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Question 7S.4: Use learning curve theory to predict the number of repetitio......

Use learning curve theory to predict the number of repetitions (units) that will be needed for a trainee to achieve a unit time of 6 minutes if the trainee took 10 minutes to do the first unit and a learning curve of 90 percent is operative.

a. Use the learning table.
b. Use the log formula.

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a. The table approach can be used for the learning percentages that are listed across the top of the table, such as the 90 percent curve in this example. The table approach is based on Formula 7S–2:

T_{n} = T_{1} × \text{Unit table factor}

Setting T_{n} equal to the specified time of 6 minutes and solving for the unit table factor yields

6 min = 10 min × Unit table factor

Solving,

Unit table factor = 6 min ÷ 10 min = .600

From Table 7S.1 , under 90% in the Unit Time column, we find .599 at 29 units. Hence, approximately 29 units will be required to achieve the specified time.

b. Using the log formula,

(1) Compute the ratio of specified time to first unit time: 6 min ÷ 10 min = .600.
(2) Compute the ratio of ln learning percentage to ln 2: ln .90 ÷ ln 2 = -0.1053605 ÷ 0.6931472 = -0.1520.
(3) Find n such that n^{-.1520}\ =\ .600:\ ^{-.1520}\sqrt{.600}\ =\ 28.809. Round to 29. Hence, 29 units (repetitions) will be needed to achieve a time of 6 minutes.

The learning percentage can be estimated from data on repetition times. The procedure for doing this is illustrated in Solved Problem 2.

TABLE 7S.1 Learning curve coefficients

70% 75% 80% 85% 90%
Unit
Number
Unit
Time
Total
Time
Unit
Time
Total
Time
Unit
Time
Total
Time
Unit
Time
Total
Time
Unit
Time
Total
Time
1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
2 .700 1.700 .750 1.750 .800 1.800 .850 1.850 .900 1.900
3 .568 2.268 .634 2.384 .702 2.502 .773 2.623 .846 2.746
4 .490 2.758 .562 2.946 .640 3.142 .723 3.345 .810 3.556
5 .437 3.195 .513 3.459 .596 3.738 .686 4.031 .783 4.339
6 .398 3.593 .475 3.934 .561 4.299 .657 4.688 .762 5.101
7 .367 3.960 .446 4.380 .534 4.834 .634 5.322 .744 5.845
8 .343 4.303 .422 4.802 .512 5.346 .614 5.936 .729 6.574
9 .323 4.626 .402 5.204 .493 5.839 .597 6.533 .716 7.290
10 .306 4.932 .385 5.589 .477 6.315 .583 7.116 .705 7.994
11 .291 5.223 .370 5.958 .462 6.777 .570 7.686 .695 8.689
12 .278 5.501 .357 6.315 .449 7.227 .558 8.244 .685 9.374
13 .267 5.769 .345 6.660 .438 7.665 .548 8.792 .677 10.052
14 .257 6.026 .334 6.994 .428 8.092 .539 9.331 .670 10.721
15 .248 6.274 .325 7.319 .418 8.511 .530 9.861 .663 11.384
16 .240 6.514 .316 7.635 .410 8.920 .522 10.383 .656 12.040
17 .233 6.747 .309 7.944 .402 9.322 .515 10.898 .650 12.690
18 .226 6.973 .301 8.245 .394 9.716 .508 11.405 .644 13.334
19 .220 7.192 .295 8.540 .388 10.104 .501 11.907 .639 13.974
20 .214 7.407 .288 8.828 .381 10.485 .495 12.402 .634 14.608
21 .209 7.615 .283 9.111 .375 10.860 .490 12.892 .630 15.237
22 .204 7.819 .277 9.388 .370 11.230 .484 13.376 .625 15.862
23 .199 8.018 .272 9.660 .364 11.594 .479 13.856 .621 16.483
24 .195 8.213 .267 9.928 .359 11.954 .475 14.331 .617 17.100
25 .191 8.404 .263 10.191 .355 12.309 .470 14.801 .613 17.713
26 .187 8.591 .259 10.449 .350 12.659 .466 15.267 .609 18.323
27 .183 8.774 .255 10.704 .346 13.005 .462 15.728 .606 18.929
28 .180 8.954 .251 10.955 .342 13.347 .458 16.186 .603 19.531
29 .177 9.131 .247 11.202 .338 13.685 .454 16.640 .599 20.131
30 .174 9.305 .244 11.446 .335 14.020 .450 17.091 .596 20.727

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