A computer attitude scale for computer science ... - Wiley Online Library

Original article

A computer attitude scale for computer science freshmen and its educational implications G.E. Palaigeorgiou, P.D. Siozos, N.I. Konstantakis & I.A. Tsoukalas Multimedia Laboratory, Computer Science Department, Aristotle University of Thessaloniki, Thessaloniki, Greece

Abstract

The successful integration of computers in educational environments depends, to a great extent, on students’ attitudes towards them. Widely used computer attitude scales (CASs) focus on the beliefs of typical computer users and do not reveal the more refined attitudes of groups that use computers extensively and develop unique relations with them. This study presents the development and validation of a CAS especially designed for computer science freshmen (CASF). The scale consists of five factors, namely, self-confidence in previous knowledge, hardware usage anxiety, computer engagement, fears of long-lasting negative consequences of computer use and evaluation of positive consequences of computers in personal and social life. Using an analytic computer experience construct, the scale’s components were related to multiple aspects of students’ computer experience. CASF responses can inform a variety of instructional decisions and classroom management strategies for the first phase of the students’ studies.

Keywords

computer attitude scale, computer experience, computer science freshmen, gender differences, survey

Introduction

The rapid diffusion of information technology in everyday operations necessitates the detection of conditions and factors that influence (positively or negatively) the interactions between humans and computers. The efficient integration of computers in education, daily practice and work remains a key objective. Theoretical models for interpreting human behaviour recognize attitudes towards behaviour or an object as important indicators of prospective behaviour. Hence, the successful integration of computers in educational environments depends, to a great extent, on teachers’ and students’ attitudes towards them (Selwyn 1999). Students’ attitudes towards computers constitute a deAccepted: 1 June 2005 Correspondence: G. E. Palaigeorgiou, Multimedia Laboratory, Computer Science Department, PO Box 888, Aristotle University of Thessaloniki, Thessaloniki, Greece. E-mail: [email protected]

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terminant factor for both participation, and subsequent achievement, in information technology activities (Jones & Clarke 1994). This is of particular importance for educational environments that entail the intensive use of computers. In such environments, for example, computer science departments, students are more likely to develop more refined attitudes towards computers. However, most computer attitude scales (CASs) already in use focus on the beliefs of typical computer users and thus are not able to inform the design of adequate instructional initiatives for the smoother transition of students in the new environment. The aim of this study is the exploration of a CAS for freshmen students (CASF) for whom the use of computers is essential in the context of their academic programme and their prospective professional life. The study examines potential components of such a scale and their correlation with students’ computer experience, in order to validate both the scale and unveil its hermeneutic potential.

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Computer attitude of CSD freshmen

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What is computer attitude?

Designing CAS for CASF

Attitude is defined as a positive or negative sentiment, or mental state, that is learned and organized through experience and that exercises a discrete influence on the affective and conative responses of an individual toward some other individual, object or event. The theory of reasoned action (Ajzen & Fishbein 1980) supports the view that beliefs about an object imply the creation of an attitude toward the object. This attitude leads to behavioural intentions, which in turn affect actual behaviour, and finally, actual behaviour causes the revision of the initial attitudes. Attitudes can be examined at different levels of generality, depending on the action, target, context and time elements being evaluated (Smith et al. 2000). Computer attitude has been defined as a person’s general evaluation or feeling of favour or antipathy toward computer technologies and specific computerrelated activities (Smith et al. 2000). Computer attitude evaluation usually encompasses statements that examine users’ interaction with computer hardware, computer software, other persons relating to computers, and activities that involve computer use. Computer-related activities examined are either single instances of behaviour (e.g. specific software use) or classes of behaviour (e.g. attaining computer-related courses) (Smith et al. 2000). Various computer attitudes scales have been developed (e.g. Robertson et al. 1995; Selwyn 1997; Richter et al. 2000; Smith et al. 2000) but the CAS developed by Loyd and Gressard (1984) is one of the most often applied scales to undergraduate students (e.g. Al-Khaldi & Al-Jabri 1998). CASs can be analysed into several intrinsic variables, such as computer anxiety, computer liking, perceived usefulness, perceived easeof-use, self-confidence (SC) and perceived consequences for society (e.g. Loyd & Gressard 1984; Heinssen et al. 1987; Robertson et al. 1995; Levine & Donitsa-Schmidt 1998; Coffin & MacIntyre 1999; Richter et al. 2000; Beckers & Schmidt 2001; Hasan 2003). Nevertheless, researchers have argued that attitudes are not the only predictive factor of behaviour and thus significant inconsistencies between attitudes and behaviour can be expected. It is assumed that inconsistencies will be present when individuals are not free to behave in accordance with their attitudes, as well as when they do not posses the required competence (Winter et al. 1998).

As already mentioned, existing CASs have certain disadvantages. They focus entirely on an abstract level of computer attitude by investigating the beliefs of typical computer users and thus including characteristics that are relevant to any computer user. However, we argue that people’s evaluative beliefs about computers evolve continuously as they become more familiar with technologies, develop their skills, and increase their knowledge and understanding of computer applications. The content of their beliefs becomes more refined as computers are used to perform different tasks and are embedded in different contexts. Hence, the asymmetric relations with computers that are developed in environments of dissimilar intensity of use reduce the practical usefulness of general questionnaires, because these do not take into account the distinctive characteristics of different groups. A significant portion of computer attitude remains unexplored and unexploited. The dependence of CASF on computers, in both academic and professional contexts, underlines the need for a different level of analysis of their computer attitude. During the first phase of their studies, computer science students deal with a dramatically increased call for computer use. They are obliged to use computers for many hours a day and they also have to become accustomed to the new conditions for the long-term. Focusing on these distinctive characteristics, we tried to develop a CAS that is orientated towards CASF. Such a scale could help instructors to recognize more precisely students’ perceptions about computers and to design efficient learning activities for the early phase of students’ studies. In order to create the CASF questionnaire, we designed five attitude variables. Three of them have been examined extensively in previous CASs: (1) Anxiety regarding current or future interactions with computers: Computer anxiety refers to ‘negative emotions and cognitions evoked in interactions with computer-based technology’ (Bozionelos 2001). Students experience varying degrees of anxiety when required to use, talk, think or learn about computers and this anxiety may provoke computer use avoidance (Heinssen et al. 1987; Weil & Rosen 1995; Chua et al. 1999; Coffin & MacIntyre 1999; Beckers & Schmidt

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2001; Gaudron & Vignoli 2002). Avoidance can raise difficulties in students’ academic performance as computer usage is a prerequisite in completing working assignments. This factor was examined by eight questions (e.g. ‘I feel discomfort when I have to use the keyboard’). (2) Computer SC (or self-efficacy) expresses one’s belief in one’s capability to perform a task. Self-confidence influences task effort, determination, expressed interest, the difficulty of the goal selected for performance and the final outcomes (Murphy et al. 1989; Levine & Donitsa-Schmidt 1998; Coffin & MacIntyre 1999; Torkzadeh et al. 1999; Durndell & Haag 2002). A positive correlation has been identified between computer SC and the amount of computer use (Coffin & MacIntyre 1999; Torkzadeh et al. 1999; Hasan 2003), while a correlation has been found between higher levels of computer self-efficacy and both an increased performance in computer courses and greater computer competencies (Karsten & Roth 1998). ‘Computer SC’ was examined by six questions (e.g. ‘I feel I don’t have adequate knowledge to meet the department’s needs regarding computer use’). (3) Beliefs about the impact of computers on social and personal life: This factor focuses on individuals’ beliefs about the positive and negative impacts of computers in general (Richter et al. 2000) and appears several times as a component of computer anxiety (Marcoulides & Wang 1990; Beckers & Schmidt 2001). It incorporates into the scale the students’ appreciation of computers in work, education and personal communication. This factor was examined by five questions (e.g. ‘I believe that computers are essential tools in work and education’). The last two factors have not been used in other CASs and are tailored specifically for CASF. They attempt to introduce motivational influences into the scale. (4) Liking of the IT profession: While IT is a profession of many specialties, students’ prospective professional lives will be tightly related to computers and attitude toward the profession may indirectly express attitude toward computers in the context of future professional life. This factor aims at reflecting students’ commitment to computers from a professional perspective and it was examined by four questions (e.g. ‘I eagerly anticipate working as a computer specialist’).

(5) Fears of long-lasting negative consequences (LNCs) of computer use: Students’ are aware that their expected long-term relation with computers may harm them physically. Substantial media attention has been directed at potential adverse health effects such as musculoskeletal problems, eye strain etc, and these concerns may provoke diverse beliefs toward the longterm intensive computer usage and thus inhibit students’ academic development. Four questions were created to examine this factor (e.g. ‘I am afraid that continuous work with the computer will harm me physically’). Students were asked to indicate the level of their agreement or disagreement with CASF statements on a five-point Likert scale. Scores of negative questions were reversed in order to correlate higher values with more positive attitudes.

A measure for computer experience

In order to assess the validity of CASF, we used an extensive questionnaire for students’ previous computer experience. Many prior studies have identified positive correlations between various computer experience measures and CASs (Al-Khaldi & Al-Jabri 1998; Levine & Donitsa-Schmidt 1998; Winter et al. 1998; Smith et al. 2000; Gaudron & Vignoli 2002) and sometimes these correlations have been used as validity indicators of newly constructed attitude scales. Computer experience expresses the cumulative effect of exposure to computers and related events. However, to date, there is no commonly accepted approach for its measurement. In this study, we used an analytical approach of computer experience (Palaigeorgiou et al. 2004) that enabled us to examine its correlation to CASF from multiple perspectives. The proposed questionnaire included five categories of characteristics for computer experience:

Category A

A category that focuses on the general environment of experiences and examines general environmental conditions that are not directly related to computer use. It consists of the following variables:



Knowledge sources This variable aims at collecting information about the sources through which students acquire computer knowledge. Students identified the sources of their knowledge about computer terminology, computer use/ maintenance, office applications and programming, specifying up to four sources for each one and ordering them by importance. Alternative sources were books, magazines, Internet, personal practice, TV and Radio, ICT in school, family, friends and educational multimedia applications. A number (1–4) was assigned to each source, depending on its ranking order in the students’ responses, and then the average of these values were computed for each knowledge domain.

Social environment’s attitude Social environment’s attitude has been correlated positively with the computer use of its members (AlKhaldi & Wallace 1999). The questionnaire tried to examine social environment’s attitude and behaviour as perceived by students (Rice & Aydin 1991) and included the following questions: ‘Many of my friends and relatives like using a computer’, ‘My friends and relatives enjoy discussions about computer issues’, ‘My friends and relatives are proficient computer users’, ‘My friends and relatives use the computer intensively’.

Use opportunities and technical environment Access to computers is a prerequisite for their use. The more accessible a device is, the less effort is required for its use (Karahanna & Straub 1999). We measured ‘computer use opportunities’, asking whether individuals had access to a computer at home, to friends’ computers, to computers at school and to computers in Internet cafes and whether they were permitted to use them in whatever manner they wished. The variable ‘computer use opportunities’ was computed as the sum of different access points at which students used the computers in whatever manner they wished.

Category B

A category that focuses on computer use environment and examines the specific conditions of computer use. It includes the following variables:

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Goals of use Representative computer use, where students use their computers in order to carry out common tasks in a new or more productive way, is a different context of use from generative computer use, where the computer plays the role of canvas for the creativity of students (Hokanson & Hooper 2000). Communication and entertainment through computers also comprise different contexts of use (Downes 1999). In order to evaluate students’ prior goals of use, we created a list of tasks that can be grouped according to the contexts previously mentioned (e.g. representative computer use consisted of questions like ‘I have used the computer in order to write down some homework for school’ while generative use consisted of the question ‘I have used the computer in order to express myself artistically (draw pictures, compose music, edit video, write poems etc)’). Respondents specified whether or not they had realized each task. Technical environment The technical characteristics of the system with which we interact influence beliefs such as perceived usefulness and usability (Karahanna & Straub 1999). The questionnaire included questions about processor speed, storing space, Internet access, and the possession of computer peripherals and relevant devices (printer, scanner, digital camera, PDA). Questions regarding computer characteristics were ranked between 1 and 4, while questions concerning access to peripheral devices were assigned values of 0 and 1. The variable ‘technical environment’ was computed as the sum of all these values. Category C

A category that focuses on the content of the interaction and investigates the different types of computational objects that users have manipulated and the corresponding perceived knowledge. It consists of the following variables: Breadth of use The variable breadth of use examined the variety of interaction types that individuals have experienced and the diversity of computational objects that they have manipulated. The questionnaire included four classes of software, entertainment applications (games, DVD and



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Music players, CD/DVD burning software), Internet applications (browsers, e-mail clients, synchronous communications applications, file-sharing applications), office applications (text editors, spreadsheets, presentation software, databases) and programming (programming languages). For each application, students indicated the number of times that they had used it (many times, several times, few times, not at all). Perceived computer knowledge Perceived computer knowledge was estimated for three knowledge domains: Internet applications, office applications and programming. The absence of entertainment software knowledge was the only difference between factors ‘perceived computer knowledge’ and ‘breadth of use’. Students specified on a five-point Likert question their estimation of their knowledge for each type of application. Category D

A category that focuses on past events and includes the variable ‘negative events’. The concept of experience is often associated with preceding events that may have a transparent relation with current attitudes, behaviour, etc. Negative events have been shown to increase cognitive analysis as wells as physiological, affective and behavioural activity, and have been considered to be important sources of individual development that may influence future knowledge, skills and motivation (Holt & Crocker 2000). In accordance with previous research the questionnaire included two categories of ‘negative computational events’; one concerning computer learning difficulties (difficulties in learning new software, finding operation and maintenance information and understanding computer terminology) and the other regarding the problematic function of software/hardware (loss of data, software halts, hardware problems) (Holt & Crocker 2000). For each negative event, the respondents had to answer three questions: frequency of occurrence, how recently it had happened and the resulting dissatisfaction. Category E

A category that focuses on current use and includes the variable ‘intensity of use’. Intensity of use was computed as the sum of the responses in two ques-

tions; one concerning the frequency of use (many times a week, everyday etc.) and the other concerning the mean time usage in each use (1–2, 2–4 h, etc.). Participants

The questionnaire was administered to 102 first-year students of a computer science department in the context of a wider study that examined computer attitudes, experience and students’ ethical judgements regarding several computer-related scenarios. The questionnaire was distributed in an introductory programming course and students were requested to return it within two weeks. Eighty-one questionnaires were collected (response rate 79%) and 79 were utilizable, as two questionnaires failed in control questions. Fifty students were male (63.3%) and 29 female (36.7%). CASF consisted of 27 questions (12 formulated negatively and 15 positively) and computer experience included 92 questions. Questions appearing in this report were first translated into English by the authors and then refined by two English language teachers and one psychologist, in order to attain the maximum equivalence between statements in Greek and in English.1 Results Computer attitudes

A principal components factor analysis with varimax rotation was conducted on responses to the CASF questions. After a careful examination of the factors table, we excluded from the scale questions with low extraction communality, questions that failed to load to at least one factor with a value greater than 0.33 and questions with high loadings to more than one factor. We also eliminated questions with corrected item-total correlations less than 0.29 (Anastasi 1990). The remaining sixteen questions (nine formulated negatively and seven positively) appear in Table 1. The scale’s Cronbach’s a was relatively high (0.87). All questions had significant correlations with the scale’s total score and with a range of r values between 0.40 and 0.80 (Table 2). The additional deletion of any question would 1 The English version of CASF is available at http://ierg.csd.auth.gr/ questionnaires/CASFen.pdf



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Table 1. CASF questionnaire statistics Questions

Factors extracted with Varimax/Oblimin rotation Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

R

I feel discomfort when I have to use the keyboard I feel uneasy on the prospect of connecting computer’s cables (keyboard, printer, etc.) I feel at ease using complicated electronic devices The thought that I may make a mistake that will cause computer malfunction really scares me When I use or am about to use a computer, I feel anxious I feel uneasy when I am present at discussions that concern computers’ technical characteristics I feel I don’t have adequate knowledge to meet the department’s needs regarding computer use I feel uncomfortable when I sit next to experienced computer users I believe that computers are essential tools in work and education Computers make our life better Computers improve people’s communication I always look forward to using a computer I enjoy programming I eagerly anticipate working as a computer specialist I feel uncomfortable with the prospect of spending long hours in front of a computer I am afraid that continuous work with the computer will harm me physically

0.605/0.606

0.51

0.776/0.762

0.70

0.575/0.523

0.67

0.716/0.708

0.67

0.829/0.866

0.61

Eigenvalues % covariance Cronbach’s a

5760 36 000 0.79

0.585/

0.515

0.80

0.749/

0.733

0.70

0.834/

0.877

0.57 0.739/0.740

0.43

0.802/0.798 0.873/0.914

0.50 0.42 0.620/0.582

0.65

0.767/0.780 0.820/0.857

0.56 0.55 0.558/0.509

0.72

0.889/0.883

0.40

Scale 1834 11 461 0.71

1264 7903 0.77

1206 7538 0.74

1119 6996 0.67

69 898 0.87

Correlation is significant at 0.01 (two-tailed).

have decreased or left unchanged the Cronbach’s a. The value of Kaiser–Meyer–Olkin Measure of Sampling Adequacy was 0.782, which meant that the final sample of questions was suitable for factor analysis. Principal components factor analysis with both varimax and oblimin rotation on the remaining questions provided five interpretable factors (Table 1). The

extracted factors corresponded satisfactorily with the ones initially drawn: (a) Hardware usage anxiety (HUA): Explained the greatest part of the covariance (36%) and consisted of five questions with loadings ranging between 0.575 and 0.829 (e.g. ‘The thought that I may make a mis-



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take that will cause computer malfunction really scares me’). The factor’s Cronbach’s a was 0.79 and inter-question correlations had r values between 0.27 and 0.52. In regard to the designed factor ‘anxiety regarding current or future interactions with computers’, questions concerning ease of learning were excluded (e.g. ‘I believe that I can easily learn to work with computers’). (b) SC in previous knowledge: Explained 11.46% of the covariance and consisted of three questions with loadings ranging between 0.585 and 0.834 (e.g. ‘I feel uneasy when I am present at discussions that concern computers’ technical characteristics’). The factor’s Cronbach’s a was 0.71 and inter-question correlations had r values between 0.510 and 0.563. In regard to the designed factor, remaining questions were related only to SC in computer knowledge, while questions concerning a more general form of confidence were rejected (e.g. ‘I feel confident when I use computers’). (c) Computer as beneficial tool (CB): Explained 7.9% of the covariance and included three questions with loadings ranging between 0.739 and 0.873 (e.g. ‘Computers make our life better’). The factor’s Cronbach’s a was 0.77 and inter-question correlations

had r values between 0.46 and 0.54. Questions from the factor ‘Beliefs about computers’ impact in social and personal life’ referring to the negative consequences of computers were eliminated (e.g. ‘Computer use causes peoples’ alienation’). Because of this fact, the factor was renamed ‘CB’. (d) Engagement with the computers (EC): Explained 7.54% of the covariance and consisted of three questions with loadings ranging between 0.620 and 0.820. (e.g. ‘I always look forward to using a computer’). The factor’s Cronbach’s a was 0.74 and inter-question correlations had r values between 0.48 and 0.50. Factor EC was formulated by two questions from the designed factor ‘IT profession liking’ in combination with one from the factor ‘computer self-efficacy’. (e) Fears of LNCs of computer use: Explained 7% of the covariance and consisted of two questions with loadings 0.558 and 0.883 (e.g. ‘I feel uncomfortable with the prospect of spending long hours in front of a computer’). The factor’s Cronbach’s a was 0.67 and the correlation between these questions was 0.50. From the four questions of factor LNC, only two participated in its formation and therefore factor LNC requires further study.

Table 2. Correlations and gender differences

CASF HUA SC CB EC LNC Computer experience Breadth of use Perceived knowledge Social envnvironment’s attitudes Opportunities of use Technical environment Negative events Goals Intensity of use KS – Magazines KS – Internet KS – Personal

HUA

SC

CB

EC

LNC

CAS

1

0.60 1

0.21 0.31 1

0.51 0.48 0.32 1

0.43 0.46 0.29 0.29 1

0.84 0.82 0.53 0.72 0.64

0.54 0.44 0.12 0.34 0.06 0.37 0.46 0.61 0.44 0.32 0.30

0.57 0.48 0.33 0.40 0.01 0.29 0.44 0.53 0.40 0.48 0.22

0.35 0.15 0.17 0.11 0.19 0.08 0.40 0.32 0.19 0.12 0.04

0.27 0.43 0.01 0.13 0.10 0.33 0.26 0.55 0.31 0.04 0.18

0.29 0.21 0.01 0.16 0.09 0.23 0.17 0.34 0.26 0.16 0.04

0.61 0.49 0.19 0.36 0.02 0.38 0.50 0.69 0.47 0.38 0.27

Correlation is significant at 0.05 (two-tailed). Correlation is significant 0.01 at (two-tailed).

CASF, computer science freshmen; HUA, hardware usage anxiety; SC, self-confidence; CB, computer as beneficial tool; EC, engagement with the computers; LNC, long-lasting negative consequences; CAS, computer attitude scale.



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In each factor, the corrected item-total correlation of each question had values greater than 0.45, demonstrating the homogeneity of each subscale (Anastasi 1990). Between-factor correlations had values less than 0.80, which is evidence that each factor contributed in a unique way in the total score, although, as expected, there was some multicollinearity. The scale was rated with values ranging between 29 and 79, covering almost the entire range of possible values. Therefore, the values of different statistical indicators for CASF were compatible with most common requirements for scales’ statistics. In Table 3, attitude differences between men and women are presented. Significant gender differences were detected in factors HUA (t 5 4.616, Po0.001), SC (t 5 4.405, Po0.001) and CB (t 5 2.564, Po0.014). Gender differences were also evident in the overall scale (t 5 4.540, Po0.001).

Computer experience

Multiple principal components factor analysis with varimax rotation on breadth of use, perceived knowledge and goals of use extracted the theoretically designed factors, interpreting 64.7% of breadth of use variance (factors: Internet applications, office applications, entertainment applications and programming), 75.5% of perceived knowledge variance (factors: Internet applications, office application, programming) and 58.9% of goals of use variance (factors: re-

Table 3. Gender differences in CASF Male

HUA SC CB EC LNC Scale

Female

t

Average

SD

Average

SD

4.29 3.73 4.01 3.81 3.51 3.94

0.69 1.28 0.63 0.88 1.12 0.61

3.38 2.56 3.52 3.43 3.21 3.24

0.95 0.95 0.93 1.01 1.31 0.73

4.616 4.405 2.564 1.587 1.080 4.540

presentative, generative use, use for entertainment and communication). Factor analysis on negative events also generated the designed factors (learning difficulties and hardware/software problems), which explained 57% of the variance. However, the question ‘when did this last happen?’ was not considered after students’ negative comments on specifying the periodicity of events’ appearance, while the combination of frequency of events with their emotional attribution did not correlate significantly with other measures of computer experience. Questions for perceived attitudes of the social environment formulated a subscale with a Cronbach’s a of 0.83. Computer experience demonstrated a coherent structure. A positive social environment for computer use was correlated significantly with the use of entertainment applications (r 5 0.33, Po0.001) and the use of Internet applications (r 5 0.36, Po0.001). Learning from Internet and magazines were the only two knowledge sources that were correlated significantly with intensity and breadth of use. Personal practice was correlated positively with perceived knowledge (r 5 0.34, Po0.001) and negatively with learning difficulties (r 5 0.39, Po0.001). Frequency of learning difficulties was correlated negatively with breadth of use (r 5 0.48, Po0.001), intensity of use (r 5 0.50, Po0.001) and opportunities for use (r 5 0.32, Po0.001). The variable ‘computer use opportunities’ was correlated significantly with breadth of use (r 5 0.58, Po0.001) and perceived knowledge (r 5 0.48, Po0.001). Technical environment was mainly correlated with goals of use (r 5 0.38, Po0.001) and breadth of use (r 5 0.38, Po0.001).

P

o0.001 o0.001 o0.014 o0.117 o0.283 o0.001


CASF, computer science freshmen; HUA, hardware usage anxiety; SC, self-confidence; CB, computer as beneficial tool; EC, engagement with the computers; LNC, long-lasting negative consequences.

Relating computer attitude and computer experience

Correlations between computer attitude subscales and computer experience variables are presented in Table 2 (only the significantly correlated knowledge sources appear). We can observe that factors CB and LNC were correlated relatively less with computer experience variables, compared with the other subscales. For example, their correlation with intensity of use was low, although significant (r 5 0.32, Po0.001 and r 5 0.34, Po0.001). In addition, a positive attitude toward most subscales was correlated with computer



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learning from magazines. Social environment attitude was related to SC (r 5 0.33, Po0.001) and as expected, the frequency of appearance of negative events was correlated negatively with EC (r 5 0.33, Po0.001). More personal practice was related to less HUA (r 5 0.30, Po0.001). Breadth of use, variety of goals and intensity of use were correlated with most factors of CASF. Generally, the correlations were compatible with our interpretation of each CASF factor. The entire scale was significantly correlated with all computer experience variables, except technical environment and social environment attitude. It demonstrated greater correlations with intensity of use (r 5 0.69, Po0.001), breadth of use (r 5 0.61, Po0.001), perceived knowledge (r 5 0.49, Po0.001), variety of goals (r 5 0.50, Po0.001) and computer learning from magazines (r 5 0.47, Po0.001). Therefore, the overall scale was also correlated with various aspects of students’ experience. Predicting computer experience and CASF

Multiple regression analyses, using the stepwise method, were conducted in order to examine the ability of CASF factors to predict three general computer experience dimensions; namely, intensity of use, perceived knowledge and breadth of use. Summarized data for these models are given in Table 4. HUA (b 5 0.417, Po0.001) and EC (b 5 0.350, Po0.001) explained 43% of intensity of use variance, while EC (b 5 0.283, Po0.000) and SC (b 5 0.234, Po0.001) explained 41% of breadth of use variance. The predictive ability of CASF was adequate in comparison with previous CASs. However, because of the nature of the computer experience measure we used, it was interesting to examine how different experience factors could predict factors of CASF and the overall scale. In Table 5, we present the regression analysis conducted for males and females, using computer experience dimensions as independent variables for the five factors of CASF and the overall scale. The predictive ability among males and females differed considerably, being less for women. For females and for factors CB and LNC, the regression analysis did not offer statistically significant results, while for the overall scale, the computer experience construct explained 55% of the males’ variance and

Table 4. Predicting computer experience from CASF factors Variables Predicting intensity of use HUA EC Predicting perceived knowledge SC EC Predicting breadth of use SC HUA CB

b

t

P

R2 adjusted 0.43

0.417 0.350

3.954 3.317

0.001 0.001 0.23

0.304 0.283

2.544 2.366

0.013 0.021 0.41

0.327 0.283 0.234

2.780 2.482 2.404

0.007 0.016 0.019

CASF, computer science freshmen; HUA, hardware usage anxiety; SC, self-confidence; CB, computer as beneficial tool; EC, engagement with the computers; LNC, long-lasting negative consequences.

only 34% of the females’ variance. In contrast, computer experience could explain a relatively high percentage of variance for males’ attitudes, (51% of HUA, 54% of SC, 70% of EC and 20% of LNC). Dissimilarities were also detected in the factors of experience that participate in the regression models for each gender. However, it may be claimed that these exist because of the different levels of intensity of computer use between males and females (t(77) 5 4.468, Po0.001). Intensity of use, frequency of appearance of negative events and computer learning from magazines emerged as the most significant prediction variables. These variables participated in the prediction of two CAS subscales and in the prediction of the overall attitude. The inclusion of the technical environment with a negative sign in different regression models was unexpected. It is possible that inexperienced users bought new, and consequently powerful, computer systems when they were informed of their acceptance by the Computer Science Department, while experienced users maintained their older PCs. In addition, the factor CB was predicted by the intensity of entertainment application usage (b 5 0.410, Po0.001), a finding that reflects the positive aspect of playing games, watching DVDs, etc. Further, the frequency of appearance of negative events predicted 20% of LNC variance, which indicates that repeated unsuccessful



0.48

Adjusted R2 0.63

0.50

0.54

14.766

0.283

0.567

0.369

0.273

0.303

0.608

0.18

0.22 0.52

0.54

6.115 24.555

0.47

Female All

0.11

0.11

–

–

0.15

0.16

0.759

0.348

0.293

0.787

Female

1.019

0.64

0.70

0.85

0.89

21.386

0.367 0.903

0.459

0.485

0.284

0.580

Male

EC

12.678 13.298

0.401

Female All

6.431 –

0.364

Male

CB

0.450

Male

0.44

0.49

0.18

0.20

11.361 10.690

0.363

0.229

0.212

0.295

0.367

All

LNC

–

0.446

0.19

0.20

16.661

Female All

0.328

0.354

0.491 0.209

0.406

Female All

0.52

0.55

0.28

0.34

0.50

0.52

16.511 5.507 23.330

0.332

0.385

0.307

Male

CASF scale

BoU, breadth of use; Kn, perceived knowledge; KS, knowledge source; NE, negative events, G, goals; CASF, computer science freshmen; HUA, hardware usage anxiety; SC, selfconfidence; CB, computer as beneficial tool; EC, engagement with the computers; LNC, long-lasting negative consequences.


0.16

0.67

0.51

R2

0.20

0.422

13.645 5.263 19.951

0.261

0.314

0.327

0.194

0.369

0.252

F

0.269

0.397

0.448

24. Intensity of use

23. G – Entert.

22. G – Commun.

21. G – Repres.

20. G – Gener.

19. NE – H/S Probl.

Learn. Diff.

18. NE –

17. Techn. Env.

16. Use Opport.

Practice

15. KS – Pers.

14. KS – Friends

13. KS – Family

12. KS – School

11. KS – Internet

10. KS – Magazines

9. KS – Books

Attitudes

8. Social Env.

Programming.

7. Kn –

6. Kn – Office apps

5. Kn – Internet

Programming.

4. BoU –

3. BoU – Entert.

2. BoU – Office apps

1. BoU – Internet

Male

Male

Female All

SC

HUA

Table 5. Predicting CASF factors from computer experience

Computer attitude of CSD freshmen 339


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learning experiences are related to a negative attitude towards long-lasting computer use. Discussion

We designed and constructed a CAS adapted to freshmen in computer science departments. It consisted of the following: two factors strongly correlated with computer experiences (SC in previous knowledge and hardware use anxiety), two factors relevant to general attitudes towards the IT profession (computer engagement and fears of LNCs of computer use) and finally a factor that expresses the evaluation of positive consequences of computers in personal and social life. The scale was considerably related to computer experience and succeeded in revealing important views of students’ reality. CASF factors can inform a variety of instructional decisions and classroom management strategies to meet the diversity of students’ needs. For example, instructors can exploit CASF to discern differences between female and male attitudes. If CASF reveals significant differentiation, then instructional decisions regarding, for example, team formation may be revisited. Using the scale, we concluded that men and women in our sample had similar engagement with computers and concerns for the future effects of continuous computer use, but women were more anxious about hardware usage, felt less SC in their previous knowledge and assessed less positively the consequences of computers in personal and social life. Hence, instructors could opt to form heterogeneous teams to bring the conflicting micro-cultures into contact and support the weaker students (Hooper & Hannafin 1991; Saleh et al. 2005). In addition, in the case that CASF points to lower students’ SC in their prior knowledge, initiatives to increase SC can be developed. For example, assignments of medium difficulty can help students to gain confidence in their own abilities by experiencing success at a variety of tasks (Keller 1983). Higher levels of computer SC are of great importance, because they have been correlated to improved performance in computer courses (Karsten & Roth 1998). Furthermore, CASF helped us to identify that students in our study were cautious about the negative long-lasting consequences of computer use for themselves. This factor had the lowest score for men and it had the second lowest

value for women, following SC for previous knowledge. The finding suggests the study of future working conditions and ergonomics issues for computer use in the early semesters of the curriculum, in order to help students understand the real size of the problem and confront it effectively. However, student responses to CASF can be presented directly in classroom settings with the aim of introducing psychological factors that may influence their academic development. Hence, CASF can be exploited as a reflective tool for motivating students to acknowledge their attitudes and for understanding the existence of various colleagues’ perspectives. It can be used as a mean for presenting concepts like computer confidence, computer engagement or computer anxiety and as an incentive for demonstrating academic excellence. Finally, CASF enables the longitudinal monitoring of freshmen computer attitude. Computer literacy competences develop continuously and different trends in CASF factors may be observed, e.g. vanishing differences between men’s and women’s attitudes (e.g. King et al. 2002). These trends can reveal correlations between initial attitudes and students’ performance in their academic career and may also contribute in sketching the profile of people interesting in studying computer science.

Limitations

The results of this survey have certain disadvantages. Its development was tested on a relatively small sample and efforts to assess its reliability and validity must be continued. Respondents completed the questionnaires in their own time and place and therefore students’ concentration and understanding of items could not be ensured. Some subjects may have responded according to their beliefs about what the experimenter wanted to know. From a methodological perspective, future research should strive to use qualitative methods in order to validate and elaborate quantitative findings. Future objectives also include the design and addition of more questions for each factor, the replication of these findings to larger samples of students and the incorporation of CASF factors into more complete models of interpreting students’ academic development and success.



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