Author's personal copy Information Processing and Management 45 (2009) 200–215
Contents lists available at ScienceDirect
Information Processing and Management journal homepage: www.elsevier.com/locate/infoproman
On-line learning performance and computer anxiety measure for unemployed adult novices using a grey relation entropy method Jyh-Rong Chou a,*, Hung-Cheng Tsai b a b
Department of Product Design, Fortune Institute of Technology, Kaohsiung 831, Taiwan Graduate Institute of Industrial Design, National Kaohsiung First University of Science and Technology, Kaohsiung 811, Taiwan
a r t i c l e
i n f o
Article history: Received 5 April 2008 Received in revised form 3 December 2008 Accepted 4 December 2008 Available online 13 January 2009
Keywords: On-line learning Computer anxiety Human information behavior Individual differences Grey relation entropy
a b s t r a c t On-line learning is an asynchronous computer-based learning mode that allows learners to learn anytime and anywhere in their own environment using information and communication technology. It can be considered as a way to bridge the digital gap. How a computer novice performs in such virtual and asynchronous learning environments is an interesting issue in human–computer interaction research. This paper presents the results of a study to investigate on-line learning performance and computer anxiety for unemployed adult novices. In this study, we propose a novel idea that integrates the concept of Shannon entropy into a grey relational analysis model. The proposed method was used to measure human information behavior in relation to on-line learning performance and computer anxiety. A total of 115 unemployed adults voluntarily participated in the experimental study. All experimental subjects were divided into groups according to individual differences in gender, age ranges, educational levels, and learning performances. Taking advantage of the grey relation entropy operation, we derived the perceptive correlations among different classified groups in terms of the accessibility of on-line learning and computer anxiety scales, respectively. Through the empirical study, certain on-line learning characteristics were also identified. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction With the incredibly fast paced advances being made in information technology, computers have emerged into the mainstream of present day society. In the meantime, the rapid development of information and communication technology (ICT) has revolutionized the learning environment and made a great impact on our educational systems. Teaching and learning are no longer restricted to traditional classrooms, and are gradually being replaced by teaching methods with a greater emphasis on ICT. It has become a popular trend to learn via educational material that is presented on computers. On-line learning is an asynchronous computer-based learning mode that allows learners to learn anytime and anywhere in their own environment using ICT. With the Internet boom since the mid-1980s, the concept of on-line learning has spread broadly. Despite the potential enhancements resulting from technology use, the transition into this novel learning mode might be challenging. Online learning requires many mental and cognitive processes. During the learning process, learners’ mental models change, marked by increased familiarity with new concepts and more complete connections between their learning experience and prior knowledge.
* Corresponding author. Fax: +886 7 7887851. E-mail addresses:
[email protected],
[email protected] (J.-R. Chou). 0306-4573/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.ipm.2008.12.001
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Many users feel anxious when dealing with information systems, especially when initially interacting with them. In sociological and psychological aspects of human information behavior, computer anxiety is one of the most important subjects that has been commonly recognized by researchers and largely documented in the literature. Computer anxiety is a feeling of discomfort, stress, or fear experienced when confronting computers (Brosnan, 1998). Studies of computer anxiety have mostly used cognition, context, and personality at the individual level as explanatory variables such as age, gender, education, and computer experience (e.g. Korukonda, 2007; Todman & Day, 2006). In addition to the socio-demographic features, socio-economic factors have been found to be associated with people’s computer anxiety (e.g. Bozionelos, 2004; Martin, Stewart, & Hillison, 2001; Mikkelsen, Øgaard, Lindøe, & Olsen, 2002). Previous research in the literature has found evidence for a relationship between computer anxiety and computer performance (e.g. Chou, 2001; Smith & Caputi, 2001). Computer anxiety directly influences a negative affective response to the computerized working situation and the virtual community (Brosnan, 1998; Desai, 2001). Since the 1980s, a great deal of tools have been developed for measuring the users’ affective and behavioral scales such as CAIN – the Computer Anxiety Index (Montag, Simonson, & Maurer, 1984), CARS – the Computer Anxiety Rating Scale (Heinssen, Glass, & Knight, 1987), and CAS – the Computer Anxiety Scale (Marcoulides, 1989). Computer anxiety is usually measured by a self-reported questionnaire associated with Likert-type items given before or after completion of a cognitive test. Traditionally, statistical analysis is regularly employed to test the significant level of experimental hypotheses. Multiple regression/correlation analysis (MRC) is the most commonly used statistical technique in behavioral sciences (Cohen, Cohen, West, & Aiken, 2003). It is generally used to summarize data as well as to model the functional relationships between quantification and characterization of experimental research. However, a multiple regression model has several shortcomings: for instance, it imprecisely assumes that all predictors are linearly related to each other and also has a statistical limitation on the number of explanatory variables (Tsuchiya, Maeda, Matsubara, & Nagamachi, 1996). In such cognition-based, Likert-typed, and self-reported measures, uncertain, imprecise, or incomplete data caused by human mistakes, recording errors, or arbitrary guesses may produce unreliable results. To improve the strength of the statistical analysis models, a number of new approaches based on artificial intelligence methods have been proposed and employed in behavioral science research. These new approaches include fuzzy set theory, genetic algorithms, neural networks, and grey system theory. They are regarded as powerful mathematical tools for formal modeling, reasoning, and computing, especially for human perception measuring. Human behavior involves complex cognitive, affective, and experience-based processes. It is difficult to be objectively and uniformly measured by a conventional research approach. Sonnenwald and Pierce (2000) indicated that human information behavior should be researched by investigating it as a process, taking into account cognitive, affective, social and contextual factors and drawing on research from multiple disciplines to increase our understanding. In this study, we proposed a new correlation analysis method that integrates the concept of Shannon entropy into a grey relational analysis model. The proposed method was used to analyze unemployed adult novices’ on-line learning performance and computer anxiety with respect to individual differences in gender, age, and educational level. Through the empirical study, we can also identify certain on-line learning characteristics of the specific user population. 2. The proposed method 2.1. Grey system theory and grey relational analysis (GRA) Grey system theory was proposed by Deng (1982). It is a practical technique for dealing with problems that are characterized by uncertainty, and which have poor or incomplete information. A grey system is defined as a system containing unknown information presented by grey numbers and grey variables. Grey relational analysis (GRA) is an important technique of grey system theory, which is suitable for solving the complicated interrelationship between multiple factors and variables. In comparison with the conventional data analysis methods which require larger size and normal distribution of samples (e.g. factor analysis, cluster analysis, and discriminant analysis), GRA possesses certain applied advantages. It involves the following features: (1) GRA is a serial model of non-functional type; (2) It is easy to calculate using GRA; (3) The size of sample in GRA is not critically important; (4) The data in GRA has no need to be restricted to a specific distribution; and (5) Using GRA will not lead to a result that conflicts with quantitative analysis (Liu & Hong, 1997). GRA is an analysis of the geometric proximity between different discrete sequences within a system. As shown in Fig. 1, the function x0(k) denotes the reference sequence, and x1(k) and x2(k) denote the compared sequences. From the viewpoint of geometric proximity, the similarity between x0(k) and x1(k) is greater than that between x0(k) and x2(k). It means the relational degree between x0(k) and x1(k) is higher while that between x0(k) and x2(k) is lower. In statistical terms, the difference between x0(k) and x2(k) is more significant than that between x0(k) and x1(k). The mathematics of GRA is derived from space theory. Let X be a factor set of grey relation, x0 = (x0(1), x0(2), , x0(n)), x0 e X represents the reference sequence, and xi = (xi(1), xi(2), , xi(n)), i = 1, 2, , m, xi e X represent the compared sequences. x0(k) and xi(k) represent the respective numerals at point k for x0 and xi. If the average relation value of c(x0(k), xi(k)) is a real number, then it can be defined as (Deng, 1989):
cðx0 ; xi Þ ¼
n 1X cðx0 ðkÞ; xi ðkÞÞ n k¼1
ð1Þ
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Fig. 1. Similarity and difference of sequence data.
The average value of c(x0(k), xi(k)) must satisfy the following four axioms: Axiom 1. Norm interval: 0 < cðx0 ðkÞ; xi ðkÞÞ 6 1; 8ðkÞ; cðx0 ðkÞ; xi ðkÞÞ ¼ 1 () x0 ðkÞ ¼ xi ; cðx0 ðkÞ; xi ðkÞÞ ¼ 0 () x0 ðkÞ; xi ðkÞ 2 ; Axiom 2. Duality symmetry: cðx0 ðkÞ; xi ðkÞÞ ¼ cðxi ðkÞ; x0 ðkÞÞ () X ¼ fx0 ; xi g: Axiom 3. Wholeness: cðxi ðkÞ; xj ðkÞÞ–cðxj ðkÞ; xi ðkÞÞ () X ¼ fxj jj ¼ 0; 1; ; ng; n P 2: Axiom 4. Approachability: c(x0(k), xi(k)) decreases when |x0(k) xi(k)| increases. If the four axioms are satisfied, c(x0, xi) is then designated as the grey relational grade in xi corresponding to x0. c(x0(k), xi(k)) represents the grey relational coefficient between x0 and xi at point k. It can be expressed as:
cðx0 ðkÞ; xi ðkÞÞ ¼
mini mink jx0 ðkÞ xi ðkÞj þ fmaxi maxk jx0 ðkÞ xi ðkÞj jx0 ðkÞ xi ðkÞj þ fmaxi maxk jx0 ðkÞ xi ðkÞj
ð2Þ
where f e [0, 1] represents the distinguishing coefficient used for scaling the distinguishing value of the grey relational grade; i = 1, 2, ... , m; k = 1, 2, ... , n. The grey relational grade, c(x0, xi), is a time-varying or object-varying magnitude of relationship. If the grey relational order is c(x0, xj) c(x0, xi), then the compared sequence xj has characteristics closer to those of the reference sequence x0 than the compared sequence xi does. 2.2. Grey entropy Traditional GRA is based on the geometric relationship between sequence data in the relational space. The grey relational grade is derived by averaging the obtained grey relational coefficients out. Such averaging operations may make the measure biased if the evaluations are deficient in quantity (i.e. the number of data point k is relatively small) and inconsistent in quality (i.e. the distribution of the grey relational coefficient, c(x0(k), xi(k)), is extremely irregular). To improve these shortcomings, the entropy concept is used in the study. Entropy was adapted for information theory by Shannon as a measure of information comprised in a given amount of signals. When drawing an analogy between human perception and a communication channel, taking input and generating output with the overlap being the amount of transmitted information, the amount of information will follow asymptotic behavior. According to Shannon (1948), the amount of statistical information P associated to a given relative abundance vector p is measured by the quantity Hn ðp1 ; p2 ; . . . ; pn Þ ¼ nj¼1 pj ln pj . Pn Let X be a sequence of grey relation, X ¼ ðx1 ; x2 ; . . . ; xn Þ; 8j; xj P 0; j¼1 xj ¼ 1, the grey entropy of sequence X can be defined as:
EðxÞ ¼
n X
xj ln xj
ð3Þ
j¼1
where xj is characterized by attributive information. The grey entropy possesses all axiomatic properties of Shannon entropy such as non-negativity, continuity, symmetry, and normality. Besides, it must have a maximum value in order to eliminate the randomness and uncertainty of the attributive information. The maximum of grey entropy can be obtained as:
EM ðxÞ ¼ ln n
ð4Þ
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2.3. Grey relation entropy (GRE) To combine the grey entropy with the GRA, the operation of grey relational coefficient must be modified as below:
Rr ¼ cðx0 ðkÞ; xi ðkÞÞ;
k ¼ 1; 2; . . . ; n
Map : Rr ! Pr
cðx0 ðkÞ; xi ðkÞÞ ; Ph 2 Pr ; h ¼ 1; 2; . . . ; n k¼1 cðx0 ðkÞ; xi ðkÞÞ
ð5Þ
P h ¼ Pn
where Rr represents the distribution of the grey relational coefficient; Pr represents the mapping of Rr ; Ph is the mapping value, representing the distribution density of the grey relational coefficient. Based on Eqs. (3) and (5), the GRE can be expressed as:
EðRr Þ ¼
n X
Ph ln Ph
ð6Þ
h¼1
The GRE grade in xi corresponding to x0 is then defined as:
P EðRr Þ nh¼1 Ph ln Ph eðx0 ; xi Þ ¼ ¼ EM ðxÞ ln n
ð7Þ
The GRE can detect data regularity and characterize its asymptotic behavior. As shown in Fig. 2, Rr1 ; Rr2 , and Rr3 represent three sets of the grey relational coefficient distribution, respectively, and each coefficient is regarded as a signal (or datum) point in the grey relational space. The grey relational order is cðx0 ; x1 Þ cðx0 ; x2 Þ cðx0 ; x3 Þ according to the measure by GRA. However, the grey relation entropy order is eðx0 ; x3 Þ eðx0 ; x2 Þ eðx0 ; x1 Þ according to the GRE measure. The set of Rr1 has the greatest grey relational grade but its data distribution is relatively divergent as compared with the other two sets. Since human perception involves certain degrees of uncertainty, imprecision, or incompleteness, such data variation and divergence will make the measure biased in terms of the viewpoint of information theory and signal detection. Obviously the GRE method is more reasonable and reliable than traditional GRA. 2.4. Interrelated grade of the grey relation entropy According to the axiom of wholeness, the GRE grades between any interrelated pair of sequences are unequal (i.e.
eðxi ; xj Þ–eðxj ; xi Þ). In order to analyze the interrelationship among the sequences as a whole, we define the interrelated grade of GRE as the average GRE grade between any interrelated pair of sequences. It can be expressed as:
eðxi Ixj Þ ¼
eðxi ; xj Þ þ eðxj ; xi Þ
ð8Þ
2
The interrelated grade of GRE is used to quantify the qualitative correlation, representing the magnitude of interrelationship between the corresponding pair of elements within a closed system. If the value of e(xiIxj) is high, the correlation between the interrelated pair of elements is regarded as similar. Conversely, it is regarded as different if the value of e(xiIxj) is low. The higher the interrelated grade, the more significant the similarity between the two interrelated elements is, whereas the lower the interrelated grade the more significant the difference between them is. In other words, if the interrelated grade of GRE is relatively low, the difference between the corresponding elements is regarded as significant; other-
Fig. 2. The comparison between GRA and GRE measures.
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wise it is regarded as insignificant. The ‘‘significance” here does not possess the implications of statistical hypothesis testing. It is based on the concept of comparative magnitude for the correlation analysis. 3. Methodology 3.1. Subjects A total of 115 subjects were selected from the trainees of an on-line computer-training program to voluntarily participate in the experimental study. The participants were qualified unemployed adults who involuntarily left their jobs. To ensure experimental variables being equitable and objective, the overall participants were required to have had no computer experience before the experiment. The demographic data of the experiment were classified as shown in Table 1. 3.2. Experimental design and materials 3.2.1. The elementary computer-training program and on-line learning system In order to conduct this experimental research, an asynchronous learning course was provided for the subjects who learned elementary computer skills. The short-term computer-training course was financially supported by the Bureau of Employment and Vocational Training (BEVT), Council of Labor Affairs, Taiwan.1 There were three major units contained in the on-line learning program: (1) fundamental computer operation (300 min), (2) word processing (180 min), and (3) Internet application (300 min). In addition to the three main units, there was an extra optional unit concerning the psychological guidance and assistance of learning in the program (120 min). The on-line learning system mainly consisted of three functions: (1) learning information, (2) starting animation, and (3) on-line instruction and case practice. The learning information briefly introduced the course subject, learning goals, and other basic knowledge concerning the presented course. The starting animation described the learning context with a short narrative animation. The on-line instruction function provided context description, content lecture, operational guidance, and case practice. The on-line learning system was constructed in an interactive multimedia environment including text, graphics, animation, audio, and video elements. It was installed in the on-line learning website of the BEVT.2 This asynchronous learning course was held at the Extension Education Center, Fortune Institute of Technology (FIT). The training classroom was available during office hours from 10:00 a.m. to 9:00 p.m. When connecting to the on-line learning system, participants had to login to the Website through the FIT’s network server by entering their own identification code. The overall learning time and process were controlled and recorded by the system automatically. 3.2.2. On-line tests The tests corresponding to the three main units were followed in the on-line learning system. After finishing each course unit, participants must be examined through the on-line testing system. Twenty items of closed-ended questions were given in each unit test, and each question contained five alternatives with one only correct answer. Each correct response scored 5 points with the full score being 100 points. Participants were required to respond to the questions within the time allotted. The passing mark was 60 points and the testing time was restricted to 10 min for each unit test. 3.2.3. Computer anxiety scales Beckers and Schmidt (2001) once proposed a six-factor model of computer anxiety. Based on their proposed model, we developed a measure scale that consists of a 15-item self-report inventory. The computer anxiety measure scale is comprised of 9 positive and 6 negative items. In addition to the six factors3, the scale includes responses that reflect the inexperienced users’ physical interaction with computers such as ‘‘I am able to use a mouse with ease” and ‘‘Reading from computer screens is acceptable to me”. To make the computer anxiety data available, we define the composite anxiety score (CAS) as the quotient obtained by dividing the total scores of the negatively-framed questions by the total scores of the positively-framed questions. The higher the numerical value the more anxious a subject was in the perceived situation. It can be expressed as below:
P si CAS ¼ q P ; sj
CAS 2 ½1; 25
ð9Þ
1 The Taiwan government has been executing an ‘‘Assisting Unemployed People to Participate Digital Capability Enhancement Training Program” since 2003. This life-long learning program has been changed from traditional face-to-face instruction to on-line learning environments since 2006. It was free of charge provided for people who involuntarily left their jobs and have had no computer experience before. The purpose of this project is to encourage unemployed adults to learn elementary computer skills, enabling them to operate computers and reenter the job market. 2 http://www.et.nat.gov.tw/TSVTC/index.jsp (in traditional Chinese) 3 The six factors include computer literacy (in terms of acquired computer skills), sell-efficacy (confidence in learning to use computers), physical symptoms in the presence of computers (such as sweaty palms and shortness of breath), feelings toward computers (like and dislike), positive beliefs about the benefits for society of using computers, and negative beliefs about the dehumanizing impact of computers (Beckers and Schmidt, 2001).
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J.-R. Chou, H.-C. Tsai / Information Processing and Management 45 (2009) 200–215 Table 1 Demographic data of the experiment. Number
Percentage
Gender
Male Female
27 88
23.5 76.5
Age group
Under 44 years old 45–54 years old Over 55 years old
57 41 17
49.6 35.6 14.8
Educational level
Low-education level (Junior high school or below the level) Middle-education level (Senior high school) High-education level (Junior college or above the level)
22
19.1
66
57.4
27
23.5
where s represents the computer anxiety score, s = {1, 2, 3, 4, 5}; i represents the negatively-framed question item; j represents the positively-framed question item; q is a scale coefficient, q = 7.5. 3.2.4. Questionnaire survey The questionnaire was comprised of three parts: (1) demographic questions, (2) an accessibility inquiry of on-line learning, and (3) a computer anxiety measure. In Part 1 of the questionnaire, participants gave their personal details including gender, age range, and educational level. In the accessibility inquiry, we attempted to identify the acceptance and effective use of on-line learning. It was used to analyze on-line learning characteristics for the unemployed adult novices. Ten closedended questions were given in Part 2 of the questionnaire, and each question contained three options with one only choice. In Part 3 of the questionnaire, 15 items of computer anxiety measure were given. Each item had to be rated on the basis of five-point Likert scales ranging from ‘‘entirely disagree” (score = 1) to ‘‘entirely agree” (score = 5). 3.3. Procedure The empirical study used a post-test design and was implemented under a life-long learning project supported by the BEVT, Taiwan. Participants learned elementary computer skills in their own rates of progress through the on-line learning system. Since the subjects were restricted to computer novices, the possible influence of the inexperienced learners’ self-efficacy on on-line learning might be a limitation. An assistant was available in the classroom, whose major assignment was not to teach the courses but to assist participants in dealing with the hardware and software problems. Three on-line tests were given in order after they had finished the corresponding three course units (excluding the extra optional unit). All test results were concurrently scored and recorded by the system, which were used to analyze the subjects’ on-line learning achievements in this study. Having completed the overall on-line learning courses and the corresponding tests, a paper-version questionnaire (see Appendix A) was provided to the subjects. Each subject responded the questions according to his/her own on-line learning experience and knowledge. The subjects were asked to respond as honestly as possible, and were told that there were no right or wrong answers. The research questions and the experimental procedure are illustrated as shown in Fig. 3. 3.4. Data analysis The statistics of the accessibility inquiry responses and computer anxiety measures were converted into sequence data, respectively. Each sequence represents the element of on-line learning characteristics or computer anxiety scales for the corGðmaleÞ represents the element of on-line learning characteristics for the responding classified group; for example, sequence xLC PðhighÞ represents the element of computer anxiety scales for the high-learning performance group. male gender group; xCA Taking advantage of the GRE operation, we could obtain the corresponding GRE grades. These GRE grades were converted into interrelated grades according to the corresponding elements’ interrelationships. Since all classified groups were considered to be equal in information magnitude, we used the mean interrelated grade to further combine and analyze the perceptive correlations among different classified groups. 4. Results 4.1. On-line learning achievements The mean score of the overall responses was 72.17 (SD = 7.34). According to the score ranges, we divided the subjects into three groups: high-performance (scored more than 80), medium-performance (scored between 70 and 79), and low-performance (scored less than 69). The mean scores of the on-line tests were 82.92 (N = 24, SD = 3.62) for the high-performance
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Fig. 3. Framework of the experimental procedure.
group, 73.79 (N = 44, SD = 2.97) for the medium-performance group, and 65.16 (N = 47, SD = 2.46) for the low-performance group. With respect to individual differences in learning achievements, the mean scores of the on-line tests were 72.72 (SD = 7.22) for the male subjects and 71.91 (SD = 7.4) for the female subjects. The mean scores of the on-line tests were 72.43 (SD = 6.87) for young adult subjects aged under 44, 71.91 (SD = 8.35) for middle-aged subjects aged between 45 and 54, and 71.96 (SD = 6.65) for elderly subjects aged over 55. Of the three different education groups, the mean scores of the on-line tests were 68.26 (SD = 6.18) for the low-education subjects, 72.68 (SD = 7.26) for the middle-education subjects, and 74.13 (SD = 7.49) for the high-education subjects (see Table 2). 4.2. The accessibility inquiry of on-line learning As shown in Table 3, the percentages of responses within each question demonstrate the correlative intensities of the accessible conditions toward on-line learning. The greater the percentage of response the closer the accessible condition was perceived to the subject group’s preference. We used the correlative intensities to identify certain on-line learning characteristics among the different classified groups.
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J.-R. Chou, H.-C. Tsai / Information Processing and Management 45 (2009) 200–215 Table 2 Descriptive statistics for the on-line learning achievements. Test
Gender
Male (N = 27)
Female (N = 88)
Age group
Under 44 years old (N = 57)
45–54 years old (N = 41)
Over 55 years old (N = 17)
Education
Low-education (N = 22)
Middle-education (N = 66)
High-education (N = 27)
Score Mean
SD
Total average
SD
Unit Unit Unit Unit Unit Unit
1 2 3 1 2 3
73.15 70.18 74.81 73.86 68.29 73.86
9.52 7.53 10.69 10.22 8.57 9.52
72.72
7.22
71.91
7.40
Unit Unit Unit Unit Unit Unit Unit Unit Unit
1 2 3 1 2 3 1 2 3
73.86 69.30 74.12 72.68 69.15 73.90 75.59 65.88 74.41
9.21 7.76 9.07 9.62 9.48 10.28 13.45 7.12 11.30
72.43
6.87
71.91
8.35
71.96
6.65
Unit Unit Unit Unit Unit Unit Unit Unit Unit
1 2 3 1 2 3 1 2 3
71.14 64.77 68.86 73.56 69.01 75.45 76.11 71.30 75.00
9.87 6.63 9.50 9.99 8.91 9.39 10.03 7.15 9.80
68.26
6.18
72.68
7.26
74.13
7.49
Taking advantage of the percentages of the subjects’ responses shown in Table 3, we constructed 11 sets of sequences (xi(kmn), ‘‘i = 1, 2, , 11” represents the group number; ‘‘m = 1, 2, , 10” represents the question number; and ‘‘n = 1, 2, 3” represents the option number). Letting f = 0.001, and respectively substituting the sequence data into the GRE operation program, we obtained 121 sets of GRE grades as shown in Table 4. Through the interaction matrix, we can clarify the mutual GðmaleÞ GðfemaleÞ ; xLC Þ ¼ 0:7629 signifies that the GRE grade in relationships between any two sequences. For example, eðxLC GðfemaleÞ GðmaleÞ corresponding to xLC is 0.7629. xLC Using the percentages of responses as variables to perform the correlation analysis, we found that the correlation between any pair of elements was significant at the 0.01 level according to the correlation analysis results measured through the Pearson, Kendall’s tau-b, and Spearman correlation coefficients, respectively (see Table 5). It is difficult to further analyze the similar or different correlation among the classified groups. Using our proposed method we obtained that the interrelated grades of GRE were 0.7619 between male and female groups, 0.6324 among the three different age groups, 0.5342 among the three different education groups, and 0.6287 among the three different learning performance groups. The interPerformance Þ eðxAge Þ eðxEducation Þ. related grade order was eðxGender LC LC LC Þ eðxLC According to the interrelated and mean interrelated grades given in Table 5, we constructed a correlation diagram as Að4554Þ Að55"Þ IxLC Þ ¼ 0:3268Þ, middle-edushown in Fig. 4. The differences between the middle-aged group and elderly group (eðxLC EðmiddleÞ EðhighÞ IxLC Þ ¼ 0:3816), low-education group and middle-education group cation group and high-education group (eðxLC EðlowÞ EðmiddleÞ PðlowÞ PðmediumÞ Þ ¼ 0:3265), and low-performance group and medium-performance group (eðxLC IxLC Þ ¼ 0:3751) (eðxLC IxLC were relatively significant in terms of the subjects’ on-line learning characteristics. As a whole, the highest mean interrelated IxPerformance Þ ¼ 0:7134, and the lowest grade was eðxEducation IxPerformance Þ ¼ 0:5487. grade was eðxGender LC LC LC LC 4.3. Computer anxiety measure Table 6 presents the means, standard deviations, and composite anxiety scores (CASs) corresponding to each classified group. The CAS results showed that the unemployed adult novices characterized by male gender, elderly group, low education, or medium learning performance were found to be more anxious toward computer use. The low-education subjects perceived the highest level of computer anxiety, whereas the low-performance subjects had the least anxiety of all of the classified groups. Taking advantage of the means of the computer anxiety measurement results, we constructed 11 sets of sequences (xi(kq), ‘‘i = 1, 2, , 11” represents the group number, and ‘‘q = 1, 2, , 15” represents the question number). Letting f = 0.01, and respectively substituting the sequence data into the GRE operation program, we obtained 121 sets of GRE grades as shown in Table 7. Using the means of the computer anxiety measurement results as variables to perform the correlation analysis, we found that whatever Pearson, Kendall’s tau-b, or Spearman correlation coefficient we took the correlation between any pair of ele-
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Table 3 Responses to the accessibility inquiries. 2–1
2–2
2–3
2–4
2–5
2–6
2–7
2–8
2–9
2–10
Male (N = 27)
a b c
1 (3.7%) 25 (92.6%) 1 (3.7%)
13 (48.1%) 1 (3.8%) 13 (48.1%)
9 (33.3%) 6 (22.2%) 12 (44.4%)
18 (66.7%) 2 (7.4%) 7 (25.9%)
24 (88.9%) 3 (11.1%) 0 (0%)
11 (40.8%) 6 (22.2%) 10 (37.0%)
17 (63.0%) 6 (22.2%) 4 (14.8%)
9 (33.3%) 6 (22.2%) 12 (44.5%)
5 (18.5%) 3 (11.1%) 19 (70.4%)
12 (44.4%) 14 (51.9%) 1 (3.7%)
Female (N = 88)
a b c
15 (17.0%) 70 (79.6%) 3 (3.4%)
54 (61.4%) 6 (6.8%) 28 (31.8%)
27 (30.7%) 20 (22.7%) 41 (46.6%)
54 (61.4%) 14 (15.9%) 20 (22.7%)
71 (80.7%) 7 (7.9%) 10 (11.4%)
54 (61.4%) 19 (21.6%) 15 (17.0%)
40 (45.4%) 16 (18.2%) 32 (36.4%)
23 (26.1%) 15 (17.1%) 50 (56.8%)
19 (21.6%) 4 (4.5%) 65 (73.9%)
48 (54.5%) 40 (45.5%) 0 (0%)
Under 44 years old (N = 57)
a b c
7 (12.3%) 48 (84.2%) 2 (3.5%)
33 (57.9%) 2 (3.5%) 22 (38.6%)
20 (35.1%) 12 (21.0%) 25 (43.9%)
33 (57.9%) 9 (15.8%) 15 (26.3%)
45 (78.9%) 7 (12.3%) 5 (8.8%)
30 (52.6%) 12 (21.1%) 15 (26.3%)
25 (43.8%) 14 (24.6%) 18 (31.6%)
22 (38.6%) 8 (14.0%) 27 (47.4%)
11 (19.3%) 5 (8.8%) 41 (71.9%)
29 (50.9%) 27 (47.4%) 1 (1.7%)
45–54 years old (N = 41)
a b c
7 (17.1%) 33 (80.5%) 1 (2.4%)
25 (61.0%) 4 (9.7%) 12 (29.3%)
9 (22.0%) 11 (26.8%) 21 (51.2%)
26 (63.4%) 7 (17.1%) 8 (19.5%)
35 (85.4%) 2 (4.9%) 4 (9.7%)
26 (63.4%) 8 (19.5%) 7 (17.1%)
23 (56.1%) 7 (17.1%) 11 (26.8%)
7 (17.1%) 7 (17.1%) 27 (65.8%)
13 (31.7%) 1 (2.4%) 27 (65.9%)
21 (51.2%) 20 (48.8%) 0 (0%)
Over 55 years old (N = 17)
a b c
2 (11.8%) 14 (82.3%) 1 (5.9%)
9 (52.9%) 1 (5.9%) 7 (41.2%)
7 (41.2%) 3 (17.6%) 7 (41.2%)
13 (76.5%) 0 (0%) 4 (23.5%)
15 (88.2%) 1 (5.9%) 1 (5.9%)
9 (52.9%) 5 (29.4%) 3 (17.7%)
9 (52.9%) 1 (5.9%) 7 (41.2%)
3 (17.6%) 6 (35.3%) 8 (47.1%)
0 (0%) 1 (5.9%) 16 (94.1%)
10 (58.8%) 7 (41.2%) 0 (0%)
Low-education (N = 22)
a b c
2 (9.1%) 20 (90.9%) 0 (0%)
12 (54.5%) 2 (9.1%) 8 (36.4%)
2 (9.0%) 10 (45.5%) 10 (45.5%)
16 (72.7%) 2 (9.1%) 4 (18.2%)
17 (77.3%) 2 (9.1%) 3 (13.6%)
8 (36.4%) 10 (45.4%) 4 (18.2%)
11 (50.0%) 2 (9.1%) 9 (40.9%)
3 (13.6%) 2 (9.1%) 17 (77.3%)
7 (31.8%) 0 (0%) 15 (68.2%)
11 (50.0%) 10 (45.5%) 1 (4.5%)
Middleeducation (N = 66)
a b c
13 (19.7%) 50 (75.8%) 3 (4.5%)
40 (60.6%) 5 (7.6%) 21 (31.8%)
20 (30.3%) 12 (18.2%) 34 (51.5%)
35 (53.0%) 12 (18.2%) 19 (28.8%)
56 (84.8%) 6 (9.1%) 4 (6.1%)
44 (66.7%) 8 (12.1%) 14 (21.2%)
29 (43.9%) 17 (25.8%) 20 (30.3%)
19 (28.8%) 15 (22.7%) 32 (48.5%)
12 (18.2%) 6 (9.1%) 48 (72.7%)
36 (54.5%) 30 (45.5%) 0 (0%)
High-education (N = 27)
a b c
1 (3.7%) 25 (92.6%) 1 (3.7%)
15 (55.6%) 0 (0%) 12 (44.4%)
14 (51.9%) 4 (14.8%) 9 (33.3%)
21 (77.8%) 2 (7.4%) 4 (14.8%)
22 (81.5%) 2 (7.4%) 3 (11.1%)
13 (48.2%) 7 (25.9%) 7 (25.9%)
17 (63.0%) 3 (11.1%) 7 (25.9%)
10 (37.0%) 4 (14.8%) 13 (48.2%)
5 (18.5%) 1 (3.7%) 21 (77.8%)
13 (48.1%) 14 (51.9%) 0 (0%)
Less than 69 points (N = 47)
a b c
8 (17.0%) 38 (80.9%) 1 (2.1%)
23 (49.0%) 5 (10.6%) 19 (40.4%)
12 (25.5%) 12 (25.5%) 23 (49.0%)
31 (66.0%) 8 (17.0%) 8 (17.0%)
38 (80.9%) 4 (8.5%) 5 (10.6%)
25 (53.2%) 11 (23.4%) 11 (23.4%)
24 (51.0%) 6 (12.8%) 17 (36.2%)
8 (17.0%) 8 (17.0%) 31 (66.0%)
12 (25.5%) 2 (4.3%) 33 (70.2%)
27 (57.4%) 20 (42.6%) 0 (0%)
70–79 points (N = 44)
a b c
5 (11.4%) 36 (81.8%) 3 (6.8%)
30 (68.2%) 2 (4.5%) 12 (27.3%)
17 (38.6%) 9 (20.5%) 18 (40.9%)
24 (54.6%) 7 (15.9%) 13 (29.5%)
37 (84.1%) 3 (6.8%) 4 (9.1%)
32 (72.8%) 6 (13.6%) 6 (13.6%)
20 (45.4%) 8 (18.2%) 16 (36.4%)
12 (27.3%) 9 (20.4%) 23 (52.3%)
7 (15.9%) 5 (11.4%) 32 (72.7%)
18 (40.9%) 26 (59.1%) 0 (0%)
More than 80 points (N = 24)
a b c
3 (12.5%) 21 (87.5%) 0 (0%)
14 (58.3%) 0 (0%) 10 (41.7%)
7 (29.2%) 5 (20.8%) 12 (50.0%)
17 (70.8%) 1 (4.2%) 6 (25.0%)
20 (83.3%) 3 (12.5%) 1 (4.2%)
8 (33.4%) 8 (33.3%) 8 (33.3%)
13 (54.2%) 8 (33.3%) 3 (12.5%)
12 (50.0%) 4 (16.7%) 8 (33.3%)
5 (20.8%) 0 (0%) 19 (79.2%)
15 (62.5%) 8 (33.3%) 1 (4.2%)
Table 4 Interaction matrix of the GRE grades for analyzing on-line learning characteristics. GðmaleÞ
xLC GðmaleÞ xLC GðfemaleÞ xLC Að44#Þ xLC Að4554Þ xLC Að55"Þ xLC EðlowÞ xLC EðmiddleÞ xLC EðhighÞ xLC PðlowÞ xLC PðmediumÞ xLC PðhighÞ xLC
GðfemaleÞ
Að44#Þ
Að4554Þ
Að55"Þ
EðlowÞ
EðmiddleÞ
EðhighÞ
PðlowÞ
PðmediumÞ
PðhighÞ
xLC
xLC
xLC
xLC
xLC
xLC
xLC
xLC
xLC
xLC
1
0.7610
0.7823
0.9251
0.8720
0.8478
0.7790
0.6108
0.7152
0.7926
0.8616
0.7629
1
0.6774
0.5352
0.3329
0.3642
0.5366
0.4567
0.5615
0.5583
0.8175
0.7835
0.6747
1
0.8142
0.7573
0.8906
0.7492
0.7836
0.9043
0.7278
0.6987
0.9251
0.5121
0.8132
1
0.3253
0.6691
0.4205
0.3547
0.6335
0.3857
0.8023
0.8721
0.3041
0.7562
0.3283
1
0.7642
0.3616
0.3565
0.4042
0.4425
0.8227
0.8467
0.2735
0.8896
0.6536
0.7593
1
0.3135
0.8946
0.8859
0.9320
0.3905
0.7788
0.5218
0.7452
0.4180
0.3564
0.3395
1
0.4056
0.3343
0.4603
0.8912
0.6033
0.3733
0.7772
0.3011
0.2989
0.8947
0.3576
1
0.3808
0.3028
0.3389
0.7151
0.5371
0.9040
0.6331
0.4003
0.8872
0.3362
0.4368
1
0.3922
0.7901
0.7900
0.5389
0.7175
0.3473
0.4031
0.9321
0.4340
0.3194
0.3580
1
0.7242
0.8597
0.8133
0.6905
0.7991
0.8205
0.3905
0.8906
0.3335
0.7855
0.7221
1
ments was significant at the 0.01 level (see Table 8). The bivariate correlations seem to be unfeasible to explain our observation phenomena due to the data properties. However, through our proposed method we found that the interrelated grades of GRE were 0.7051 between male and female groups, 0.7741 among the three different age groups, 0.7375 among the three different education groups, and 0.8463 among the three different learning performance groups. The interrelated grade order Education Þ eðxAge Þ eðxGender Þ. was eðxPerformance CA CA CA Þ eðxCA
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J.-R. Chou, H.-C. Tsai / Information Processing and Management 45 (2009) 200–215 Table 5 Correlation matrix for analyzing on-line learning characteristics. xAge LC
xEducation LC
xPerformance LC
0.7619 1
0.6830
0.5827
0.7134
0.7829 0.953** 0.812** 0.940** 0.9251 0.903** 0.706** 0.876** 0.8720 0.909** 0.720** 0.884**
0.6760 0.978** 0.894** 0.976** 0.5236 0.980** 0.887** 0.968** 0.3185 0.941** 0.789** 0.921**
1
0.6324
0.5860
0.6409
0.8137 0.941** 0.801** 0.928** 0.7567 0.928** 0.775** 0.911**
1
0.3268 0.895** 0.754** 0.872**
1
0.8472 0.852** 0.665** 0.824** 0.7789 0.908** 0.740** 0.905** 0.6070 0.959** 0.810** 0.938**
0.3188 0.905** 0.741** 0.884** 0.5292 0.981** 0.888** 0.967** 0.4150 0.935** 0.803** 0.935**
0.8901 0.871** 0.696** 0.854** 0.7472 0.972** 0.882** 0.973** 0.7804 0.960** 0.842** 0.951**
0.6613 0.922** 0.788** 0.913** 0.4192 0.958** 0.797** 0.918** 0.3279 0.905** 0.772** 0.904**
0.7617 0.853** 0.659** 0.813** 0.3590 0.919** 0.784** 0.918** 0.3277 0.938** 0.761** 0.905**
1
0.5342
0.3265 0.827** 0.622** 0.794** 0.8946 0.856** 0.708** 0.827**
1
0.3816 0.903** 0.740** 0.901**
1
0.7151 0.916** 0.725** 0.888** 0.7913 0.889** 0.745** 0.897** 0.8606 0.944** 0.809** 0.928**
0.5493 0.978** 0.872** 0.961** 0.5486 0.971** 0.879** 0.972** 0.8154 0.890** 0.755** 0.891**
0.9041 0.949** 0.814** 0.935** 0.7226 0.959** 0.847** 0.961** 0.6946 0.938** 0.817** 0.932**
0.6333 0.979** 0.936** 0.984** 0.3665 0.945** 0.809** 0.934** 0.8007 0.856** 0.693** 0.845**
0.4022 0.932** 0.790** 0.903** 0.4228 0.910** 0.758** 0.897** 0.8216 0.881** 0.705** 0.847**
0.8865 0.944** 0.792** 0.915** 0.9320 0.827** 0.645** 0.811** 0.3905 0.821** 0.679** 0.817**
0.3352 0.944** 0.783** 0.922** 0.4471 0.972** 0.867** 0.967** 0.8909 0.885** 0.733** 0.887**
0.4088 0.926** 0.793** 0.918** 0.3111 0.915** 0.782** 0.924** 0.3362 0.921** 0.760** 0.902**
Interrelated grade Pearson Kendall’s tau-b Spearman
xGender LC
xGender LC
1 0.7619 0.910** 0.738** 0.899**
xAge LC
xEducation LC
xPerformance LC
**
0.5487
1
0.6287
0.3751 0.919** 0.780** 0.919** 0.7878 0.874** 0.712** 0.856**
1
0.7231 0.826** 0.681** 0.841**
1
Pearson, Kendall’s tau-b, or Spearman correlation is significant at the 0.01 level (2-tailed).
Fig. 5 presents the perceptive correlations among different classified groups’ computer anxiety scales. The differences beAð4554Þ Að55"ÞÞ IxCA ¼ 0:6265), and between low-education and middle-education tween the middle-aged and elderly groups (eðxCA EðlowÞ EðmiddleÞ Þ ¼ 0:4145) were relatively significant. As a whole, the higher mean interrelated grades were groups (eðxCA IxCA Gender Performance eðxEducation IxPerformance Þ ¼ 0:8356 and eðxGender IxAge IxCA Þ ¼ 0:7385. CA CA CA CA Þ ¼ 0:8217, and the lowest grade was eðxCA 5. Discussion Correlation analysis is a statistical technique regularly used to measure the relationship between two data sets. It is concerned with the strength of a linear relationship between two or more variables. The test of significance is based on the assumption that the distribution of the residual values (i.e. the deviations from the regression line) for the dependent variable follows the normal distribution, and that the variability of the residual values is the same for all values of the independent variable. The significance of a correlation coefficient of a particular magnitude will change depending on the size of the sample from which it was computed. However, the assumption and sample requirement seem to be unreasonable to deal with the cognition-based, Likert-typed and self-reported questions we explored. Unlike previous research in the literature that used traditional statistical methods to test experimental hypotheses or to derive variable relationships, this study proposes a new mathematical tool to analyze the similar or different correlations between experimental variables within an
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J.-R. Chou, H.-C. Tsai / Information Processing and Management 45 (2009) 200–215
Fig. 4. The correlations of on-line learning characteristics among the experimental subject groups.
Table 6 The means, standard deviations, and composite anxiety scores of computer anxiety measure. 3–1
3–2
3–3
3–4
3–5
3–6
3–7
3–8
3–9
3–10
3–11
3–12
3–13
3–14
3–15
CAS
Male (N = 27)
3.78 0.93
3.33 1.04
3.78 0.64
3.56 0.80
3.44 0.85
4.11 0.42
4.04 0.44
3.96 0.59
2.56 0.70
2.56 0.85
2.26 0.59
2.41 0.69
2.37 0.84
3.78 0.70
4.04 0.52
3.40
Female (N = 88)
3.95 0.69
3.69 0.85
3.66 0.80
3.58 0.81
3.43 0.81
4.18 0.63
3.95 0.60
4.01 0.62
2.36 0.75
2.31 0.79
2.26 0.77
2.37 0.91
2.36 0.80
3.83 0.70
3.90 0.77
3.26
Under 44 years (N = 57)
3.79 0.86
3.63 0.92
3.82 0.71
3.49 0.85
3.39 0.82
4.21 0.65
3.98 0.58
4.02 0.64
2.39 0.70
2.32 0.80
2.23 0.78
2.40 0.90
2.30 0.82
3.89 0.70
3.88 0.78
3.25
45–54 years (N = 41)
4.05 0.63
3.61 0.89
3.54 0.81
3.58 0.86
3.41 0.86
4.12 0.60
3.98 0.61
4.05 0.59
2.41 0.80
2.34 0.82
2.27 0.63
2.32 0.79
2.29 0.72
3.78 0.65
4.02 0.72
3.25
Over 55 years (N = 17)
4.00 0.61
3.53 0.94
3.59 0.79
3.82 0.39
3.65 0.72
4.12 0.33
3.94 0.43
3.82 0.53
2.47 0.72
2.59 0.79
2.35 0.79
2.47 0.94
2.76 0.90
3.65 0.79
3.94 0.43
3.55
Low-education (N = 22)
3.82 0.73
3.50 0.91
3.41 0.73
3.41 0.80
3.41 0.80
4.18 0.50
3.73 0.55
3.86 0.64
2.54 0.86
2.64 1.00
2.45 0.74
2.32 0.78
2.59 0.80
3.59 0.67
3.77 0.81
3.60
Middle-education (N = 66)
4.00 0.76
3.71 0.89
3.76 0.82
3.68 0.79
3.48 0.85
4.18 0.68
4.08 0.56
4.04 0.62
2.36 0.74
2.26 0.77
2.18 0.78
2.45 0.98
2.35 0.87
3.86 0.74
3.95 0.75
3.21
High-education (N = 27)
3.78 0.75
3.44 0.93
3.74 0.59
3.44 0.85
3.33 0.78
4.11 0.42
3.93 0.55
4.00 0.55
2.41 0.64
2.41 0.69
2.30 0.54
2.26 0.59
2.22 0.64
3.89 0.58
4.04 0.52
3.26
Low-performance (N = 47)
4.02 0.67
3.79 0.91
3.85 0.72
3.66 0.89
3.45 0.88
4.28 0.65
4.04 0.59
4.15 0.62
2.32 0.75
2.34 0.89
2.15 0.72
2.25 0.85
2.30 0.86
3.87 0.74
3.98 0.79
3.12
Medium-performance (N = 44)
3.79 0.82
3.43 0.90
3.48 0.82
3.43 0.79
3.52 0.76
4.11 0.44
3.95 0.57
3.89 0.62
2.54 0.73
2.39 0.69
2.34 0.71
2.66 0.91
2.48 0.82
3.79 0.73
3.84 0.71
3.54
High-performance (N = 24)
3.92 0.77
3.58 0.88
3.75 0.68
3.67 0.64
3.25 0.79
4.04 0.69
3.87 0.54
3.92 0.50
2.33 0.70
2.37 0.87
2.33 0.76
2.12 0.68
2.29 0.69
3.75 0.53
4.04 0.55
3.19
CAS = [(M3–5 + M3–9 + M3–10 + M3–11 + M3–12 + M3–13)/(M3–1 + M3–2 + M3–3 + M3–4 + M3–6 + M3–7 + M3–8 + M3–14 + M3–15)] 7.5.
observation phenomenon. The significance of our proposed method is based on the concept of comparative magnitude for the correlation analysis. According to the on-line test results, males were better than females, particularly in the learning achievements of the word processing unit. However, males were found to be more anxious than females in terms of the CAS results. The similar correlation between gender groups was relatively significant in terms of the on-line learning characteristics Þ ¼ 0:7619 "), but relatively insignificant in terms of the computer anxiety scales (eðxGender Þ ¼ 0:7051 #). (eðxGender LC CA Of the three different age groups, the young adult subjects (aged under 44) had the highest on-line learning achievements. There was little difference in learning achievements between middle-aged (aged 45–54) and elderly (aged over 55) subjects. The elderly subjects had the worst on-line learning achievements in the word processing unit. They were also
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J.-R. Chou, H.-C. Tsai / Information Processing and Management 45 (2009) 200–215 Table 7 Interaction matrix of the GRE grades for analyzing computer anxiety scales. GðmaleÞ
xCA GðmaleÞ xCA GðfemaleÞ xCA Að44#Þ xCA Að4554Þ xCA Að55"Þ xCA EðlowÞ xCA EðmiddleÞ xCA EðhighÞ xCA PðlowÞ xCA PðmediumÞ xCA PðhighÞ xCA
GðfemaleÞ
Að44#Þ
Að4554Þ
Að55"Þ
EðlowÞ
EðmiddleÞ
EðhighÞ
PðlowÞ
PðmediumÞ
PðhighÞ
xCA
xCA
xCA
xCA
xCA
xCA
xCA
xCA
xCA
xCA
1
0.6938
0.8549
0.7345
0.8258
0.8738
0.8984
0.6775
0.6004
0.7147
0.6641
0.7165
1
0.9222
0.7833
0.8368
0.5292
0.7307
0.8749
0.9385
0.6339
0.9486
0.8549
0.9194
1
0.7757
0.9224
0.8513
0.9458
0.7635
0.6994
0.6484
0.8629
0.7313
0.7549
0.7726
1
0.6428
0.5077
0.8977
0.7459
0.7009
0.8125
0.7306
0.8182
0.8240
0.9213
0.6103
1
0.9176
0.5773
0.7597
0.8390
0.7839
0.8933
0.8746
0.5074
0.8521
0.5183
0.9194
1
0.4061
0.9057
0.8974
0.7015
0.9553
0.9000
0.7265
0.9468
0.9022
0.6290
0.4230
1
0.8956
0.9225
0.9425
0.8173
0.6548
0.8646
0.7364
0.7218
0.7597
0.9037
0.8912
1
0.8297
0.7559
0.7257
0.6004
0.9371
0.6994
0.7050
0.8433
0.8969
0.9204
0.8366
1
0.9305
0.7996
0.6895
0.5597
0.6110
0.8061
0.7839
0.6591
0.9408
0.7559
0.9295
1
0.8127
0.6286
0.9463
0.8559
0.7032
0.8933
0.9546
0.8046
0.7257
0.7926
0.8127
1
Table 8 Correlation matrix for analyzing computer anxiety scales. xAge CA
xEducation CA
xPerformance CA
0.7051 1
0.8217
0.7593
0.7385
0.9208 .995** .875** .955** 0.7691 .996** .889** .966** 0.8304 .978** .735** .890**
1
0.7741
0.7751
0.7706
0.7741 .987** .804** .917** 0.9218 .964** .631** .817**
1
0.6265 .978** .761** .909**
1
0.8742 .970** .762** .906** 0.8992 .982** .839** .946** 0.6661 .993** .897** .973**
0.5183 .983** .839** .943** 0.7286 .998** .952** .989** 0.8697 .988** .786** .915**
0.8517 .974** .734** .889** 0.9463 .994** .886** .968** 0.7499 .993** .837** .945**
0.5130 .984** .903** .970** 0.8999 .993** .842** .949** 0.7338 .988** .831** .944**
0.9185 .975** .788** .915** 0.6031 .979** .748** .892** 0.7597 .960** .696** .862**
1
0.7375
0.4145 .974** .773** .918** 0.9047 .975** .820** .934**
1
0.8934 .985** .798** .925**
1
0.6004 .983** .859** .957** 0.7021 .981** .857** .953** 0.6463 .982** .846** .950**
0.9378 .998** .875** .962** 0.5968 .988** .864** .956** 0.9474 .989** .735** .896**
0.6994 .996** .848** .950** 0.6297 .987** .856** .955** 0.8594 .984** .728** .882**
0.7029 .993** .880** .969** 0.8093 .987** .754** .916** 0.7169 .990** .810** .942**
0.8411 .977** .767** .901** 0.7839 .970** .755** .894** 0.8933 .973** .762** .905**
0.8971 .982** .889** .971** 0.6803 .973** .722** .899** 0.9549 .975** .847** .950**
0.9214 .996** .886** .964** 0.9416 .989** .914** .971** 0.8109 .986** .728** .898**
0.8331 .989** .856** .955** 0.7559 .981** .777** .921** 0.7257 .990** .912** .974**
Interrelated grade Pearson Kendall’s tau-b Spearman
xGender CA
xGender CA
1 0.7051 .981** .788** .917** 0.8549 .986** .839** .950** 0.7329 .983** .814** .924** 0.8220 .968** .786** .904**
xAge CA
xEducation CA
xPeformance CA
**
Pearson, Kendall’s tau-b, or Spearman correlation is significant at the 0.01 level2-tailed).
0.8356
1
0.8463
0.9300 .981** .798** .936** 0.7961 .992** .845** .946**
1
0.8127 .965** .696** .880**
1
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Fig. 5. The correlations of computer anxiety scales among the experimental subject groups.
found to be more anxious than the other two age groups. The differences between middle-aged subjects and elderly subjects Að4554Þ Að55"Þ IxLC Þ ¼ 0:3268) and computer anxiety were relatively significant in terms of the on-line learning characteristics (eðxLC Að4554Þ Að55"Þ IxCA Þ ¼ 0:6265), respectively. For example, when using the on-line learning system for the first time, scales (eðxCA 41.2% of the elderly respondents were amazed, yet 51.2% of the middle-aged respondents immediately tried using the system without any hesitation. When an error message appeared on the system, 35.3% of the elderly respondents wanted to reboot the system, yet 65.8% of the middle-aged respondents wanted to request experienced help. 94.1% of the elderly respondents required more user-friendly interfaces and learning guidelines, while 31.7% of the middle-aged respondents laid stress on the substantial contents of the on-line learning courses. On the whole, the similar correlation among age groups was moderate in terms of the subjects’ on-line learning characteristics (eðxAge LC Þ ¼ 0:6324) and computer anxiety scales (eðxAge CA Þ ¼ 0:7741), respectively. Education differences in on-line learning performance were quite obvious, with the higher the educational level the higher the on-line learning achievement. It is worthy to remark that the high-education subjects had a relatively high level of learning achievements, whereas the low-education subjects did the worst, especially in the word processing unit. The low-education group experienced the highest measure of computer anxiety, while the middle-education group was found to be less anxious toward computer use. There were relatively significant differences in on-line learning characteristics beEðlowÞ EðmiddleÞ Þ ¼ 0:3265), and between middle-education and hightween low-education and middle-education subjects (eðxLC IxLC EðmiddleÞ EðhighÞ IxLC Þ ¼ 0:3816). The differences in computer anxiety scales between low-education and education subjects (eðxLC EðlowÞ
EðmiddleÞ
Þ ¼ 0:4145) were also relatively significant. For example, when using the on-line middle-education subjects (eðxCA IxCA learning system for the first time, 51.9% of the high-education respondents were amazed at the system and 45.5% of the loweducation respondents felt to be a loss as to what to do. However, 51.5% of the middle-education respondents immediately tried using the system without any hesitation. 66.7% of the middle-education respondents considered on-line learning to be disadvantageous due to the lack of timely help by a teacher, while 45.4% of the low-education respondents indicated that the on-line learning system was too complicated for them to understand. 63% of the high-education respondents expressed that the most difficult-to-do thing toward on-line learning was to familiarize themselves with the usage of the operating interfaces, while 40.9% of the low-education respondents were worried about the unexpected crash of the system. When error messages appeared, 77.3% of the low-education respondents wanted to request experienced help, whereas 37% of the high-education respondents wanted to read the content of the message as well as to try to solve the problem. On the whole, the similar correlation among education groups was relatively insignificant in terms of the subjects’ on-line learning charÞ ¼ 0:5342 #). acteristics (eðxEducation LC With respect to learning performance differences, the medium-performance subjects were perceived to be more anxious toward computer use. There were relatively significant differences in on-line learning characteristics between low-perforPðlowÞ PðmediumÞ Þ ¼ 0:3751). 68.2% of the medium-performance respondents mance and medium-performance subjects (eðxLC IxLC considered on-line learning to be a tendency toward present day society, while 40.4% of the low-performance respondents thought it an ideal solution to the digital divide problem. 59.1% of the medium-performance respondents considered that they got a limited achievement but did not refuse learning more computer skills. However, 57.4% of the low-performance respondents expressed that they got some measure of achievement and also developed a high interest in learning computer skills after they had completed the on-line learning program. On the whole, the similar correlation among the three different Þ ¼ 0:8463 "). learning performance groups was relatively significant in terms of the computer anxiety scales (eðxPerformance CA
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To the unemployed adult novices as a whole, educational level is the critical factor influencing their on-ling learning achievements. The high-education group had the best on-line learning achievements, whereas the low-education group did the worst. The low-education group perceived the highest measure of computer anxiety while the low-performance group had least anxiety of all of the classified groups. Learning performance effect highly correlates with gender effect and education effect. The correlation between gender effect and learning performance effect was relatively similar in terms IxPerformance Þ ¼ 0:7134 ") but relatively different in terms of the computer anxof the accessibility of on-line learning (eðxGender LC LC Gender Performance Þ ¼ 0:7385 #). On the opposite side, the correlation between education effect and learning periety scales (eðxCA IxCA IxPerformance Þ ¼ 0:8356 ") but formance effect was relatively similar in terms of the subjects’ computer anxiety scales (eðxGender CA CA Gender Performance Þ ¼ 0:5487 #). Gender effect and age relatively different in terms of the accessibility of on-line learning (eðxCA IxCA IxAge group effect were also relatively similar in computer anxiety perception (eðxGender CA CA Þ ¼ 0:8217 ").
6. Conclusion Current information theory associated with artificial intelligence methods is applicable to dealing with human cognition and perception problems. This study proposes a new correlation analysis method to measure human information behavior in relation to on-line learning performance and computer anxiety. The proposed method integrates Shannon entropy into a grey relational analysis model, taking advantage of the geometric proximity between different discrete sequences and the asymptotic behavior in a discrete information system to quantify the qualitative correlations among subjects’ responses to the designed questions. The empirical study has shown a credible result. The proposed method can help to mathematically measure human behavior by modeling the magnitude relationships between quantification and characterization of experimental variables. Correlation is normally classified as a non-experimental, descriptive method. A correlation analysis study is one designed to determine the degree and direction of relationship between two or more variables or measures of behavior. Although this research has contributed to providing a novel idea for measuring human information behavior, there were some limitations in our proposed method as conventional correlation analysis methods suffered. The greatest limitation is that it does not tell researchers whether or not the relationship is causal. This non-function-typed method only shows that two or more variables are related in a comparative magnitude, but it does not prove nor disprove that the correlation is a cause-andeffect relationship. However, we can derive some behavioral phenomena through the explanations of the correlative magnitude. In conclusion, unemployed adult novices possess socio-demographic and socio-economic features in terms of the digital divide perspective. In addition to computer anxiety, computer attitudes can affect the subsequent behavior concerning people’s use of information systems. Many researchers reported that the variance of intent to use information technology could be explained by attitudes toward computers (e.g. Herbert & Benbasat, 1994). Continuing with our findings, further research should focus on analyzing unemployed adult novices’ attitudes toward on-line learning.
Acknowledgements This study was supported by the National Science Council of Taiwan under Grant NSC 96-2221-E-268-002. The authors also gratefully acknowledge the subjects who participated in the experimental study voluntarily.
Appendix A. List of the questionnaire Part 1. Demographic questions Question 1-1. What is your gender? ___Male ___Female Question 1-2. What is your age range? ___Under 44 years old ___45–54 years old ___Over 55 years old Question 1-3. What is your educational level? ___Junior high school or below the level ___Senior high school ___Junior college or above the level Part 2. Accessibility inquiry of on-line learning Question 2-1. What is your initial impression on a computer before attending this one-line learning program? a. It is a high tech machine with certain complexity. b. It is an applied tool that people have to possess the skills. c. It is a trendy product belonged to young people.
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Question 2-2. What do you think about ‘‘on-line learning”? a. It is a tendency toward present day society. b. It is a novel development of current information and communication technology. c. It is an ideal solution to the digital divide problem. Question 2-3. What is your experience in using the on-line learning system for the first time? a. I am amazed at the system. b. I feel to be a loss as to what to do. c. I immediately try using the system without any hesitation. Question 2-4. What is your outlook on the ‘‘on-line learning” mode? a. It is a good way for learning. b. It is not true to life and lacks a sense of reality. c. The learning process and outcome are difficult to be anticipated. Question 2-5. What is the greatest advantage of using the on-line learning system? a. The courses are flexible and allow learners to learn in their own rates of progress. b. Its learning environment is more independent with less external disturbance. c. The standardized program assures learners of learning quality. Question 2-6. What is the greatest disadvantage of using the on-line learning system? a. I cannot solve the problems timely with the help of a teacher. b. The learning system is too complicated for novices to understand. c. It cannot satisfy individual requirements due to its uniform learning mode. Question 2-7. What is the most difficult-to-do thing while using the on-line learning system? a. Familiarizing myself with the usage of the operating interfaces b. Dealing with error problems accompanied by an error message c. The unexpected crash Question 2-8. Supposing the system displays an error message, what will you do then? a. I would like to read the content of the message carefully as well as to try to solve the problem by myself. b. I would like to reboot the system. c. I would like to request experienced help. Question 2-9. Which function should be enhanced the most for the on-line learning system? a. A course with substantial contents b. Vivid interaction effects c. More user-friendly interfaces and learning guidelines Question 2-10. What do you gain the most from finishing this on-line learning program? a. I get some measure of achievement and also develop a high interest in learning computer skills. b. I get a limited achievement but do not refuse learning more computer skills. c. I get no achievement and do not want to learn more computer skills either.
Part 3. Computer anxiety measure (A 5-point Likert scale ranging from ‘‘entirely disagree” to ‘‘entirely agree”) Question 3-1. I am able to use a computer mouse with ease. Question 3-2. I am able to use keyboard with a computer correctly. Question 3-3. Reading from computer screens is acceptable to me. Question 3-4. I can well comprehend the information presented on a computer screen. Question 3-5. I find it difficult to understand the technical aspects of a computer. Question 3-6. Nowadays, everyone can learn to use a computer. Question 3-7. I am confident that I can learn computer skills. Question 3-8. The computer has simplified my life. Question 3-9. I think the computer inaccessible. Question 3-10. I feel like I am short of breath when I am in front of the computer. Question 3-11. I have sweaty hand palms when I work with the computer. Question 3-12. Computers make people become isolated.
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Question 3-13. Computers destroy human creativity. Question 3-14. Computers bridge the information gap between rich and poor countries. Question 3-15. Computers help to effectively fight the large world problems, such as poverty, knowledge disparity, etc.
References Beckers, J. J., & Schmidt, H. G. (2001). The structure of computer anxiety: A six-factor model. Computers in Human Behavior, 17, 35–49. Bozionelos, N. (2004). Socio-economic background and computer use: The role of computer anxiety and computer experience in their relationship. International Journal of Human–Computer Studies, 61, 725–746. Brosnan, M. J. (1998). The impact of computer anxiety and self-efficacy upon performance. Journal of Computer Assisted Learning, 14, 223–234. Chou, H. W. (2001). Effects of training method and computer anxiety on learning performance and self-efficacy. Computers in Human Behavior, 17, 51–69. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates. Deng, J. L. (1982). Control problems of grey system. Systems and Control Letters, 1(5), 288–294. Deng, J. L. (1989). Introduction to grey system theory. Journal of Grey System, 1(1), 1–24. Desai, M. S. (2001). Computer anxiety and performance: An application of a change model in a pedagogical setting. Journal of Instructional Psychology, 28(3), 141–149. Heinssen, R. K., Glass, C. R., & Knight, L. A. (1987). Assessing computer anxiety: Development and validation of the computer anxiety rating scale. Computers in Human Behavior, 3, 49–59. Herbert, M., & Benbasat, I. (1994). Adapting information technology in hospitals: The relationship between attitudes/expectations and behavior. Hospital & Health Services Administration, 39(3), 369–383. Korukonda, A. R. (2007). Differences that do matter: A dialectic analysis of individual characteristics and personality dimensions contributing to computer anxiety. Computers in Human Behavior, 23, 1921–1942. Liu, M. C., & Hong, C. M. (1997). A study on the implementation of evaluation criteria for computer skill instruction by grey relational analysis. Journal of Grey System, 2, 117–130. Marcoulides, G. A. (1989). Measuring computer anxiety: The computer anxiety scale. Educational and Psychological Measurement, 49, 733–739. Martin, B. L., Stewart, D. L., & Hillison, J. (2001). Computer anxiety levels of Virginia extension personnel. Journal of Extension, 39(1). Retrieved 28.11.2007. Mikkelsen, A., Øgaard, T., Lindøe, P. H., & Olsen, O. E. (2002). Job characteristics and computer anxiety in the production industry. Computers in Human Behavior, 18, 223–239. Montag, M., Simonson, M. R., & Maurer, M. M. (1984). Test administrator’s manual for the standardized test of computer literacy and computer anxiety index. Ames, IA: Iowa State University Research Foundation. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423. 623-656. Smith, B., & Caputi, P. (2001). Cognitive interference in computer anxiety. Behaviour and Information Technology, 20, 265–273. Sonnenwald, D. H., & Pierce, L. G. (2000). Information behavior in dynamic group work contexts: Interwoven situational awareness, dense social networks and contested collaboration in command and control. Information Processing and Management, 36, 461–479. Todman, J., & Day, K. (2006). Computer anxiety: The role of psychological gender. Computers in Human Behavior, 22, 856–869. Tsuchiya, T., Maeda, T., Matsubara, Y., & Nagamachi, M. (1996). A fuzzy rule induction method using genetic algorithm. International Journal of Industrial Ergonomics, 18, 135–145.
Jyh-Rong Chou, Ph.D., is an associate professor of the Department of Product Design, Fortune Institute of Technology (FIT), Taiwan. He serves as the Dean of Research and Technology Cooperation Office at FIT concurrently. He is also the executive director of the International Association of Organizational Innovation (IAOI) and the assistant editor of the International Journal of Organizational Innovation (ISSN 1943-1813). Hung-Cheng Tsai, Ph.D., is an assistant professor in the Graduate Institute of Industrial Design at National Kaohsiung First University of Science and Technology, Taiwan.
