Factors Influencing Junior High School Teachers Computer-Based Instructional Practices Regarding Their Instructional Evolution Stages

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1 Hsu, Y.-S., Wu, H.-K., & Hwang, F.-K. (2007). Factors Influencing Junior High School Teachers Computer-Based Instructional Practices Regarding Their Instructional Evolution Stages. Educational Technology & Society, 10 (4), Factors Influencing Junior High School Teachers Computer-Based Instructional Practices Regarding Their Instructional Evolution Stages Ying-Shao Hsu Department of Earth Sciences & Science Education Center, National Taiwan Normal University, Taiwan // Hsin-Kai Wu Graduate Institute of Science Education, National Taiwan Normal University, Taiwan // Fu-Kwun Hwang Department of Physics, National Taiwan Normal University, Taiwan // ABSTRACT Sandholtz, Ringstaff, & Dwyer (1996) list five stages in the evolution of a teacher s capacity for computerbased instruction entry, adoption, adaptation, appropriation and invention which hereafter will be called the teacher s computer-based instructional evolution. In this study of approximately six hundred junior high school science and mathematics teachers in Taiwan who have integrated computing technology into their instruction, we correlated each teacher s stage of computer-based instructional evolution with factors, such as attitude toward computer-based instruction, belief in the effectiveness of such instruction, degree of technological practice in the classroom, the teacher s number of years of teaching experience (or seniority ), and the teacher s school s ability to acquire technical and personnel resources (i.e. computer support and maintenance resources). We found, among other things, that the stage of computer-based instructional evolution and teaching seniority, two largely independent factors, both had a significant impact on the technical and personnel resources available in their schools. Also, we learned that belief in the effectiveness of computer-based instruction is the single biggest predictor of a teacher s successful practice of it in the classroom. Future research therefore needs to focus on how we can shape teachers beliefs regarding computer-based learning in order to promote their instructional evolution. Keywords Technology adoption, Teachers beliefs, Educational technology, In-service teachers Introduction The rapid development of modern information and communication technologies has opened new possibilities for establishing and delivering distance learning. Given the popularity of the Internet, computer applications have recently become one of the most promising kinds of educational tool. Computers can now help educators in designing and promoting the teaching and learning process (Ministry of Education in Taiwan, 1999; Sinko & Lehtinen, 1999; Smeets, Mooij, Bamps, Bartolomé, Lowyck, Redmond, & Steffens, 1999). From studies (Angeli & Valanides, 2005; Hsu, Cheng, & Chiou, 2003), computers or/and Internet technology have positive impacts on students learning only when teachers know how to use computers or/and Internet technology to promote students knowledge construction and thinking. How can teachers use computers or/and Internet technology to promote students meaningful learning? Firstly, the teacher s role should no longer be that of a traditional lecturer; rather, the teacher must now be a coach or co-learner (Beaudion, 1990; Brophy & Good, 1986). Secondly, activities in the classroom should become learnercentered and flexible in order to help students organize information and undergo self-initiated, exploratory learning processes (McKenzie, Mims, Davidson, Hurt & Clay, 1996; Winn, 1993). With computers and Internet technology, a teacher can utilize online teaching resources to arrange flexible learning activities; these can assist students in analyzing and organizing large amounts of information. Thirdly, the teacher s attitude toward computers will be important to the way computer-based technology is used in instruction (Beaudion,1990; Ercan & Ozdemir, 2006; Gardner,Discena & Dukes, 1993). Lloyd and Gressard (1984) have pointed out that a teacher s positive feelings about computers will also help to generate or reinforce such feelings in the students. Comber et al. (1997) found that younger teachers might have more experience in computer use and thus a more positive attitude toward computers (Jennings & Onwuegbuzie, 2001). Braak (2001) noted that personal acceptance of technological innovation would ISSN (online) and (print). International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at 118

2 influence attitudes toward computers, and furthermore that computer experience tends to directly affect attitudes toward computers (Kay, 1989; Gardner, Discena & Dukes, 1993; Woodrow, 1994; Yildirim, 2000). The teachers will need to adapt to the impact of computing technology while integrating it into their classrooms. During this adaptation process, a teacher may need to change his educational beliefs, values, and rationale in order to properly integrate computing and Internet technology into daily teaching. A researcher who wants to understand a teacher s adaptation process should investigate the changes in the teacher s teaching strategies, the design of his/her learning activities, and students assessments (Beaudoin, 1990; Brophy & Good, 1986; Mc Kenzie, Mims, Davidson, Hurt & Clay, 1996; Thatch & Murphy, 1995; Verduin & Clark, 1991). Therefore, integrating technology into instruction not only requires dealing with hardware and software issues but also dealing with complex issues like human cognition, policy and values (Sandholtz, Ringstaff, & Dwyer, 1996). For instances, teachers knowledge (OTA, 1995; Pelgrum, 2001), educational rationales (Czerniak & Lumpe, 1996; Niederhauser & Stoddart, 2001; Ruthven Hennessy & Brindley, 2004) and instructional strategies (Becker, 2000a, 2000b; Ravitz, Becker & Brindley, 2000) about integrating computers into instruction affect students meaningful learning with computers. Educational policy, curriculum standards, school culture and peer supports influence teachers intention to use computers in classrooms (Chiero, 1997; Mooij & Smeets, 2001; Rogers, 2000; Russell, Bebell, O Dwyer & O Connor, 2003; Ruthven et al., 2004; Windschitl & Sahl, 2002; Teo & Wei, 2001; Zhao & Frank, 2003). Above all, the key to successful computer-based instruction, especially with teachers who are new to it, is to find the methods which can help teachers face and adjust their beliefs, attitude, and instructional strategies regarding computer-based instruction. From the above studies, a comprehensive list of factors influencing computer-based instruction has been addressed but there is a systematical investigation necessary to reveal the interactions among factors and the possibility of incorporating computers into a broader educational reform context. This study takes structural factors such as teachers instructional evolution and teaching seniority into consideration and explores how they interact with teachers beliefs regarding, attitude toward and practices of computer-based teaching. We conducted a national survey in Taiwan and collected a pool of six hundred science and mathematics teachers who had integrated computing technology into their instruction in junior high schools. The following questions guided this study: (1) What are the numbers of teachers in different stages of instructional evolution as defined by Sandholtz, Ringstaff and Dwyer (1996)? (2) How do teachers instructional evolution and teaching seniority interact with their beliefs regarding, attitudes toward and practices of computer-based instruction? (3) What regression models can be proposed to examine the relationships among teachers beliefs, attitudes, practices, and resources when using computing technology in the classroom? Research Methods This research project employed a survey method to investigate teachers beliefs regarding, attitudes toward and practices of computing technology in the classroom. Sample selection The population under investigation included all science and mathematics teachers at junior high schools in Taiwan. A stratified cluster random technique was employed to select the sample according to school size (large: more than 37 classes, middle: classes, small: less than 15 classes) and school region (schools in the northern, middle, southern, and eastern parts of Taiwan). Among 892 junior high schools in Taiwan, approximately 11% (99 schools) were selected. Of 2019 questionnaires mailed out, 1002 replies from 82 schools were received (an 89.1% school response rate and 49.6% teacher response rate). After ignoring the questionnaires which were not filled in completely, the valid sample size was determined to be 940. Of the valid sample, 613 respondents who reported that they had used computing technology in their teaching were finally examined. Instrumentation We developed the questionnaire to collect information on teachers use of computing technology in their classrooms. The questionnaire was divided into three sections: (1) demographic background, (2) current stage of instructional 119

3 evolution with computing technology, and (3) perceptions and practices of computer-based instruction. Demographic background included information about age, gender, teaching seniority, school size and school region. Items in the latter two sections were rated on a 5-point Likert-type scale from 1 (strongly disagree) to 5 (strongly agree). From data in the second section, the respondents were classified into five evolutionary stages (entry, adoption, adaptation, appropriation and invention) to indicate teachers level of computer use, according to the definitions of Sandholtz, Ringstaff, & Dwyer (1996): (1) entry stage: teachers spend a lot of time in installing software and managing hardware and students spend most of time in learning computer skills instead of subject contents; (2) adoption stage: teachers utilize software (i.e. word processors, excel etc.) to assist their traditional teaching; (3) adaptation stage: teacher apply various software for instructional purposes and integrate technology successfully in classrooms; (4) appropriation stage: teachers develop multiple teaching strategies to promote students cognitive ability, share computer-based teaching experience with other teachers, and feel confident in integrating technology into teaching; (5) invention stage: teachers lead students to use software as a learning tool, develop innovative teaching strategies and assessments with computers, and affirm the value of computer-based instruction. Table 1 shows teachers instructional evolution with computers in terms of five categories: classroom management, software use, teaching strategies, learning efficiency, and confidence and beliefs. There is an item for each of the five evolutionary stages plotted against the five categories: thus 25 items in all. The respondents needed to pick one description for each category to represent their current level of experience with computing technologies. The round average of the values (from 1: entry to 5: invention) in these five categories represents the current stage of the teacher s computer-based instructional evolution. Table 1. Characteristics of the Stages of Instructional Evolution (Sandholtz, Ringstaff, & Dwyer, 1996) Contractors Stages Classroom management Software use Teaching strategies Learning efficiency Confidence and beliefs Reacting to problems Entry Adoption Adaptation Appropriation Invention Dealing with problems in software installations and management No change Students spend time in learning computer skills No faith in computerbased instruction; having doubts most of the time Anticipating and developing strategies for solving problems Learning software Designing activities to teach students computer skills Promoting learning motivation but not improving conceptual understanding (sometimes having a negative impact on students grades) Attempting to use technology in classrooms Utilizing the technological advantage in managing the classroom Using software for instructional purposes Integrating technology to improve students knowledge comprehension Reducing students learning load and no significant improvement in conceptual understanding Often integrating technology successfully in classrooms Intertwining instruction approaches and management strategies Integrating software in learning processes and enhancing students mutual support in software use Using multiple methods to promote students cognitive ability Cultivating cognitive ability Having confidence in integrating technology into teaching; sharing experiences with other teachers Leading students to use software as a learning tool Developing strategies for innovative teaching such as project-based learning, modeling etc. Promoting problemsolving ability Affirming the value of computer-based instruction 120

4 In the third section, factor analytical techniques were used to determine the underlying structure of teachers responses to items. Principal axis factor analysis with varimax rotations was employed for the factor analysis. The results for both the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (0.85) and the Bartlett Test of Sphericity (χ2 =6803.8, N = 613, p < ) were significant, indicating that factor analysis was suitable for this sample. By using Cattell s screen test and examining the factor loadings of the items, we removed 13 items from the questionnaire: five factors emerged. According to the pattern of correlation between and among items and factors, we assigned a descriptive name to each of the factors. The factors arrived at were: (1) belief in the use of computing technology in classrooms (e.g., I believe that technology-based instruction can improve learning achievement ); (2) high degree of interactive use of technology (e.g., I have had students learn collaboratively through the Internet ); (3) technical and personnel resources available in a given teacher s school (e.g., In my school, there are enough technicians to maintain computers ); (4) low degree of interactive use of technology (e.g., I have used computers to play videos in classrooms ); and (5) attitude toward technology-based instruction (e.g., Learning software will not make me nervous and uncomfortable ). The validity values of the factors (eigen values) were 4.82, 2.13, 1.73, 1.24, and The factor loadings of the 24 items ranged from 0.53 to 0.70, and 58% of the variations were included within the five factors (see details in Table 2). As shown in Table 2, the composite reliability coefficients were ranged from 0.69 to 0.89 and the overall instrument reliability reached Therefore, the reliability of the instrument was established. Data Analysis We used frequency analysis to show the distribution of teachers in different stages of instructional evolution and MANOVA techniques to examine correlations and interactions between two factors (teaching seniority and the stage of computer-based instructional evolution) on five measures (beliefs, high-level interactive practices, technical and personnel resources, low-level interactive practices, and attitudes). The stepwise method of multiple linear regression was applied to indicate the relationships between and among these variables. The calculations to determine the coefficients of regression models and MANOVA were performed through the application of the SPSS 12.0 package. No. Table 2. Questionnaire Items and Factor Loading Average Factor Items and Factors variance Loading extracted Composite reliability Subscale score Belief e19 I think technology is helpful for my teaching e26 I believe that technology-based instruction can improve learning achievement e9 I think technology-based instruction is one of the future trends in education e27 I believe that technology-based instruction can make my teaching more lively and energetic e21 I should create different teaching strategies for technologybased instruction e25 I believe that technology-based teaching can increase students motivation e20 Using technology can help me share my teaching experiences with others e10 I am willing to follow school policy on implementing technology-based instruction e22 I should develop different assessment strategies for technologybased instruction e1 I believe that conventional teaching methods are more efficient than technology-based instruction High-interaction practices (behaviors) e35 I have designed activities that allow students to learn through the Internet e36 I have had students learn collaboratively through the Internet e37 I have used the Internet to support individual learning e33 I have used computers and the Internet to collect and grade students assignments. Mean S.D

5 Resources e17 In my school, there are enough technicians to maintain computers e16 In my school, administrators can provide hardware and software for supporting technology-based instruction e13 In my school, teachers often discuss computer-related topics and exchange ideas about computer hardware and software e14 In my school, teachers often surf teaching websites Low-interaction practices (behaviors) e32 I have used educational software to promote learning e30 I have used computers to play videos in classrooms e34 I have used computer applications to create pictures, videos and animations and used them in classrooms e31 I have used document management software, such as Word and Powerpoint, to display my syllabus and my lectures Attitude toward computer technology e3 I will not feel anxious when I take any computer-related courses e2 Learning software will not make me feel nervous and uncomfortable e5 Currently, information on the Internet is useful to my teaching Instrument reliability: 0.87 Results and Discussion The results are presented in three sections. The first section shows the descriptive statistics associated with two factors (stage of computer-based instructional evolution and teaching seniority) and five dependent measures (beliefs, high-interaction practices, low-interaction practices, resources and attitudes). In the second section, results of MANOVA are presented and interactive effects between/among the stage of instructional evolution and teaching seniority in relation to the five dependent measures are shown. The third section outlines the relationships among beliefs, high- and low-interaction practices, resources and attitudes. Descriptive analyses According to the participants responses in the second section of the questionnaire (current stage of instructional evolution with computing technology), one-third of the teachers (about 37%) were in the third or adaptation stage; the teachers in that stage can use appropriate software and information technology to improve their teaching and students learning in science and mathematics. Roughly one-fifth of the teachers were in either the entry stage (15%), adoption stage (23%) or appropriation stage (18%). Very few teachers (about 7%) were in the final invention stage; the teachers in this stage need to be able to use computer-based instruction in a creative way, guiding students to use software as a learning tool in their knowledge construction, modeling, and communication. After surveying junior high school science and mathematics teachers who had integrated computing technology into their instruction in Taiwan, most of them were in adaptation and appropriation stages; that meant they usually applied computer software for instructional purposes, developed multiple teaching strategies to promote students cognitive ability, and felt confident in integrating technology into teaching. Teachers degree of seniority could indicate the degree of their teaching experiences, pedagogical content knowledge (PCK), subject knowledge, and computer skills. Teachers with a seniority of less than 10 years tended to have more computer skills because they more likely were able to take computer-based instruction courses in their teachers training programs. In contrast, the teachers who had already taught for more that 11 years tended to lack training in computer skills and to feel anxious about computers, while also having a higher degree of PCK due to their greater teaching experience. As Figure 1 shows, most teachers with low seniority are in the third or adaptation stage: 43% of the teachers who have taught for less than 5 years are in this stage. This means that most of them often integrate technology into their instruction but cannot use multiple methods to promote students conceptual understanding and cognitive ability because they lack the teaching experience, even though their computer skills are probably excellent. About 76% of this low-seniority group are in the first three stages (entry, adoption, and adaptation). On the other 122

6 hand, more teachers whose seniority ranged between 16 and 20 years were in the fourth or appropriation stage (25%). This means teachers with more teaching experience and greater PCK can use multiple methods to integrate technology into their classroom teaching; however, with greater seniority the percentage of teachers in the entry and adoption stages increased as well, suggesting again that younger teachers greater computer skills and more positive attitudes toward computers do also affect computer-based instructional evolution. Figure 1. Frequencies of teachers seniorities at each evolutionary stage Effects of instructional evolution and teaching seniority Table 2 outlines the mean scale scores and standard deviations for different -stage and different-seniority teachers beliefs, high-interaction practices, low-interaction practices, resources and attitudes. Compared with the teachers in the entry stage, the teachers in the appropriation and invention stage tended to have higher mean scores on all of dependent measures. It is not surprising that the teachers in higher stages of instructional evolution seemed to hold more positive belief and attitude toward technology and to have more resources to support technology-based instruction. In order to examine the effects of computer-based instructional evolution and teaching seniority on beliefs, high-interaction practices, low-interaction practices, resources and attitudes, a 5 (stages) 5 (levels of teaching experience or seniority) MANOVA was employed. As Table 4 shows, there were significant differences regarding teachers beliefs in, attitudes toward, and practices of computer-based instruction. For instance, teachers in the adaptation and appropriation stages tended to have more positive beliefs about and attitudes toward computer-based instruction than those just in the entry stage; teachers in the entry stage also tended to perform low-interaction computer-based activities than those in the appropriation and invention stages; the latter two groups were still behind those in the invention stage as regards the degree of interactivity of their computer-based learning practices. The results imply few things: (1) teachers in the later stages (adaptation, appreciation, and invention) of computer-based instructional evolution held more positive beliefs and attitudes, and practiced more computer-based instructions in classroom; (2) teachers with positive beliefs and attitudes possibly moved to the later stages of computer-based instructional evolution and intended to practice computer-based instructions; (3) the successful teaching experiences in computer-based instructional evolution could promote teachers positive beliefs and attitudes, and encourage them to practiced more computer-based instructions in classroom. Therefore, teachers with different computer-based instructional evolution could be due to their different beliefs and attitudes; also, experiences of computer-based teaching practices could feedback to teachers beliefs, attitudes, and computer-based instructional evolution. As we can see in Table 4, teachers stage of instructional evolution and degree of teaching seniority had a significant impact on the amount of technical and personnel resources available at their schools. Since the interaction between 123

7 teachers instructional evolution and seniority had reached a significant level, an ANOVA simple-effect analysis was conducted (see Table 5 and Figure 2). The results showed that teachers in the entry stage whose seniority was 6-10 years reported that they had less technical and personnel resources available in their school than entry stage teachers whose seniority who were more than 21 years; these same teachers with 6-10 years of seniority in the entry stage also reported, very predictably, that they had less technical and personnel resources available in school than teachers with 6-10 years of seniority in the appropriation and invention stages. This means that teachers who hold computer-instruction skills and pedagogies could move to the later stage of computer-based instructional evolution if there are technical and personnel resources available in school. Table 3. Stage & Seniority vs. Beliefs, Practice, Resources, and Attitude Condition Beliefs Resources High practice Low practice Attitudes Mean SD Mean SD Mean SD Mean SD Mean SD < 5 years Entry Adoption Adaptation Appropriation Invention years Entry Adoption Adaptation Appropriation Invention years Entry Adoption Adaptation Appropriation Invention years Entry Adoption Adaptation Appropriation Invention > 21 years Entry Adoption Adaptation Appropriation Invention Total < 5 years years years years > 21 years Total Entry Adoption Adaptation Appropriation Invention

8 Table 4. Summary of MANOVA Results Condition Beliefs Resources High practice Low practice Attitude F E.S. F E.S. F E.S. F E.S. F E.S. Main effect Stages *** ** *** *** 0.20 Post Hoc (1<<3, 4, 5; 2<<4) (1<<4,5; 2,3<<5,) (1<<3,4;2<<3,4) (1<<3,4; 2<<4) Teaching seniority * *** (a>>c, d, e, b>>c, e) Post Hoc (b>>d) Interaction Stages * Teaching seniority * * p <.05, ** p <.01, *** p <.001; Stage: 1-entry, 2-adoption, 3-adaptation, 4- appropriation, 5- invention Teaching seniority: a-< 5 years, b-6~10 years, c-11~15 years, d-16~20 years, e-> 21 years Table 5. Simple-Effect ANOVA Summary for Resources vs. Stage & Seniority Source SS df MS F Post Hoc Stages Entry * 6-10 years << more than 21 years Adoption Adaptation Appropriation Invention Teaching seniority < 5 years years *** Entry << Appropriation, Invention years years > 21 years Figure 2. Profile plot of school resources vs. stage & seniority 125

9 Regression models indicating relationships The questions in the third section of the questionnaire were grouped by dependent variable (high- and lowinteraction practices) and independent variable (beliefs, attitudes, and resources). The means of the responses were then used to represent each variable, since this is one of the most-used parameters to represent a group of values (Moore & Benbasat, 1991; Holcombe, 2000). After calculating these means, an analysis of the correlation between variables was done. As we can see in Table 6, there were significant correlations among teachers beliefs, attitude toward computers, available resources, and teaching practices (including high- and low- interaction practices). Beliefs show the greatest correlation with the dependent variables (high- and low-interaction practices); therefore beliefs will have a greater impact in the regression models. Table 6. Correlation Matrix for the Five Teacher Factors (N = 613) Pearson Correlation Beliefs H-Practice L- Practice Attitude Resources Beliefs 1 H-Practice 0.31** 1 L- Practice 0.48** 0.44** 1 Attitude 0.33** 0.09* 0.32** 1 Resources 0.27** 0.21** 0.16** **Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed). As we can see in Table 7, these coefficients of the multiple linear regressions were grouped into two categories: (1) the dependent variable is high-interaction practices; (2) the dependent variable is low-interaction practices. In each category six regression models, including one group with all the subjects and five groups with the subjects distributed into five stages, were calculated in order to compare the different contributions of variables (beliefs, attitudes toward computers, and available resources). Teachers beliefs and available resources turned out to be the major predictors of their high-interaction practices. Besides, all of the t values reached a significant level (0.05), which means that independent variables (beliefs and available resources) contributed significantly to the prediction of the dependent variable (high-interaction practices). The same procedure was used to analyze the subjects in the five stages of computer-based instructional evolution. As we can see in Table 7, beliefs are the greatest predictor of high-interaction practices except at the entry stage; here teachers beliefs and available resources are the best predictors of this variable. By contrast, beliefs and attitudes contribute significantly to the prediction of low-interaction practices. As we can see, beliefs are the greatest predictor for these practices except at the adaptation stage. In this stage, teachers beliefs and attitudes best predict that teachers will engage in low-interaction activities when they integrate computerbased technology into their classrooms. In conclusion, teachers beliefs are the most reliable predictors of their computer-based instructional practices. Similar findings can be found in many studies (Czerniak & Lumpe, 1996; Niederhauser & Stoddart, 2001; Ruthven, et al., 2004; Sandholtz, Ringstaff & Dwyer, 1996 ). Total (N=613) Entry (N=89) Table 7. Summary of Regression Models High-Interaction Practice Variables Beta S.E. t (p) R Constant (.016) Beliefs (.000).34 Resources (.001) Constant (.061) Resources (.015).44 Beliefs (.031) Constant (.001) Beliefs (.043).17 Constant (.001) Beliefs (.001).23 Constant (.615) Beliefs (.000).41 Adoption (N=141) Adaptation (N=229) Appropriation (N=111) Invention Constant (.850)

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