: . . , . . , . .
- 1
2005 . . . *, . . **, . . ***
* , , . ,
** , ,
*** , . ,
( ) () , - ( ), . ( ), ( WizWhy). , , ( ). (116 IQ), .
: , , , ,
- (IQ) . , ( , - ) , " " (IQ) 0.30 [8].
( , , ). , . , ( IQ ), "" ( , .. , , ) " ", IQ, . , (110 - 119) [18].
|
|
, , :
1. , "" [13, 14, 16 .]. ( , ): - ;
1 ( 2384.2003.06), ( N 03 - 06 - 80128).
. 29
, ; [17].
2. , , , , , , , [7, 9].
3. , , " " (deliberate practice) [I, 15].
4. , [6].
, ( ) .
- ( , , , , ..), , , . , .
- , . ( ); ( ); ; ; ; .. [9, 10].
, "" , [10].
|
|
, / , , " " ( - " -" " "); / - ( "-" ""); / - - - ( "" "") [ ].
:
1. - ( ) ( , , , , ).
2. () / . .
3. () .
:
1. ( ) ( /, /, / ) -
. 30
- - , .
2. , - ( ).
. . 21 27 ( 127 .). - .
, - : ; 30 ; ; ; ; ; . / , . "" 39 ., ( 20 19 ).
(.. "" ""), (88 ., 22 66 ).
|
|
. ( . . ) (. [2]). : 1) 2 " " ( , , IQ); 2) 6 " " ( , IQ); 3) , IQ.
, .
1. " " . ( ) / ( - , ; - ).
2. " " . / ( - , ; - ).
3. "- " . / ( - , ; - "" "" T2 /T1 - - ).
(ANOVA, SPSS, GLM), WizWhy.
, .. , , - - Data Mining. Data Mining (discovery - driven data mining) (), . , , , [4].
Data Mining "if-then" - "-". WizWhy, , ,
. 31
. ( , ) . 6. WizWhy "if-then" ( "if-then-not"), "" . , - [ ].
|
|
90- XX . , , , , [4, 12]. , , (, ), .
" " : IQ (r = 0.56, p < 0.0001); (r = 0.44, p < 0.0001); (r = 0.50, p < 0.0001).
, " " , (r = 0.24, p < 0.01) (r = -0.20, p < 0.05); (r = -0.27, p < 0.01). , , , .
( - 0, - 1) " " (r = -0.26, p < 0.01). IQ (r = -0.30, p < 0.01) (r = -0.30, p < 0.01). (r = -0.33, p < 0.0001) (r = 0.28, p < 0.01); (r = 0.24, p < 0.02); 1- "" (r = -0.24, p < 0.02). , - - , ( ), , .
, , - . " " ( ).
, IQ (F (l) = 15.37, p < 0.0001; "" 122.2; - 107.1) (F (1) - 3.97, p < 0.05; "" 119.2; - 108.2). .
, (p < 0.0001) , . " " .
(F (l) = 8.8, p < 0.03). , - . , IQ (F = 4.64 (1), p < 0.05; - 118.5; - 112.6); , IQ ( 6; F = 9.36 (1), p < 0.03; - 122.3; - 112.2); . - - (F = 12.43, p < 0.001; - 49.8 ; - 34.0 ) (F = 7.67 (1), p < 0.01; - 7.6;
|
|
. 32
- 12.5); (F = 8.70 (1), p < 0.05; - 7.24 ; - 12.21 ); 1- (F = = 11.4 (1), p < 0.001; - 43.0 ; - 38.6 ).
- . (, , , ..).
. WizWhy 51 "if... then". (n = 127). , [4]. , WizWhy, .
1 - 4 ( 0.922 0.969; p < 0.00001) ( 68 - 70 .) . : , , ( 88 116), .
5 - 10 ( 0.857 0.917; p < 0.00001 p < 0.0001) ( 10 - 12 .). : , ( 5); ( 6); ( 7); ( 8); 3- ( 9) ( 10), . ( , ), - .
12,14,19, 20, 22, 30, 31 ( 0.950 1.00; p < 0.001 p < 0.01) ( 17 - 22 .). : , ( 88 119) ( ), ( ) ( ), . , 1 - 4: - .
11,13,15, 23, 24 ( 0.955 1.00; p < 0.01) ( 17 - 22 .). : , 92 116 ( 1- 2- ) , . : - , -, .
16,17,18 ( 0.95 1.00; p < 0.01) . : ,
. 33
IQ 89 - 92 116 - 119, .
21,22,27 - 29 , (, 0...16, -26...63 ). ( /).
32 - 51 (p < 0.1). 10 - 20 . 0.90 - 1.00, . , 40, 41, 43 - 45, 50 , . : ( 3.80...6.80 ; - 18.0...25.0 ; -17.0...38.0; - 9.0...25.0 ), . , , , [10].
, : 51 45 "" - , .. ( if-then-not). , ( , ), , - ( ).
, () , : -, ( ) IQ -, ; -, , .
: IQ . ( 6; ), , .
, . , , - - , - , , .. , "" , , .
, - . -, - ; -, - ; -, - "" ( ) "" ( , , ).
-, , -
. 34
, , . . , , , , , [5].
, - ( ) - , , .
( ), . , / - .
, , , , (" IQ, "). , WizWhy : , , IQ, .
WizWhy . : , IQ ( IQ- 88 - 116; - 92 - 116; - 74 - 119) ( 1 - 4). : " " , -
( , , ) . : - . : , .
: , ( 5 - 10). , , - .
- , . , , WizWhy, - , .
" " . . : , [3]. " " , IQ 116 . ( 19 ( 31) ). , IQ .
, , , . , [11]. , , , ("") ( 5 - 10),
. 35
: () , , . , -, ( ) .
: ( 13,15,16,19) "" ( 2 ). , , .
, , , , . , , WizWhy, .
() - - . , , , , , . , , IQ, , .
" ", , , , . , " " 116 IQ. , IQ , IQ. - , . , , , , IQ, (.. ).
, () WizWhy .
WizWhy
1) If IQ is 88.00... 116.00 (average = 104.69)
Then
ycnex is not 1
Rule's probability: 0.958
The rule exists in 68 records.
Significance Level: Error probability is almost 0
6) If nol is 0
and time is 46.00... 74.00 (average = 59.50)
and W2 is 1.40... 7.10 (average = 3.66)
and nk2 is 0.43... 0.85 (average = 0.67)
Then
ycnex is 1
Rule's probability: 0.917
The rule exists in 11 records.
Significance Level: Error probability < 0.00001
20) If IQ6 is 80.00... 119.00 (average = 102.80)
and inf is 30.00... 42.00 (average = 35.10)
Then
ycnex is not 1
Rule's probability: 0.950
The rule exists in 19 records.
Significance Level: Error probability < 0.01
:
IQ - ( );
. 36
IQ 6 - ( 6);
0 - ; 1 - ;
1 - ();
time - ;
W2 - ;
nk2 - ;
inf- .
1. . . . 5- . .: , 2002.
2. . ., . . (.). . .: - , 1987.
3. . . : " " // . . 1998. . 19. N 2. . 61 - 70.
4. ., . Data Mining: . .: , 2001.
5. . . .: , 1976.
6. . : , . .: -, 2002.
7. / . . . .: , 2002.
8. . , , // . 1999. N 11. . 19 - 27.
9. . . : . 2- ., . .: , 2002.
10. . . : . .: , 2004.
11. . ., . . / / IQ // . . 2002. . 21. N 4. . 46 - 56.
12. . . MATLAB // Exponenta pro. 2003. N 2. . 9 - 14.
13. Ceci S.J., Liker J. Academic and nonacademic intelligence: An experimental separation // Practical intelligence: Nature and origins of competence in the everyday world / Eds. RJ. Steinberg, R.K. Wagner. N. Y.: Cambridg Univ. Press, 1986. P. 119 - 142.
14. Chi M.T.H., Feltovich P.J., GlaserR. Categorization and representation of physics problem by experts and novices // Cognitive science. 1981. N 5. P. 121 - 152.
15. Ericsson K.A. The acquisition of expert performance // The road to excellence / Ed. R.A. Ericsson. Hillsdale, N.J.: Erlbaum, 1996. P. 1 - 50.
16. Glaser R. Education and thinking: The role of knowledge // Amer. Psychologist. 1984. V. 39 (2). P. 93 - 104.
17. Klemp C.O., McClelland D.C. What characterizes intelligent functioning among senior managers? // Practical intelligence: Nature and origins of competence in the everyday world / Eds. R.J. Sternberg, R.K. Wagner. N.Y.: Cambridg Univ. Press, 1986. P. 31 - 50.
18. Schneider W. Acquiring expertise: Determinants of exeptional performance // International handbook of research and development of giftedness and talent / Eds. K.A. Heller et al. Oxford: Pergamon, 1993. P. 311 - 324.