Factors affecting teaching and learning of computer disciplines at. Rajamangala University of Technology

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1 December 2010, Volume 7, No.12 (Serial No.73) US-China Education Review, ISSN , USA Factors affecting teaching and learning of computer disciplines at Rajamangala University of Technology Rungaroon Sripan 1, Bandit Suksawat 2 (1. Graduate School, King Mongkut s University of Technology North Bangkok, Bangkok 10800, Thailand; 2. Department of Teacher Training in Mechanical Engineering, Faculty of Technical Education, King Mongkut s University of Technology North Bangkok, Bangkok 10800, Thailand) Abstract: This research aims to analyze and compare factors affecting teaching and learning of the computer disciplines at RMUT (Rajamanala University of Technology). A questionnaire was used as a research tool to survey perspectives of teachers and students. The numbers of sample were determined from Krejcie and Morgan table by using multistage sampling technique. The survey resulted from 92 teachers and 307 students were analyzed by descriptive statistical methods. The analysis of affecting factors of teaching and learning of the computer disciplines found that, the factors can be classified into 3 clusters, consisting of the factor of instructor, the factor of student and the factor of learning materials, with high correlation coefficient at Comparison of the 3 factors by using one-way ANOVA (analysis of variance) found that, opinions of teachers and students have at least one factor different. The factor of student and the factor of learning materials significantly differed at 0.05 level. All analyzed factors that affect to teaching and learning of the computer disciplines will be determined as an important weight for a concept selection in order to develop the teaching and learning system of the computer discipline at RMUT. Key words: factors affecting; teaching and learning; computer discipline 1. Introduction According to its development, IT (information technology) has been used in several fields especially in education. Recently, a number of departments in the ministry of education largely focus on the policy of lifelong autonomous learning by using IT as an educational tool. The Thai government together with international organizations also have initiated and supported several projects to accomplish this policy, whose the operation is starting from primary schools up through university (Intratat, 2007). RMUT (Rajamangala University of Technology), an institute of science and technology under the ministry of education, is willing to produce skillful and high performance workforces that are suitable for industrial sectors and other segments. RMUT also focus on developing the computer science disciplines for supporting several professions or enterprises. The teaching and learning do not only need the theory, but also require practices to achieve the objective of curriculum. According to Piyamongkonkul (2000), learners have to create and improve the contribution based on independent and logical thinking initiation and development of capability by oneself. Rungaroon Sripan, Ph.D. candidate, Graduate School, King Mongkut s University of Technology North Bangkok; research field: education technology. Bandit Suksawat, Ph.D., Department of Teacher Training in Mechanical Engineering, Faculty of Technical Education, King Mongkut s University of Technology North Bangkok; research field: engineering education. 33

2 This strategy aims to establish the learners knowledge and ability for working in the occupation. Since enhancement of the students practical knowledge and skills in computer discipline, the teaching and learning system is based on the important factors consisting of professional lecturer, appropriate facility, good education management and modernization. The suitable factors for curriculum operation at the university can conduct learners to be the professionals, meet the needs of the labor market and have basic knowledge and skills for further higher education (Pichetgid, 2000). Therefore, the study of general problems and confinement of teaching and learning in the computer disciplines will provide important information for improving the teaching and learning method or curriculum development at RMUT. Sripan and Suksawat (2009) investigated general problems of teaching and learning in the computer disciplines. The results showed that, most teachers did not evaluate students before teaching and learning in order to acknowledge the student backgrounds and classify the study groups. Furthermore, the teaching materials were unavailable for students learning. These problems significantly and directly impact on the development of learners knowledge and skill. However, general problems were only reported in the previous study. The specification in statistical analysis of factors affecting teaching and learning of the computer disciplines has not been reported yet. Therefore, this paper aims to analyze and compare the factors affecting teaching and learning of the computer disciplines at RMUT. 2. Literature review Factor of learning material has strongly positive effect on learning computer discipline. This factor is an interface tool between learning materials and teaching methods with learning object in order to create the knowledge of learning content to the learners (Leelitthum, 2000). Features of the learning materials should be properly aligned with the content and purpose of education. Therefore, the right form of education appropriates to their learning style and the environment (Vatawatanasak, 2005). Accordingly, Leelitthum (2000) found that teaching and learning materials are important factors in learning management, because people who are interested in learning can learn more in class and has more understanding with the materials. In addition, the teachers and the students also represent a significant impact on group learning of computer courses, because instructors have played a role in management of learning (Promchun, 2005). Instructors must understand the curriculums, learning objects, contexts and learning methods and have to focus on learning experience that is relevant (Thammetar, 1998). According to the concepts proposed by Pudenpar (2000), teachers should have good abilities to teach subjects and to manage a large number of students. Piyamongkonkul (2000) found that, the inappropriate number of students per group results in very uncomfortable classrooms and unpleasant teaching and learning environments. The group learning of computer courses should provide sufficient computers to all the students in each group, in order to facilitate learning and build knowledge and skills of computer usages. 3. Methodology The study of general problems of teaching and learning in the computer disciplines at RMUT was performed by using questionnaire as research tool to survey teachers and students perspectives. The numbers of sample were determined from Krejcie and Morgan table by using multistage sampling technique. The survey resulted form 92 34

3 teachers and 307 students were analyzed by descriptive statistical methods. The data of 5 factors, consisting of teacher, student, subject matter, learning activities and learning media, were used to analyze and compare in this paper. The details of 5 factors including 11 aspects are described as follows: (1) The factor of instructor consists of the NTTCD (number of teachers who teach the computer discipline) and the UIMCE (using of instruction media and computer equipment); (2) The factor of student consists of SLSC (students who lack skills in computer) and ENSG (excessive number of students per group); (3) The factor of subject matter consists of BSMT (balance between the subject matter when teaching) and items related to RBPTTP (relationship between the time of theory and the time of practice); (4) The factor of learning activities consists of CLATAS (consistent with the level of activity and teaching ability of students) and TPCS (time to practice on a computer student); (5) The factor of learning media includes QCES (quantity of computer equipment of students), the QIMNE (quantity of instruction media is not enough) and the UCTOS (use of computer time outside of students). The data analysis consists of the factor analysis method and ANOVA to compare the regression of the factor at 0.5 significant level. The factor analysis process was performed as follows: (1) It should compute the KMO (Kaiser-Myer-Olkin) measure of sampling adequacy, the rule of thumb is that the KMO value should be greater than 0.5 for a satisfactory factor analysis to proceed. And Bartlett s test of specificity to determine whether correlation exists between measurable variable. It should be noticed that, if Bartlett s test is not significant, this implies that correlation matrix is not significantly different from the identity matrix, hence the set of measurable variables are not correlated or each measurable variable is indeed a factor influencing response; (2) Factor extraction based on principle component analysis is computing the Eigen values of the correlation matrix. The magnitude of the Eigen values exceeding a certain pre-predetermined threshold will identify one significant factor. The rule of thumb is that, if the sum of the Eigen values are greater than or equal to 1.0, and each factor must be weighted from 0.50 above (Thompson, 2004); (3) It should compute the structure communality coefficient for each measurable variables, communality variable can measure the amount of variance; (4) It should varimax orthogonal factor rotation. 4. Results 4.1 Analysis results of the KMO measure of sampling adequacy and Bartlett s test of specificity The samples to be back up 399 people to test the KMO measure and Bartlett s test of specificity values have to verify for appropriateness factor analysis. The value of statistical test for specificity based on a chi-square transformation of the determinant of the correlation matrix was 0.852, and the associated significant level was 0, which are summarized into that the KMO value should be greater than 0.5 for a satisfactory factor analysis to proceed. And Bartlett s test of specificity to determine whether correlation exists between measurable variable is shown in Table 1. 35

4 Table 1 KMO and Bartlett s test KMO measure of sampling adequacy Bartlett s test of specificity Approx. chi-square df 55 Sig Analysis results of the factor extraction based on PCA (principle component analysis) PCA was the variables that have correlation with other variables to extract significant factor, and then describe the variability of most variables. The factor extraction based on the following basis factor is key factor that must be Eigen values greater than or equal to 1.0 and each factor must be weighted from 0.50 above. The results showed that, the principal component analysis yielded a 3-factor solution, representing % of the variance of the respondents scores on the 11 variable scale as shown in Table 2. Table 2 Total variance explained Aspects Initial Eigen values Rotation sums of squared loadings Total Variance (%) Cumulative (%) Total Variance (%) Cumulative (%) Analysis results of the component factor analysis The principal of component factor analysis was used to determine the average of variance in teaching and learning of computer disciplines. The dependent variable is teaching and learning of the computer disciplines. The independent variables consist of the factor of instructor, student and learning materials as mentioned above. The results of component factor analysis in confirmatory model of each variable are shown in Figure 1. The detail of 3 factors including 11 aspects is in follows: (1) The factor of instructor consists of QCES, the UCTOS, the QIMNE, NTTCD and the UIMCE; (2) The factor of student consists of SLSC and ENSG; (3) The factor of learning materials includes items related to RBPTTP, the BSMT, CLATAS and TPCS. The obtained factors were named on the basis of research carried out on the general problems of teaching and learning. The combination of items with loadings, which was greater than 0.505, was considered as a separated factors and defined as follows: (1) The factor of instructor with 5 aspects, loadings between to 0.810; (2) The factor of student with 2 aspects, loading between to 0.821; (3) The factor of the learning materials with 4 aspects, loading between to

5 0.875 RBPTTP BSMT Learning materials CLATAS TPCS QCES Teaching and learning of computer discipline Instructor UCTOS QIMNE NTTCD UIMCE SLSC Student ENSG Figure 1 Model of confirmatory factor analysis of factors affecting teaching and learning of the computer discipline at RMUT The factor loadings analysis results are shown in Figure 1. The results revealed that RBPTTP is the most important variable as in learning materials factor for the teaching and learning of the computer disciplines. Meanwhile, UIMCE is the less important variable as in the factor of instructor for the teaching and learning of the computer disciplines. 4.4 ANOVA for comparison of 3 factors The data of 3 factors consisting of instructor, student and learning materials from the model of confirmatory factor analysis were used to compare analysis of variance as shown in Table 3. Table 3 shows that opinions of teachers and students have at least one factor different. The factor of student and the factor of learning materials were significantly different at 0.05 level. The factor of instructor was not significantly different at 0.05 level. 37

6 Table 3 One-way ANOVA analysis for comparison of three factors Factor Sum of squares df Mean square F P Instructor Student Learning materials Notes: * p<0.05. Gender Error Total Gender * Error Total Gender * Error Total Conclusion Analysis of factors affecting teaching and learning of the computer disciplines at RMUT found that the factor of the learning materials, the factor of instructor and the factor of student are critical elements. All factors were to give students the skills to learn effectively. Therefore, the proposed research is the concept of teaching computer disciplines, it has been concluded that: (1) The teachers should be taught skills including the ability to transfer knowledge to students of insight, so the teachers should have a computer course in computer expertise for understanding of content, and the content of the course should always be improved to be modern; (2) The learning materials should be selected to fit to computer disciplines, so that the learning materials should be appropriate to the level of knowledge and experience of students, and the opportunity for students engaged in learning activities should also be advanced. References: Intratat, C. (2007, January-March). Investigation on advantages and disadvantages in using English CALL according to the opinions of Thai university students and lecturers. King Mongkut s University of Technology Thonburi Journal, Leelitthum, C. (2000). Creating the laboratory sheets of C language programming as required by higher vocational education certificate course in computer technology (electronics). (Master Thesis, King Mongkut s University of Technology Thonburi) (In Thai) Piyamongkonkul, S. (2000). Status, needs and problems of computer instruction for bachelor degree of business computer in private higher education institutions. (Master Thesis, King Mongkut s University of Technology Thonburi) (In Thai) Pichetgid, S. (2000, January-June). Problems of vocational development. Journal of Research and Development Center Vocational, Promchun, S. (2005). Didactic for technical course. Bangkok, Thailand: King Mongkut s University of Technology North Bangkok, (In Thai) Pudenpar, S. (2000). A study of status problems and needs of the computer teachers of Rajabhat Institute and in Rajamangala Institute of Technology. (Master Thesis, King Mongkut s University of Technology Thonburi) (In Thai) Sripan, R. & Suksawat, B. (2009, September 3-4). Survey of status and problems of computer track in Rajamangala University of Technology. The National of Ramkhamhaeng University Research Conference, Bangkok, Thailand, Thammetar, T. (1998). The concept of teaching and learning of computer. Bangkok, Thailand: Silpakorn University. (In Thai) Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, D.C.: American Psychological Association. Vatawatanasak, A. (2005). Learning materials. Retrieved May 26, 2009, from generality/ html. (Edited by Nicole and Sunny) 38

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