Attitudes towards Computer: Statistical Types and their Relationship with Computer Literacy
Diana Saparniene Siauliai University, Lithuania
Gediminas Merkys Kaunas University of Technology, Lithuania
Gintaras Saparnis Siauliai University, Lithuania
ABSTRACT. The article presents the results of a diagnostic analysis on students’ computer literacy. The study includes students from Lithuanian universities and colleges (N=1004).
In order to determine students’ emotional and motivational connection with computers, efforts are being made to classify and define their statistical categories. This will also enable a connection to be established between their computer literacy skills and these categories. In today’s society, the significance of computer literacy is growing due to our dependence on technology. It is now considered as crucial as reading, writing, and mathematics were in the 19th and 20th centuries (Anderson, 1983).
Computer literacy, also known as socialization or secondary socialization, is widely recognized by scholars as essential for successful socialization and professional careers. Therefore, education plays a critical role in societal development and addressing the issue of literacy, specifically computer literacy. Consequently, research globally, including Lithuania (M.), actively highlights the importance of computer literacy.
Several researchers including Hayden (1999), D. Johnson and M. Eisenberg (1991), S. McMillan (1996), A. Mitra (1998), J. Oderkirk (1996), R. Petrauskas (1998), D. Saparniene (2002), et al., have examined the insufficient investigation on the impact of psychological factors on computer literacy level.
Similarly, M. Igbaria and A. Chakrabarti (1990), A. Harrison and R.K Jr.Rainer ( 1996 ), G.A Marcoulides, Y.Stocker, and L.D Marcoulides(2004) ,A.Brogos(2005) et al., conducted research on how psychosocial factors affect computer literacy level.
Despite extensive research, little attention has been given to the impact of non-cognitive personality traits on computer literacy. The question of whether different attitudes towards computers lead to varying levels of computer literacy has not been adequately addressed. It is crucial to consider sociopsychological and socioeducational factors when examining academic achievements. While intellect and general knowledge have traditionally been associated with academic success, emotions and motivation also play a role in relation to computer literacy. Attitude is an internal psychological state that influences behavior, which can be observed through actions and words. For example, actively avoiding computers indicates a negative attitude towards them. It should be noted that attitude is shaped by experiences and their impact on new situations, rather than being an innate instinct.
Attitudes can be formed and changed through experience and internal or external factors. The quality of computer literacy is closely linked to motivation, an essential component of attitude. When a student lacks motivation to work with a computer, their learning outcome suffers. Conversely, a motivated computer user willingly engages with the technology, even in unfavorable circumstances. In everyday language, motivation levels are often measured by factors like “time spent working with a computer” or “degree of effort.”
According to Keller (1999), motivation is the process that drives individuals towards an object and increases their efforts in relation to that object. For many years, scientists have devoted significant efforts to understanding this inner process (Schunk, 1991). Currently, researchers are exploring how inner motivation influences achievements and work behavior with computers. Inner motivation is connected to an individual’s personality traits such as demands, interests, and wishes. It plays a role in uncovering a pupil’s inner interests (Astleitner, Keller, 1995; Keller, 1999).
Inner motivation involves repeating actions that generate positive emotions. All types of inner motivation mentioned above contribute to defining a person’s satisfaction and enjoyment from successfully using a computer. Identifying the factors that inspire motivation and demotivation can be challenging, including whether these circumstances are internal or external, consistent or variable, and controlled or uncontrolled. To prevent computer demotivation, researchers (Pancer, George, Gebotys, 1992; Hancock, 1995) propose the following: 1) ensuring that computer work aligns with the current or future needs of the user.
To meet these requirements, it is necessary to consider the goals of the students and clearly state the purpose. Additionally, adjusting the level of difficulty and working with computers can boost self-confidence, confidence in success, and encourage continued effort. It is crucial for students to feel satisfied and maintain motivation while working with computers. They should perceive the benefits they receive as fair, neutral, and long-lasting. Overall, there is a clear connection between emotions and motivation.
Existing scientific literature lacks sufficient focus on the impact of non-cognitive personality traits on computer literacy levels. When students face personal issues, their performance in computer subjects and task concentration may be affected. On the other hand, positive emotions like enthusiasm and satisfaction can aid in completing challenging tasks and achieving academic success.
This article explores the potential impact of different factors, such as computer literacy and non-cognitive personality traits, on identifying statistical types among a population of students. The primary focus is on how attitudes and other traits influence computer literacy in higher education. Using multidimensional statistical methods, the study categorizes students based on their emotional-motivational relationship with computers and their actual computer skills.
The study’s empirical foundation consists of multiple diagnostic studies involving 1004 surveyed students from four Lithuanian universities, as well as five high schools and colleges. The sample comprised 84.7% university students and 15.3% high school and college students.
The majority of the sample, consisting of 73.1% (N=733) of students, came from management and economics study programs. The remaining respondents (22.9%, N=271) included students from various other areas such as education, philology, informatics, physics, mathematics, technical, agricultural, and health sciences. This study was conducted with voluntary participation and anonymity ensured. It used a test (theoretical and practical) on computer literacy (CL), as well as two anonymous closed type questionnaires titled “Student and computer” and “Student and studies”. These questionnaires contained questions about computer literacy and studies respectively, and were created by Saparniene in 2002.
The article examines the study’s use of different research instruments, including tests, to evaluate respondents’ attention, intelligence (both overall and verbal/non-verbal), and knowledge. These instruments had been previously developed by other researchers and utilized in prior studies. The main focus of the article is analyzing empirical research findings that identify students’ statistical types based on their attitudes towards computers and how this correlates with their computer literacy.
The current research involves analyzing the responses from participants in the “Student and computer” questionnaire, specifically regarding questions about emotional-motivational relationships. Additionally, the researchers are assessing the results of a computer literacy test. The article primarily focuses on the psychometric validity of these variables being studied. The computer literacy test was created with input from experts and consists of two sections. The first section evaluates participants’ overall comprehension of computers using 19 theoretical questions.
The second part of the test involved 24 practical tasks to assess the respondents’ proficiency in using the applied software. The test results were analyzed, and statistics such as percentage frequency, central tendencies (average, standard error, and standard deviation) were calculated and presented in Table 1. Another table, Table 2, displayed the reliability rates for measuring computer literacy. These rates demonstrated that the constructed scale for measuring computer literacy is highly reliable (Bortz, 1993; Anastasi & Urbina, 2001; Merkys, 1999). Parameters for the Computer Literacy Test Scale were also provided in Table 1, including standard error and deviation values as well as scale averages for both theoretical and practical parts of the test. Similarly, Table 2 showed reliability indices such as Cronbach coefficient, Gutman Split half coefficient, and Spearman Brown coefficient for both theoretical and practical parts of the test. Additionally, a separate scale was developed to measure emotional-motivational relationship with a computer through intuitive selection based on qualitative analysis before being empirically verified.
The psychometric applicability of the stimulus material on the initial emotional-motivational scale was validated using factor analysis, which also facilitated the construction of sub-scales. Through this method, five factors (sub-scales) were identified and given the names: “Computer as a hobby and an object of admiration”, “Computer as a source of fatigue, stress and dissatisfaction”, “Indifference to a computer”, “Dissociation from computer enthusiasts and fanatics”, and “Computer as a factor of improvement and education”. The ratings of the statements and the extracted factors showed significant correlations.
The obtained fluctuation limits of the correlation coefficient are between 0.41 and 0.79. The factor descriptive variation ranges from 16% to 8%, with a total explained variation of 53.1%. The Kaiser-Meyer-Olkin (KMO) coefficient, which is relatively high at 0.92, explains the applicability of the matrix for factor analysis. The inner consistency of single factors, measured by the Cronbach alpha coefficient, ranges from 0.59 to 0.83 and all five factors are homogeneous. The combined scale also shows a high inner consistency of 0.69.
Overall, the scale parameters meet the methodological standards for construct reliability and factor validity. It is important to note the meaningful categorization of factors. Factors 1 and 5 indicate positive attitudes towards a computer, factors 2 and 4 indicate negative attitudes, and factor 3 represents indifference. Therefore, the factors consist of variables that represent similar dimensions. The subscale “Computer as a hobby and an object of admiration” (15.9% variation) includes sentences that reflect attitudes of computer enthusiasts.
This factor includes statements such as “My most important hobby is computer”, “Living without a computer for me is the same as living without air”, “If anybody deprived me of the possibility to use a computer, my life would become humdrum”, etc. These statements clearly demonstrate that factor 1 represents a strong emotional-motivational satisfaction from working with a computer. Therefore, working with a computer and having computer competence are equated to achieving success in life and gaining life experience.
The subscales “Computer as a source of fatigue, stress, and dissatisfaction” (11.5% variation) and “Dissociation from computer enthusiasts and fanatics” (8% variation) consist of statements reflecting negative attitudes towards computers. In this context, emotional dissatisfaction is revealed through statements such as “If I were able, I would ‘run away’ from the computer, but the situation is such that I must start studying this subject”, “While working with a computer, I constantly feel trouble and get irritated”, “The computer and I are two opposites”, “The computer causes me continual stresses”, etc. Likewise, the statements “I feel bored in the company of those who are delighted by computers” and “I find computer fanatics strange” illustrate a sense of detachment from individuals who have a strong enthusiasm for computers.
The factor of indifference towards the computer (10% variation) comprises statements that indicate complete apathy towards computers by the respondents.The statements in this subscale include “I am indifferent to the computer,” “I can live without a computer,” and “A computer is just a tool for my work.” All the statements in subscale 5, which focus on the computer as a factor of improvement and education and account for 8% of the variation, express a positive attitude towards computer technologies and a strong awareness of their impact on success in life.
The study indicates that the quality of computer literacy is influenced by the emotional-motivational relationship with a computer. Results show that students who have a positive connection with a computer tend to possess higher computer literacy skills, whereas those with a negative attitude demonstrate lower proficiency. To classify respondents according to their emotional-motivational relationship with computers, cluster analysis was employed.The preferred method for cluster analysis was k-means because there were a considerable number of surveyed individuals and objects that needed to be classified. The clusters’ breaks reflected their structure.
The surveyed participants were divided into 5 scales according to their emotional-motivational connection with a computer: “Surely YES,” “almost yes,” “do not know,” “almost no,” and “surely NO.”
Dividing the respondents (N=1004) into 3 clusters provided the most informative and interpretable results. This is visually depicted in Figure 1. Group 1 comprised 6.5% of the surveyed individuals, group 2 comprised 33.5%, and group 3 comprised 20.1%.
Cluster 1 and cluster 3 had a relatively higher percentage of women (NMale -39.6%, NFemale-49.4% in cluster1; NMale-15.8%, NFemale-21.9% in cluster3), while cluster2 had more men (NMale-44.7%, NFemale-28.7%).
The evaluation of subscales revealed that cluster1 consisted of “computer enthusiasts” (46.5%), cluster2 consisted of “computer fans” (33.5%), and cluster3 consisted of “computer phobics” (20%). This typology is presented in Figure1(modelofthreeclusters)(N=917).
The classification of students based on their attitudes towards computers can be organized into three clusters.Oneof these clusters, Cluster1, consists of individuals who maintain a neutralandfunctionalstanceintheirrelationshipwithacomputer.Thisgroupwassurveyedand theresultsindicatetheir inclinationtowardsthis particularattitude.(N=917)
Metaphorically speaking, the way people view a computer is similar to how they view other pieces of equipment, like a vacuum cleaner. They don’t have any strong feelings of affection or admiration towards it, nor do they have any fear of using it. Instead, they see it as a tool to perform specific tasks. This group of people, who make up nearly half of the surveyed population (46.5%), can be called “functionalists” due to their utilitarian and practical approach.
It is possible to hypothesize that the structure of the statistical types among school age students may vary slightly. It is believed that the number of “fans and enthusiasts” would increase while the number of “functionalists” would decrease. Cluster 2 consists of computer enthusiasts who view computers as a hobby and object of admiration. They express their emotions through statements such as “Life without a computer for me is like living without air” and “If someone took away my ability to use a computer, my life would become boring”.
The participants in this study have different attitudes toward computers. One group has a positive attitude and sees computers as a means of improvement and education. Another group has a negative attitude and views computers as a source of weariness, stress, and dissatisfaction. They feel indifferent towards computers and are uncomfortable around computer enthusiasts. This group does not see computers as essential for their improvement and education, and they express emotional-motivational dissatisfaction with computers. This group is referred to as “computerphobes.” The study also found a correlation between these attitude clusters and the participants’ computer literacy levels.In other words, we have explored whether there is a correlation between a student’s attitude towards computers and their level of computer literacy. Our findings suggest that students who have a positive attitude towards computers tend to have higher computer literacy skills, whereas those with a negative attitude tend to have lower computer literacy skills.
When analyzing the data, it has been valuable to investigate how gender influences the statistical interaction of the two variables mentioned earlier. The graphical analysis in Figure 2 clearly illustrates that respondents from group 2 (both male and female) or those who are enthusiastic about computers achieve the best results, while respondents from group 3, who have a completely negative attitude towards computers, perform the worst. Additionally, the analysis shows statistically significant differences in attitudes between males and females in groups 1 and 2 (cluster 1 – t = 4.1, p).
