Full Paper

Michael Newby

newbym@cbs.curtin.edu.au

School of Information Systems

Darrell Fisher

ifisherd@info.curtin.edu.au

Science and Mathematics Education Centre

Curtin University

Computing has been a subject of academic study since the 1960s. Initially it was taught in courses with titles such as Computer Science, Computer Studies or Electronic Data Processing and these were intended for the computing specialist, who would start their careers as programmers or systems analysts. Computer Science has established itself firmly as a discipline in most universities and in such courses the emphasis is on the study of computer systems themselves. The other terms mentioned have, in general, been replaced by Business Computing or Information Systems, which is now emerging as a discipline in its own right. Courses under these titles concentrate on the application of computers to business problems. In addition, many universities offer programmes in Software Engineering and Computer Engineering. All the courses mentioned involve the study of programming as the means by which computer based systems are developed.

The introduction of the microcomputer in the early 1980s led to the wider use of computers throughout tertiary education in courses including business, education and engineering. Here the computer is often used as a tool to assist in learning, as a means of delivering educational material and for on-line assessment. More recently, the availability of multimedia has extended the use of computers to other areas such as graphic design, and the Internet has made the workstation an invaluable educational and research tool. This has led to the inclusion of some form of computer education in most disciplines at the university level.

The one aspect that most computing courses, both specialist and non-specialist, have in common is the computer laboratory class. This is understandable given that using a computer is perceived as a skill. For specialist courses this would mean learning to program, something that cannot be done simply by reading a book and requires practice (Azemi, 1995). The skill in using computers must be mastered before any progress can be made, and laboratory classes provide an opportunity for students to gain proficiency.

The joint ACM-IEEE Curriculum Task Force recommended that introductory computer science courses should be supported by extensive laboratory work (Denning et al., 1989; ACM/IEEE-CS, 1991). More recently the ACM SIGCSE Working Group on Computing Laboratories published guidelines for the use of laboratories in Computer Science education (Knox et al, 1996). Their report was predicated on a number of assumptions, one of which was that laboratory experiences are relevant in most Computer Science courses across all levels from literacy and language courses for non-specialists to graduate level theory courses. It discusses a number of aspects in detail, and these are the scope of laboratories, the relationship between lecture and laboratory, pedagogy, an Internet repository, institutional support and the use of technology. Although the focus of this report is the use of laboratories in Computer Science Education, many of its recommendations are relevant to non-specialist laboratories.

Whatever the computer laboratory class experience that is to be provided, there are a number of ways in it can be done. The two extremes are the closed or formal laboratory (Prey, 1996; Lin, Wu & Chiou, 1996) and the open laboratory, sometimes known as the drop-in laboratory. The formal laboratory is scheduled in the same way as lectures and tutorials with specific exercises being set for students. They are generally staffed by a lecturer or higher grade who is available to help guide the students. On the other hand, open laboratories allow students to come and go as they please with technical assistance being provided by laboratory demonstrators who are often senior students. For these an instructor assigns a problem and students works on it in their own time usually on their own. Most computing classes run in Australian and UK universities provide formal scheduled laboratory classes, with different levels of prescriptriion with respect to the work to be done. However, in the United States, it seems that the open laboratory is the norm (Prey, 1996) and a study showed that only about a third of the university courses surveyed used formal classes (Denk, Martin & Sarangarm, 1993).

Learning environments have been a subject of academic research for over thirty years (Fraser, 1991), and although the concept of a classroom environment is a subtle one, teachers have always been aware of it in an informal manner. The research in this area has succeeded in conceptualising learning environments, and it arose from two independent programmes which started at about the same time. As part of the evaluation of the Harvard Physics Project, Anderson and Walberg (1974) developed the Learning Environment Inventory (LEI). Working in a quite separate field, Moos developed a number of social climate scales, including those for use in correctional institutions (Moos, 1968) and psychiatric hospitals (Moos & Houts, 1968). These instruments led to the development of the Classroom Environment Scale (Trickett & Moos, 1973). Other instruments for assessing a number of different contextual learning environments have been developed since that time (Fraser, 1991).

The study of classroom environments has demonstrated that perceived classroom environment may be predictive of student learning. Haertel, Walberg and Haertel (1981) carried out a meta-analysis which encompassed 823 classes in 8 subject areas and represented the perceptions of 17,805 students in 4 nations, They found that student achievement was enhanced in those classes which students felt had greater cohesiveness, satisfaction and goal direction and less disorganisation and friction. Fraser (1986) gives a table of 45 studies into associations between classroom environment and various student outcomes, cognitive, affective and behavioural. There have been many further investigations since that time and it is clear that a student's perception of classroom environment plays an important role in learning.

The increased use of computers in classrooms has led to studies to evaluate the effectiveness of computer assisted learning (Maor & Fraser, 1993, Teh & Fraser, 1994) and to investigate the association between gender, computer experience and perceived environment (Levine & Donitsa-Schmidt, 1995).

This study involved the refinement of a previously developed instrument called the Computer Laboratory Environment Inventory (CLEI) used in conjunction with an instrument for measuring attitude towards computers and computing courses (ACCC) (Newby & Fisher, in press).

The instrument for assessing computer laboratory environment is based on the actual version of the Personal form of the Science Laboratory Environment Inventory designed by Fraser, Giddings and McRobbie(1993). The SLEI has five scales Student Cohesiveness, Open-endedness, Integration, Rule Clarity and Material Environment, using seven items per scale. Initially, all scales except Rule Clarity are seen as being relevant to computer laboratories. The nature of a computer laboratory class is such that rules tend to be restricted to "No food or drink in this laboratory", or deal with the legal and appropriate use of software, whereas in a science laboratory, the primary purpose of rules are for student safety (Collette & Chiappetta, 1994). A further dimension of a computer laboratory environment is the suitability of the technical facilities, which in the case of computer classes means the technology, both hardware and software. This would be ascertained by answers to questions such as "is the software suitable for the specified tasks?" and "is the hardware powerful enough to handle the number of users?". This new scale measures Technology Adequacy which is the suitability of the technology for the task required. The Material Environment dimension of SLEI covers the physical laboratory environment and this was included in the original version of the CLEI (Newby & Fisher, 1996). However, after some pilot surveys, and discussions with staff and students involved in laboratory classes, it was felt that the availability of the laboratory was more important than the physical environment. As a result of this another new scale Laboratory Availability was included in the CLEI. Both the Geography Classroom Environment Inventory (Teh & Fraser, 1994) and the Computer Classroom Environment Inventory (Maor & Fraser, 1993) use a single scale for measuring the adequacy of resources, and this scale incorporates both the physical environment and technology adequacy. In the former instrument it is called Resource Adequacy and in the latter Material Environment. The researchers believe that for university level courses, Technology Adequacy is a separate scale and Material Environment is less important. A description of the scales used in the modified version of the instrument is described in Table 1 with a sample item from each scale.

The instrument for assessing students' attitudes towards computers and computer courses (ACCC) has been described in a previous study (Newby & Fisher, in press). For assessing attitude towards computers, the scales Anxiety, Enjoyment, and Perceived Usefulness of Computers were based upon an instrument devised by Loyd and Loyd (1985). They also included a Confidence scale but differentiating between lack of confidence and anxiety proved difficult so the Confidence scale was omitted. A fourth scale was included to measure the student's perception of the usefulness of the course. As with the CLEI, all the scales have seven items and a description of the scales used in the instrument is given in Table 2 together with a sample item from each scale.

The instrument was administered to 208 students undertaking courses within the Curtin Business School. All courses involved a laboratory component. The sample was representative with respect to gender, age, mode of study, and level of study (undergraduate / postgraduate). The classes surveyed included those in which the development of software was the focus of study, such as Information Systems, and others in which the computer was used as a tool for word processing, spreadsheets and access to the Internet. The computer systems used were standalone PCs, networked computers, or a multi-access system.

+ Items designated (+) are scored 1,2,3,4 and 5, respectively for responses Almost Never, Seldom, Sometimes, Often, Almost Always

- Items designated (-) are scored 5,4,3,2 and 1, respectively for responses Almost Never, Seldom, Sometimes, Often, Almost Always

+ Items designated (+) are scored 1,2,3,4 and 5, respectively for responses Strongly Disagree, Disagree, Not Sure, Agree, Strongly Agree

- Items designated (-) are scored 5,4,3,2 and 1, respectively for responses Strongly Disagree, Disagree, Not Sure, Agree, Strongly Agree

Table 3 provides some statistical information about the CLEI when used with the sample of 208 Business students. The Cronbach alpha reliability measures the internal consistency of the scale and the figures presented in the table show that for the seven item scales the alpha reliability figures ranged from 0.60to 0.89 indicating that the scales are satisfactory in terms of their internal consistency. The mean correlation of a scale with the other scales of the questionnaire is accepted as a measure of discriminant validity and is the extent to which the scales are unique in what they are measuring. Table 3 indicates that the mean correlations of the scales of the CLEI ranged from 0.08 to 0.22, indicating that there is little overlap in what they measure.

Associations between the computer laboratory environment and student attitudes towards computers and their computing class were investigated by examining simple correlations between the scales of the CLEI and the ACCC. Table 4 depicts the results of this application. An examination of the simple correlation figures indicates that there were 17 significant relationships, out of 20 possible, between computer laboratory environment variables and student attitude variables.