2. Methods

#### 2.1 System

Our purpose of this study is to clarify human information processing in order to optimize machine learning for AI computer, which is intended to communicate interactively with human being.

At first, there were problems in collaborative learning of practical nursing class at university and we needed to find the solution. After investigating them in 2014, we have found that there was the main cause of those problems which were failing at a relationship among team members. Then, we have developed the Personalized Education and Learning Support System (PELS) in 2015 [1], which helps instructors and learners to work interactively with each other by optimizing combinations of team members from the viewpoint of personality (Figure 1).

reason of those phenomena might be influenced by not only their personality but also their cognitive traits [12], especially concerning with language information processing, because our lifestyle has been changed dramatically in digital society

From these reasons, we have been examining PELS from the viewpoints of optimizing combination for teaming members, through comparing performances and individual differences between successful and unsuccessful teams. Combinatorial optimization, however, is considered that it is difficult to find out precise solution because of discrete and non-contiguous data structure; therefore, we have decided to find solution of interactive problems by introducing the method of scaling up [13–15], which needs to be revised in the field of education. As this scaling up method should

not change the current education system at their university, we have asked

even in educational field [6, 7].

Changing scores over the years.

Figure 1.

Figure 2.

Figure 3.

53

Local search for solution of combinatorial optimization.

DOI: http://dx.doi.org/10.5772/intechopen.89681

Dual Loop Theory: Eidetic Feedback Control and Predictive Feedback Control

Local search for solution of combinatorial optimization.

The main system of PELS is Big Data processing system (1) (Figure 2), [11], which gathers students' various data, for instance. measuring their traits (2), recording their behavior, results of their performance, questionnaires, and so on, and analyzes them (3), then inform them to instructors (4) so that they can make plans for instructions included teaming members for collaborative learning interactively.

The result of students' performances at the first semester (Figure 3, upper) has been improving after introducing PELS to nursing classes, comparing the average scores with the conventional form in 2014; on the other hand, it has been dealing from 2015 to 2017 at second semester (Figure 3, right). We have supposed that the Dual Loop Theory: Eidetic Feedback Control and Predictive Feedback Control DOI: http://dx.doi.org/10.5772/intechopen.89681

#### Figure 1.

questions or the number of words in one question) in the experiment by sound voice (listening) was higher than those of by letters (silent reading). And more, there was greater dispersion of response time among subjects in presenting letters experiment than former ones. From these reasons, we predicted that there would be differentiation of individual traits of information processing for letters than those of

We have therefore examined response time by silent reading individually and found out that there were persons of Visual type (N = 12 of 98, r < 0.3) whose correlation coefficients were much lower than those of Auditory type (N = 31 of 98, r > 0.5). In addition to this, the average of response time of Visual type persons was

Moreover, we have inspected reaction time of silent reading, especially among Intermediate type (N = 55), and found out there were another pattern of information processing between Emotional and non-Emotional questionnaires [6, 7].

In this paper, we have categorized two types, Eidetic type and Adjusting type, whose correlation coefficients and response time patters were different with each other. From these viewpoints, we had formulated a hypothesis (dual loop theory) and verified them by the experiments of practical collaborative learning in nursing class. One loop might be concerning positive feedback control (PFC) and other one might be negative feedback control (NFC) [8, 9]. Epidemic type persons might have tendency of PFC while they are solving problems. On the other hand, Adjustive type might tend coordinating two cycles (PFC and NFC) [10, 11]. We had

Consequently, we would like to propose that the results of this study might help AI computer to learn machinery, thereby analyzing Big Data of various students' results and predicting their individual pattern of behavior so that it can support for personalized education, for instance, optimizing combination for collaborative

Our purpose of this study is to clarify human information processing in order to optimize machine learning for AI computer, which is intended to communicate

At first, there were problems in collaborative learning of practical nursing class at university and we needed to find the solution. After investigating them in 2014, we have found that there was the main cause of those problems which were failing at a relationship among team members. Then, we have developed the Personalized Education and Learning Support System (PELS) in 2015 [1], which helps instructors and learners to work interactively with each other by optimizing combinations of

The main system of PELS is Big Data processing system (1) (Figure 2), [11], which gathers students' various data, for instance. measuring their traits (2), recording their behavior, results of their performance, questionnaires, and so on, and analyzes them (3), then inform them to instructors (4) so that they can make plans for instructions included teaming members for collaborative learning interactively. The result of students' performances at the first semester (Figure 3, upper) has been improving after introducing PELS to nursing classes, comparing the average scores with the conventional form in 2014; on the other hand, it has been dealing from 2015 to 2017 at second semester (Figure 3, right). We have supposed that the

significantly shorter than those of Auditory type [6, 7].

revealed differentiations between the two types of behaviors.

team members from the viewpoint of personality (Figure 1).

sound voice [6, 7].

Assistive and Rehabilitation Engineering

learning.

2. Methods

2.1 System

52

interactively with human being.

Local search for solution of combinatorial optimization.

Figure 2. Local search for solution of combinatorial optimization.

Figure 3. Changing scores over the years.

reason of those phenomena might be influenced by not only their personality but also their cognitive traits [12], especially concerning with language information processing, because our lifestyle has been changed dramatically in digital society even in educational field [6, 7].

From these reasons, we have been examining PELS from the viewpoints of optimizing combination for teaming members, through comparing performances and individual differences between successful and unsuccessful teams. Combinatorial optimization, however, is considered that it is difficult to find out precise solution because of discrete and non-contiguous data structure; therefore, we have decided to find solution of interactive problems by introducing the method of scaling up [13–15], which needs to be revised in the field of education. As this scaling up method should not change the current education system at their university, we have asked

instructors and students to participate in experimental practical nursing class and agree to investigate their problems and solutions continuously [16].

introduced the theory that the perceptional system (τp), cognitive system (τc), and

As questionnaires would be the same between those presented by sound voice and letters, differences of their response time should be the same, except the duration of comprehension for problem solving (τc2) and decision making of intention (τc3), which are considered working as high-order functions. Hence, response time, which is measured in this study, is not the same as simple reaction time but same as complex reaction time. According to the theory of information processing by Card [17, 18], reaction time for encoding by perceptive organs (τc1) is correlated with the number of

The results of our exploratory experiments (over 100 subjects aged from 13 to 64) have been shown, however, that the system of encoding might not be the same among subjects. Especially, encoding system [19] for letters might be different individually, and the results of preliminary experiments which have been

conducted in the same conditions (age, sex, history of education, and environment of experiments) have imprecated the individual differentiation of cognitive system,

From these perspectives, we had introduced the model of human information processing (Figure 5) into our research. Specifically, it was predicted that there might be individual differences of information processing, depending on contents of questionnaires, between emotional and non-emotional factors [4] because of the

Consequently, the model of information processing had been reviled to Figure 6 which shows two types of cycle: (4) and (5). Along with previous examinations, the criteria would be decided for discriminating each other by analyzing correlation coefficient between response time and duration of reading (listening) or the

encoding system or image schema system (Figure 5; A2, V2) [20], which is concerning with conceptualization. Those might have effects on their comprehen-

sion (Figure 5; A3, V3) or decision making (Figure 5; A4, V4) strongly.

words, because of cycling for processing with each elements of the word.

motor system (τM) are involved in simple reaction time [17, 18].

Dual Loop Theory: Eidetic Feedback Control and Predictive Feedback Control

DOI: http://dx.doi.org/10.5772/intechopen.89681

2.3 Hypothesis

included encoding.

Figure 6.

55

Model of language information processing system.
