IEEE Systems, Man and Cybernetics Magazine - January 2021 - 22

Error (%)

choice, the data regarding the wrong answers (error rate)
made by the users should be analyzed. To reach statistical
significance, these automatically recorded data were combined across the three games that dealt with single words.
As explained previously, the letter number was selected
for the weight (difficulty) parameter in the optimization
model. To evaluate this decision, the error rates (percentage of user errors in the number of presentations) for each
letter number are presented in Figure 4. The trend line
indicates that the errors increased with word length. However, as it appears in the diagram, the word length is not a
perfect predictor of word difficulty. It is confirmed by the
linear regression analysis that the letter number is not a statistically significant predictor of word difficulty

50
45
40
35
30
25
20
15
10
5
0

1

2

3
4
5
Word Length (Letters)

6

Figure 4. The percentage of errors for different word

lengths.

50

Error (%)

40
30
20
10
0

12

19

31

33 40 45 47
Word Frequency

49

51

53

Figure 5. The percentage of errors for each frequency

level.

Table 3. The situational motivation scale
for the two usability studies.
Motivation Type

First Study

Second Study

Intrinsic motivation

77%

75%

Identified regulation

75%

70%

External regulation

37%

56%

Extrinsic regulation

40%

38%

22	

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 2021

(F (1, 4) = 3.90, p = 0.119), and it accounts for only 36.7% of
the variability in error percentages. The lack of statistical
significance in the linear regression model may be due to
the low number of samples (in this case, the letter numbers).
Moreover, the same analysis was carried out for the frequency of words. The results demonstrated that the least frequent words produce more errors. Figure 5 portrays this
inverse relationship between the word frequency and the percentage of errors. The results of the linear regression analysis
indicated that word frequency is a statistically significant predictor of error percentage (F (1, 17) = 16.64, p = 0.001).
However, it only accounts for 46.5% of the variability
i n error percentages. Therefore, neither word length
(R-squared = 36.7%) nor word frequency (R-squared =
46.5%) is a sufficiently accurate predictor of word difficulty. A combination of lexical information such as the word
length, word frequency, and consistency of mapping
between graphemes and phonemes as well as the age of
the player is needed to build a more reliable word difficulty
prediction model. It is also worth mentioning that for Figure 5, the frequency levels that had less than 20 presentations were discarded because they were too sparse to be
reliable as an average of the error rates.
Furthermore, additional data were collected from the
questionnaires presented to the users. The usability was
assessed by the SUS test. Each question was represented
by a score ranging from zero to four. Zero indicates the
lowest usability, while four indicates the highest usability.
The overall usability score was calculated for both experiments separately. The obtained scores were 75% for the
first study and 80% for the second study. This increase was
expected after improving the games according to the feedback received from the first study.
The second questionnaire in this study was the SIMS for
the evaluation of the variables of intrinsic motivation, identified regulation, external regulation, and amotivation. The
scores for each of these four variables were calculated and
are presented in Table 3. In general, participants were more
intrinsically motivated in the first study. The biggest gap
between the first and the second study comes from the
external regulation, which is significantly higher for the
second usability study. This was expected as the average
age of the participants in the second study (23.9) was significantly higher than the first study (14.4).
Finally, in the last questionnaire, the participants were
asked to rate the games based on their level of difficulty
and amusingness. The most difficult game was AWSR
(Thanksgiving), with a rating of 3.75 out of 5, and the easiest game was APR (Easter), with a rating of 4.5. The least
amusing game was AWD (Christmas), with a rating of 3.81,
and the most amusing game was AWMR (Halloween), with
a rating of 4.25.
Conclusion and Research Perspectives
Reading is an indispensable skill in modern life, and its
importance cannot be overemphasized. Dyslexia as a



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