-Hai Li: Ching Fan Shen (Selected from
2080.00% Grandmothers Story) Shande Ding: Countryside 757.14% D.
Shostakovitch: Spanish Dance (47)60.00% Hu-Wei Huang: Aba Night
Club (Selected from the 5 (32)56.25% Painting of Basu Suite)
Ing-Hei Li: Playing Balls 2100.00% F.MendelassohnSong without words
(43)66.67% 6 C.Boehm: Fountain12 27.27% Tsu-Chaung Wu/Ming-Shin Tu:
Watergrass (Selected from 2100.00%68 23. drama The Mermail)
J.BrahmsWaltz Op.39 No. 15 1060.00% 7 D.B.Kabalevsky: Rondo862.50%
F.Chopin: Nocturnes Op.9 No.2 1070.00% 8 F.Schubert: Impromptu
Op.142 No.2580.00% Mozart: Turkey March, Sonata in A Major, Mov. 3
10.00% S.Prokofiev: Prelude Op.12 No.75100% 9 F.Chopin: Polonaises
Op.40 No.1450.00% () 30 4.3 12 A (Performing Skill) B
(Interpretation) C (Stage Presence) (92 ) (145 ) (528 ) 36 B1. 528
A2. 466 A3. 271 B1 (1) (2) (3) (4) (970202001) (970202006) 69 24.
12 13 3 6 8 A. B. 14 B1 A2 A3 A1 A2 A3 A1 A2 A3 (970202005)
(970201004)70 25. 13 14 71 26. 4.4 15 AB ( 30 ) B. (W.A.Mozart)
(C.F.Handel) (F.Couperin) (R.Schumann) Op.68 No.1 A. 15 AB 16 ( 30
) (FR. Kuhiau) Op.55 No.1(C ) 82% 13% 5% 72 27. (Bach) 67.5%
(W.A.Mozart) 63.16% 165. (OLAP) a) b) c) d) (1.) 56.25% 41.67% 73
28. 77.46% 64.23% (2.) 11-20 (3.) A B B A B B CD (4.) A B (5.) AB
Op.100, Op.68 No.8 (6.) Op.43 No.1 57.58% (7.) Op.100 62 Op.39 58
47 43 74 29. 23 (8.) (FR. Kuhiau) Op.55 No.1(C ) 82% (PH.E.Bach)
67.5% (W.A.Mozart) 63.16% (9.) 2000-2008 (10.) 528 466 271 (11.) 75
30. 1. A B quot; quot;(Sele ct eith er group A or group B) . 2. A B
(Pieces fro m group A and B canno t b e cro ss selected). 3. ( Ple
as e p la y th e p iec es in sequ en tia l ord er dur ing th e te
st). 4. 4 ( Th e fo r th p iece in group A or B can ber ep la c ed
b y a n y p i ec e f r o m t h e S e le c t ed ) . Category Content
Level One [A ] Group A 1. (K.Czerny) Op.599 No.19 K.Czerny: Etude
Op.599, No.19 2. (C.F.Handel) C.F.Handel: Passepied 3. (B.Bartok)
B.Bartok: Sorrow 4. (L.V.Beethoven) L.V.Beethoven: Russian Folk
SongRepertoire[B ] Group B 1. (l.Berkovich) Berkovich : Etudes 2.
(J.S.Bach) J.S.Bach: German Folk Song 3. (J.Haydn)G J.Haydn: Major
G Sonata, Movement 4 4. (Gurlitt) Gurlitt: A sad tale 1. / Bi
Kuang, Tang/ Ching Chi, Chen: Liuyang River 2. (Op.100)Selected
Burgmuller: Pastoral (Op. 100) 3. B.Bartok: Children at play Level
Two [A ] Group A 1. Gurlitt: Etudes 2. Repertoire Handael : Gavotte
3. Mozart: Rondo 4. Op. 68 No. 1 Schumann: Melodie Op.68 No.176 31.
Category Content[B ] Group B 1. Op.176 No.14 J.B.Duvermoy: Etudes
Op.176 No.14 2. F. Couperin: Gavotte 3. S. Arnold: Allegro 4.
Ai-Pin, Jin: Childrens Flowers 1. Op.100 Burgmuller: Innocence
Op.100 2. SelectedLudwig van Beethoven: Scottish Salute 3. M.
Seiber: Jazz - Etudiette Level Three [A ] Group A 1. Loschhorn:
Etudes 2. J.S.Bach: Polonaise 3. Op.55 No.1 C Fr.Kuhiau: Presto
Op.55 No.1 Major C, Movement 2 of Sonatina 4. Op.100 Burgmuller:
Ballade Op.100Repertoire[B ] Group B 1. Op.37 A. Lemoine: Etudes
Op.27 2. C.F. Handel: Courante 3. Op.36 No.3 C M.ClementiAllegro
Op.36 No.3 Major C, Movement 3 of Sonatina 4. Op.68 No.8 Schumann:
Wilder Reiter Op.68 No.8 1. D.Shostakovich: Barrel-Organ Waltz 2.
SelectedG.P.TelemannSongs 3. TchaikovskySweet Fantasy Level Four [A
] Group A 1. Op.46 No.1 HellerEtudes Op.46 No.1Repertoire 2.
J.S.BachAllemande 3. Op.36 No.5 G 77 32. Category Content
M.ClementiRondo Op.36 No.5 Major G, Movement 3 of Sonatina 4.
F.GriegGrandmothers Minuet[B ] Group B 1. Op.849 No.12 Carl
CzernyEtudes Op.849 No.12 2. N0.4 J.S.BachTwo-Part Inventions No.4
3. Op.20 No.1 G J.L.DussekRondo Op.20 No.1 Major G, Movement 2 of
Sonatina 4. Op.12 No.2 F.Grieg: Waltz Op.12 No.2 1. Shande Ding:
Countryside 2. Op. 39() SelectedTchaikovskyNeapolitan Dance Op.39
3. (quot;quot;) Ying-Hai Li: Ching Fan Shen (Selected from
Grandmothers Story) Level Five [A ] Group A 1. Op.109 Burgmuller:
Agitato Op.109 2. No.14 Bach: Two-Part Inventions No.14 3. (D )
J.Haydn: Finale (Major D, Sonata, Movement 3) 4. - Op.37a
TchaikovskyApril- Snowdrop Op.37aRepertoire[B ] Group B 1. Op.636
No.6 Carl Czerny: Etudes Op.636 No.6 2. BWV.937 J.S.Bach: Prelude
BWV.937 3. -D D.B.KabalevskyVariation in D Major Toccatta 4. BI150
F.ChopinWaltz BI150 1. (quot;quot;) Ing-Hei Li: Playing Balls
(Miscellaneous Sketch IV) 2. (quot;quot;) SelectedHu-Wei Huang: Aba
Night Club (Selected from the Painting of Basu Suite) 3. D.
Shostakovitch: Spanish Dance Level Six [A ] Group ARepertoire 1.
Op.299 No.11 Carl Czerny: Etudes Op.29 No.11 78 33. Category
Content 2. Op.14 G.P.Telemann: Bourree Op.14 3. G Hobxv127 J.Haydn:
Major G, movement 1 of Sonata, Hobxv127 4. Op.43 No.1 F.Grieg:
Butterfly Op.43 No.1[B ] Group B 1. Op.636 No.10 Carl Czerny:
Etudes Op.636 No.10 2. No.10 J.S.Bach: Two-Part Inventions No.10 3.
G K283 W.A.Mozart: Major G Sonata, Movement 1, K283 4. Lu-Ting He
:The Cowherd's Flute 1. C.Boehm: Fountain 2.
SelectedF.MendelassohnSong without words 3. /(quot;quot;)
Tsu-Chaung Wu/Ming-Shin Tu: Watergrass (Selected from drama The
Mermaid) Level Seven [A ] A Group 1. Op.299 No.19 Carl Czerny:
Etudes Op.299 No.19 2. No.15 J.S.BachThree-Part Sinfonias No.15 3.
K.570 bB W.A.MozartRondo K.570 Major bB, Sonata, Movement 3 4.
Op.38Nr.2 F.MendelassohnSong without wordsRepertoire[B ] Group B 1.
No.17 J.B.CramerEtudes No.17 2. (D )L463(K430) D.ScarlattiSonata (
Major D) L463(K430) 3. ()WoO80 L.V.BeethovenVariation (VI)WoO80 4.
(quot;quot;) Hu-Wei Huang: Spring in Chengdu (Selected from the
Painting of Basu Suite) 1. D.B.Kabalevsky: Rondo 2. ()
SelectedPei-Shin, Chen: Mai Za Huo (Guangdong Folk Melody) 3. Op.39
No. 15 J.BrahmsWaltz Op.39 No. 15 Level Eight [A ]79 34. Category
ContentRepertoire Group A 1. Op.299 No.24 Carl Czerny: Etudes
Op.299 No.24 2. --E BWV817 J.S.Bach: Gigue French Suite- No.6,
Major E BWV817 3. D K311 W.A.Mozart Major D Sonata, Movement 1,
K311 4. F.Chopin Waltz Op.64 No.1[B ] Group B 1. Op.64 No.1
J.B.Cramer: Etudes Op.64 No.1 2. No.13 J.S.Bach: Three-Part
Sinfonias No.13 3. C K330 W.A.Mozart: Major C Sonata, Movement 1
K330) 4. Chien-Chung, Wang: Embroidering a golden silk banner 1.
k.331 A Mozart: Turkey March Major A Sonata, movement 3 2. Op.9
No.2 SelectedF.Chopin: Nocturnes Op.9 No.2 3. Op.142 No.2
F.Schubert: Allegretto Op.142 No.2 Level Nine [A ] Group A 1.
Op.740 No.41 Carl Czerny: Etudes Op.740 No.41 2. G () J.S.Bach:
Prelude & Fugue in G Major book II 3. a K310 Mozart: minor a ,
sonata, movement 1 K310 4. Villa-Lobos: PayasoRepertoire[B ] Group
B 1. Op.72 No.5 M.Moszkowsky: Etudes Op72 No.5 2. (G ) L487(K125)
D.Scarlatti: Sonata (Major G) L487(K125) 3. F Op.2 No1
L.V.Beethoven: minor F sonata, movement 1, Op.2 No.1 4. ()
Pei-Shin, Chen: Bold Thunder in Drought (Guangdong Folk Melody) 1.
Op.40 No.1 F.Chopin: Polonaises Op.40 No.1 2. Op.12 No.7
SelectedS.Prokofiev: Prelude Op.12 No.7 3. Cl.Debussy: Clair de
Lune80 35. Apfelstadt, Hilary. quot;First Things First: Selecting
Repertoire.quot; Music Educators Journal(2000): 19-46.Burrack,
Frederick. quot;Enhanced Assessment in Instrumental Programs.quot;
Music EducatorsJournal 88.6 (2002): 27-32.Cheng Fa Tsai, Yu Tai Su,
Chiu Yen Tsai, Chun Yi Sung. quot;Discovering Potential
MusicalInstruments Teachers Using Data Clustering Approach.quot; In
10th WSEAS Int.Conf.on Neural Network. Prague, Czech Republic,
2009.Fayyad, U. M., et al. Advances in Knowledge Discovery and Data
Mining. MIT pressCambridge, Mass, 1996.Goolsby, Thomas W.
quot;Assessment in Instrumental Music.quot; Music Educators
Journal86.2 (1999): 31-50.Hornel, Dominik. quot;Chordnet: Learning
and Producing Voice Leading with NeuralNetworks and Dynamic
Programming.quot; In Journal of New Music Research,
387-97:Routledge, 2004.J.Roiger, Richard, and Michael W.Geatz. Data
Mining. Pearson Education Taiwan Ltd,Addison Wesley, 2003.Nielsen,
Siw Graabraek. quot;Achievement Goals, Learning Strategies and
InstrumentalPerformance.quot; Music Education Research 10.2 (2008):
235-47.Persellin, Diane. quot;The Importance of High-Quality
Literature.quot; Music Educators Journal87.1 (2000):
17-18.Reynolds, H. Robert. quot;Repertoire Is the Curriculum.quot;
Music Educators Journal 87.1(2000): 31-33.Scott, Sheila.
quot;Evaluating Tasks for Performance-Based Assessments: Advice for
MusicTeachers.quot; General Music Today 17, no. 2 (2004): 17-27.81
36. Victorian Curriculum and Assessment Authority. quot;VCE Music
Solo PerformanceAssessment Handbook 2006-2010.quot; Australia:
Victorian Curriculum andAssessment Authority, 2008. 82