Aptitude-treatment interactions in preservice teachers' behavior change during computer-simulated teaching

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(1)Aptitude-treatment interactions in preservice teachers’ behavior change during computer-simulated teaching Yu-Chu Yeh. Institute of Teacher Education, Center for Creativity and Innovation Studies, National Chengchi University, 64, Chih-nan Road, Sec. 2, Wenshan, Taipei 116, Taiwan. Received 12 January 2004; accepted 10 March 2005. Abstract Adapting training methods to specific teacher traits to best facilitate the training effects for preservice teachers is an important, yet neglected, topic in aptitude-treatment interaction research. This study investigated interactions between four personal traits (CT-dispositions, thinking styles, CT-skills, and intrapersonal intelligence) and two designed treatments on preservice teachers’ behavior change during a computer-simulated teaching experience. One hundred and seventy-eight preservice teachers participated in this study. The CS-TGCTS simulation program was employed to measure the preservice teachers’ actual use of effective teacher behaviors, as well as the four targeted personal traits which were measured by three Likert-scale inventories and one multiple-choice test. The results suggest that preservice teachers with high levels of CT-dispositions, CT-skills, and intrapersonal intelligence – as well as those with judicial or legislative thinking styles – are mindful, analytical, and reflective in their teaching practices and therefore more likely to continually improve their teaching skills.. Keywords: Architectures for educational technology system; Improving classroom teaching; Interactive learning environments; Simulations; Teaching/learning strategies..

(2) 496. Y.-C. Yeh / Computers & Education 48 (2007) 495–507. 1. Introduction When incorporating computers into teacher training, teacher educators must consider teachers’ personal traits, because aptitude-treatment interactions (ATIs) inXuence the course and outcomes of training. The evaluation of ATIs must go beyond merely assessing the interactions among individuals and situational variables; ATIs oVer a framework for new theories of aptitude interpreted as personal readiness to proWt from particular treatment situations (Snow, 1991). Consequently, teachers with certain traits beneWt more from a speciWc teacher-training method than do those who lack those traits. Because many researchers have suggested that computer simulation is an eVective tool for teacher training (Charischak, 2000; Haneghan & StoZett, 1995; Kenny, Covert, Schilz, Vignola, & Andrews, 1995; Yeh, 2004) and because critical thinking has been regarded as a new teaching standard (Birman, Desimone, Porter, & Garet, 2000; Darling-Hammond, 1999), a computer simulation for critical-thinking instruction was employed in this study to investigate the ATI eVects on preservice teachers’ behavior change. BrieXy speaking, this study attempted to understand whether certain preservice teachers’ personal traits interact with speciWc treatments in a simulation program to inXuence the improvement of eVective teacher behaviors in critical-thinking instruction. In this study, four personal traits were of particular interest: critical-thinking dispositions (CT-dispositions), thinking styles, critical-thinking skills (CT-skills), and intrapersonal intelligence.. 2. Personal traits and changes in teacher behaviors 2.1. EVective teacher behaviors and mechanisms for their improvement Teachers’ behavior change in critical-thinking instruction was the dependent variable in this study. Drawing upon previous research Wndings (e.g., Facione, Sanchez, Facione, & Gainen, 1995; Garcia & Pintrich, 1992; Halpern, 1998; Haneghan & StoZett, 1995; Harris & Eleser, 1997; Kluger & DeNisi, 1996; Larson, 2000; McBride & Knight, 1993; Michelli, Pines, & Oxman-Michelli, 1990; Udall & Daniels, 1991), I deWned the variable in terms of 12 eVective teacher behaviors in CT instruction that fall within three categories of intent. The Wrst category involves increasing students’ prior knowledge. Related teacher behaviors include (a) providing students with advance organizers and (b) providing students with review sessions. The second category focuses on enhancing students’ critical-thinking dispositions. Related behaviors include (c) keeping students focused on tasks or discussions, (d) giving ample time for thinking, (e) allowing a variety of student answers, (f) giving cues when students cannot answer correctly, (g) giving positive feedback, and (h) monitoring the students’ learning process. The Wnal category is upgrading students’ critical-thinking skills, and related behaviors are (i) asking higher-order questions, (j) asking extended questions, (k) requesting explanations for answers, and (l) encouraging cooperative learning and conducting discussions. These 12 teacher behaviors were measured in the simulation program employed in this study. Previous studies have suggested that self-awareness and mindfulness contribute to nurturing reXective practice (Collier, 1999; Tillema, 2000; Titone, Sherman, & Palmer, 1998), and they bring.

(3) Y.-C. Yeh / Computers & Education 48 (2007) 495–507. 497. about remarkable improvements in teacher behaviors (Yeh, 2004). ReXective teaching refers to instructors’ purposeful and systematic inquiry into their own personal theories of teaching and learning, as well as into the practices dictated by those theories (Abell, Bryan, & Anderson, 1998). Such reXection maximizes a teacher’s creative ability to improve his or her teaching practices (Collier, 1999; Rodriguez & Sjostrom, 1998; Titone et al., 1998). Accordingly, mindful learning, selfawareness, and reXective teaching are crucial mechanisms that lead to the improvement of teacher behaviors. Four personal traits (two personality characteristics and two abilities) that may interact with these mechanisms and result in behavior change are discussed in the following sections. 2.2. Teacher personalities and behavior change This study investigated, in part, teachers’ CT-dispositions and thinking styles. CT-dispositions refer to attitudes, commitments, and tendencies for thinking critically (Norris & Ennis, 1989). They involve such qualities as open-mindedness, intellectual curiosity, reXective thinking, and being analytical and systematic in problem solving (Bailin, Case, Coombs, & Daniels, 1999; Facione et al., 1995; Halpern, 1997; McBride & Knight, 1993; Paul & Elder, 2001). More speciWcally, CT-dispositions include (a) a willingness to engage in, and persist at, a complex task, (b) habitual use of plans and the suppression of impulsive activity, (c) Xexibility or open-mindedness, (d) a willingness to abandon nonproductive strategies in an attempt to self-correct, and (e) an awareness of the social realities that need to be overcome so that thoughts can become actions (Halpern, 1998). Yeh (1997) found that CT-dispositions are related to mindful learning and self-awareness; they therefore contribute to preservice teachers’ professional growth. Moreover, research Wndings (Facione et al., 1995) have suggested that a person’s dispositions toward critical thinking are signiWcantly related to his or her ego-resiliency, which refers to a person’s ability to change his or her model of perceptual and behavioral functioning in order to adapt to situational constraints. Accordingly, teachers with strong CT-dispositions are aware of their behaviors, open-minded and mindful in learning, and reXective and self-regulated in teaching; thus, they are able to improve their skills during a training session. Thinking style relates to one’s mental self-government; and individuals prefer to use their intellectual abilities for certain functions, which gives them a characteristic thinking style (O’Hara & Sternberg, 2000–2001). More speciWcally, thinking style is the preferred way of expressing or using one’s intellectual abilities; it is related to how a person chooses to exploit knowledge and decide how to use it in day-to-day interactions with the environment (Sternberg, 1994, 1997; Zhang & Sternberg, 2000). Sternberg (1988) proposed 13 thinking styles grouped together within Wve aspects: functions, forms, levels, domains, and learnings (Cano-Garcia & Hughes, 2000; Sternberg, 1988, 1997). This study focused only on functions. Three functions are legislative, executive, and judicial styles (Sternberg, 1997). People with a legislative style prefer to do things their own way and to build their own structures when deciding how to approach a situation or a problem; they prefer creative and constructive planning-based activities. Individuals with a judicial style like to evaluate rules and procedures and analyze and evaluate existing rules; they prefer activities that exercise the judicial functions. An executive style is seen in those who prefer pre-structured tasks; they like activities that are already deWned for them (Sternberg, 1997; Zhang & Sternberg, 2000). Several researchers (Chang, 1998; Sternberg, 1997; Zhang, 2001) have suggested that thinking styles relate to teaching eYcacy, teaching approaches, and teaching behaviors. For example,.

(4) 498. Y.-C. Yeh / Computers & Education 48 (2007) 495–507. Zhang (2001) found that teachers with judicial or legislative thinking styles tend to employ a student-focused approach or a conceptual-change approach. Such teaching approaches emphasize students’ intellectual autonomy and the chance to make decisions. Furthermore, preservice teachers with judicial thinking styles tended to be most analytical in their teaching behavior; those with executive thinking styles were the least analytical (Sternberg, 1997). Constructivist teaching approaches and the characteristics of judicial and legislative thinking styles align with the positive teacher behaviors of critical-thinking instruction; they are required if teachers are to be self-reXective about their behaviors. 2.3. Teacher abilities and behavior change Two types of teacher abilities were studied here: CT-skills and intrapersonal intelligence. Critical thinking is a purposeful, goal-directed, and self-regulatory thinking process; it requires a set of dispositions as well as skills (Norris & Ennis, 1989). CT-skills are diVerent from CT-dispositions in that the former concern “cognitive abilities”, while the latter involve “aVective attitudes”. CTskills involve both cognitive and metacognitive skills such as analysis, interpretation, inference, evaluation, induction, deduction, judgment of credibility, identiWcation of assumption, self-monitoring, self-regulation, and self-evaluation (Browne & Meuti, 1999; Gadzella & Masten, 1998; Halpern, 1998; Hittner, 1999; Lawson, 1999; McCarthy-Tucker, 2000; Paul & Elder, 2001). Norris and Ennis (1989) indicated that a good critical thinker must possess skills for establishing comprehensive support, obtaining clariWcation, drawing inferences, and applying strategies. Beyond pointing out a set of skills, Halpern (1998) proposed a taxonomy for CT-skills: (a) verbal reasoning, (b) argument analysis, (c) hypothesis testing, (d) using likelihood and uncertainty, and (e) decision making and problem solving. CT-skills are a form of rational thinking, and research Wndings have suggested that rational information processing is positively related to adaptation (Epstein, Pacini, & Heier, 1996). When people think critically, they are not only evaluating their thinking process; they are also providing useful and accurate feedback that serves to improve their thinking process (Halpern, 1996, 1998). Consequently, teachers with good CT-skills are likely to be analytical and reXective about their teaching and to make good use of feedback given during the training process. As a result, they can adapt their teacher behaviors. Intrapersonal intelligence has been broadly deWned as the capacity to self-reXect: to be aware of the strengths and weaknesses, feelings, and thought processes that constitute one’s knowledge of self (Furnham, Tang, Lester, O’Connor, & Montogomery, 2002; Gardner, 1999; Shepard, Fasko, & Osborne, 1999). Moreover, intrapersonal intelligence is closely related to goal setting, thinking skills, emotional expression, and self-directed learning (Campbell, Campbell, & Dickinson, 1999); it is associated with one’s ability to identify problems and to undertake deep introspection (Armstrong, 2000; Harman & Rheingold, 1984). Intrapersonal intelligence also features in problem-solving endeavors with signiWcance for the individual (Gardner, 1993, 1999). It is related to strategic processing of executive control: the ability to reXect upon and regulate one’s thoughts and behaviors (Campbell et al., 1999; Gardner, 1993; Shepard et al., 1999). In addition, it encompasses a form of self-awareness that goes beyond the strict demands of selecting strategies and evaluating outcomes in the problem-solving process (Shepard et al., 1999). Mitina and Kuz’menkova (1999) found that teachers with a high level of.

(5) Y.-C. Yeh / Computers & Education 48 (2007) 495–507. 499. professional self-awareness can move outside the boundaries of everyday and routine pedagogical practice. Such strategic processing abilities and self-awareness are clearly essential to learning and professional development, especially for the improvement of teaching practices. 2.4. Hypothesis The following hypothesis was proposed in the present study: preservice teachers’ personal traits interact with the designed treatments in the CS-TGCTS simulation program in ways that aVect the outcome as deWned in terms eVective teacher behaviors. The designed treatments emphasized being mindful in learning, self-aware in teacher behaviors, and reXective in teaching practices. These emphases were the key mechanisms for the attribute-treatment interactions in the CSTGCTS. SpeciWcally, preservice teachers with high levels of CT-dispositions, CT-skills, and intrapersonal intelligence were expected to improve their teacher behaviors signiWcantly as a result of the CS-TGCTS simulation program, while their counterparts with low levels of those personal traits were expected to beneWt less. In addition, those with judicial or legislative thinking styles were expected to improve their teacher behaviors after the computer-simulation training, whereas those with executive thinking styles would improve less or not at all.. 3. Methods 3.1. Participants The participants were 51 male (28.7%) and 127 female (71.3%) preservice teachers enrolled in a two-year teacher education program at the National Sun Yat-sen University, Taiwan. They were preparing to be qualiWed as secondary-school teachers. With a mean age of 23.90 years (SD D 3.67), the largest group was that between 21 and 25 years old (82.6%), and the mode was 23 years. 3.2. Instruments The participants’ interactive teaching experience in this study was accomplished via Computer Simulation for Teaching General Critical-Thinking Skills, CS-TGCTS (Yeh, 2004). The CSTGCTS comprised two integrated serial simulations, each simulation taking about 2 h to complete. Twelve teacher behaviors were measured in the CS-TGCTS simulation. The measured scores indicated the percentages of a teacher’s actual usage of teacher behaviors pertaining to improving students’ prior knowledge, CT-dispositions, and CT-skills. The CS-TGCTS simulation also provided records for measurements of the participants’ thinking styles, CT-dispositions, intrapersonal intelligence, and CT-skills. The employed instruments for these teacher traits were the Inventory of Critical Thinking Dispositions (ICTD), the Inventory of Thinking Styles (ITS), the Questionnaire of Intrapersonal Intelligence (QII), and the Critical Thinking Test, Level II (CTT-II). The Wrst three inventories were Likert-type scales; the fourth was a multiple-choice test. With a total of 20 items, the ICTD comprised four factors: systematicity and analyticity (nine items), open-mindedness (four items), intellectual curiosity (three items), and reXective thinking.

(6) 500. Y.-C. Yeh / Computers & Education 48 (2007) 495–507. (four items). ICTD was a 6-point Likert scale anchored from “never” to “always”. The Cronbach’s coeYcients for all items and the four factors were .88, .83, .58, .70, and .63, respectively. The correlations among the four factors were rs(98) D .31–.60 (ps < .01) (Yeh, 1999). The QII, a 6-point Likert scale anchored from “totally disagree” to “totally agree”, was adapted from Armstrong’s checklist of intrapersonal intelligence. The QII contained only one factor and its Cronbach’s coeYcient was .80 (seven items) (Chu, 2001). The ITS, a 5-point Likert scale anchored from “never” to “always”, was adapted from the Sternberg-Wagner Thinking Styles Inventory (Li, 1999). With a total of 15 items, the three thinking styles included in this inventory were judicial, legislative, and executive style. The Cronbach’s coeYcients for all items and for the three styles were .80, .69, .62, and .61, respectively. The CTT-II, which comprised 25 multiple-choice items, was divided evenly into Wve subtests: assumption identiWcation, induction, deduction, explanation, and argument evaluation (Yeh, 2001). Each item contained one statement and four multiple-choice answers. The time limit was 20 min, which was automatically timed in the simulation. The mean discriminate index of CTT-II was .53; its mean diYculty index was .58. The correlations between the subtest scores and the total score were rs(185) D .59–.69, ps < .01. 3.3. Procedures and instructional design All participants enrolled in Educational Psychology took the CS-TGCTS simulation in a computer laboratory as part of their class requirements. After receiving a brief introduction to the simulation and a 10-min demonstration by the teacher trainer, the participants had a 10-min practice session with the CS-TGCTS. After participants had become familiar with the components of the CS-TGCTS, they began the Wrst teaching simulation without any time limit imposed. One week later, as scheduling permitted, the participants returned to the computer laboratory and performed their second teaching simulation. The one-week interval was a result of the limited availability of the computer lab and the participants’ schedules. The Wrst simulation consisted of the following sessions presented sequentially: background information, inventories, classroom teaching, and treatments; the second simulation comprised classroom teaching and debrieWng. The classroom teaching session included four main teaching activities: arranging student location, giving an advance organizer, teaching lesson content, and evaluating student performance. Two types of treatments were incorporated in the CS-TGCTS program to promote mindful learning of professional knowledge, self-awareness of teacher behaviors, and reXective teaching (Yeh, 2004). The Type I treatment included Wve text Wles of researchbased the literature on teaching critical thinking. The Type II treatment comprised a personalized bar chart depicting each participant’s actual usage rate of each teacher behavior during the Wrst simulation. Fig. 1 shows an example of the bar graph provided to each participant in the Type II treatment. While the pretest scores of the teacher behaviors were obtained from the Wrst simulation, the posttest scores of teacher behaviors were collected from the second simulation. 3.4. Analyses Several Repeated Measures Analyses of Variance were performed to test gender eVects and the eVects of the four independent variables (CT-dispositions, thinking styles, CT-skills, and.

(7) Y.-C. Yeh / Computers & Education 48 (2007) 495–507. 501. Fig. 1. Example of the graph provided to individual participants in the Type II treatment. Note: Ad. Organizer, provide advance organizers; Review, provide review session; Focus, keep students focused on undertaking tasks or discussions; Thinking time, give time for thinking; Variety, allow a variety of student answers; Cue, give cues when students cannot answer correctly; Feedback, give positive feedback; Discussion, conduct group discussions; Monitor, monitor students’ discussion process; HOT Q, ask higher-order questions; Extended Q, ask extended questions; Explanation, request explanations for answers; Motivation, evaluate students’ motivation in learning critical thinking; Skill, evaluate students’ critical-thinking skills. The evaluation of student motivation and skills are not included in the 12 deWned eVective teacher behaviors, but they are part of the instructional design in the CS-TGCTS program.. intrapersonal intelligence) on the changes of the dependent variable (teacher behaviors). In these analyses, two levels of variables were used: Group and Test. The Wrst level was the gender Group (male vs. female) or the independent variable Group (low vs. high group divided by the mean score). The second level Test was the dependent variable scores (pretest vs. posttest teacher behaviors).. 4. Results 4.1. Gender diVerences and average time used for simulation One Repeated Measure Analysis of Variance was employed to test gender eVects on behavior change. No signiWcant Gender (male vs. female) £ Test (pretest vs. posttest) interaction eVect ( D .99, p D .113) or main eVect of Gender, F(1, 175) D 0.02, p D .877, on the preservice teachers’ behavior change was found, which revealed that the CS-TGCTS was not genderbiased. On average, the participants took 73.69 (SD D 17.43), 53.99 (SD D 18.25), and 127.66 (SD D 32.18) minutes to complete the Wrst, the second, and the entire simulation program, respectively..

(8) 502. Y.-C. Yeh / Computers & Education 48 (2007) 495–507. 4.2. EVects of personalities on behavior change Four Repeated Measures Analyses of Variance were performed to examine the eVects of CTdispositions and the three thinking styles on the participants’ behavior change. None of the Group (high vs. low) £ Test (pretest vs. posttest) eVects was signiWcant, s D .99, 1.00, 1.00, .98 for CT-dispositions, judicial style, legislative style, and executive style, respectively. However, the four analyses yielded signiWcant main eVects of Test on behavior change, F(1, 175) D 34.40, p < .001 for CT-dispositions and Fs(1, 174) D 31.58, 32.36, 28.36, ps < .001 for judicial, legislative, and executive style, respectively (see Tables 1 and 2). These Wndings indicate that the preservice teachers used more positive teacher behaviors on the posttest than on the pretest in all the analyses. Moreover, except for the executive style, F(1, 174) D 0.03, p D .863, the other three main eVects of Group were signiWcant, F(1, 175) D 9.89, p < .01 for CT-dispositions and Fs(1, 174) D 10.91, 5.18, ps < .05 for judicial and legislative style, respectively. Comparisons of the marginal means revealed that the preservice teachers with high CT-dispositions showed greater improvement in teacher behaviors than those with low CT-dispositions. Moreover, those who were prone to the judicial and legislative thinking styles demonstrated more improvement in teacher behaviors than those who were not. 4.3. EVects of abilities on behavior change No signiWcant Group (low vs. high CT-skills) £ Test (pretest vs. posttest) interaction eVect was found in the Repeated Measures Analysis of Variance ( D 1.00, p D .882). However, there were signiWcant main eVects of Test and Group, Fs(1, 175) D 32.47, 10.71, ps < .001 (see Table 3). Comparisons of the estimated marginal means revealed that the preservice teachers used more positive teacher behaviors on the posttest than on the pretest; moreover, those who had a tendency to think critically showed greater improvement in teacher behaviors than those who did not. Again, the Repeated Measures Analysis of Variance did not yield a signiWcant Group (low vs. high intrapersonal intelligence) £ Test (pretest vs. posttest) interaction eVect ( D .99, p D .187); but there were signiWcant main eVects of Test and Group, Fs(1, 175) D 33.28, 3.99, ps < .05 (see Table 4). Comparisons of the estimated marginal means revealed that the preservice teachers used more. Table 1 Main eVects of CT-disposition Group and Test on behavior change N Group Low High Test Pretest Posttest ¤ ¤¤. p < .01. p < .001.. p. 2. 9.89¤. .002. .05. 34.40¤¤. .000. .16. M. SD. F(1, 175). 93 84. 51.56 57.71. 1.35 1.42. 177 177. 51.10 58.17. 0.98 1.30.

(9) Y.-C. Yeh / Computers & Education 48 (2007) 495–507. 503. Table 2 Main eVects of thinking-style Group and Test on behavior change M. SD. F(1, 174). p. 2. 86 90. 51.13 57.59. 1.40 1.37. 10.91¤¤. .001. .06. Test Pretest Posttest. 176 176. 50.98 57.74. 0.97 1.30. 31.58¤¤¤. .000. .15. Legislative style Group Low High. 100 76. 52.46 57.03. 1.32 1.51. 5.18¤. .024. .03. Test Pretest Posttest. 176 176. 51.29 58.20. 0.99 1.33. 32.36¤¤¤. .000. .16. Executive style Group Low High. 74 102. 54.64 54.29. 1.00 1.35. .863. .00. Test Pretest Posttest. 176 176. 51.24 57.68. 1.00 1.35. 28.36¤¤¤. .000. .14. N Judicial style Group Low High. ¤ ¤¤ ¤¤¤. 0.03. p < .05. p < .01. p < .001.. Table 3 Main eVects of CT-skill Group and Test on behavior change N. M. SD. F(1, 175). p. 2. Group Low High. 79 98. 50.93 57.34. 1.46 1.31. 10.71¤. .001. .06. Test Pretest Posttest. 177 177. 50.66 57.61. .97 1.32. 32.47¤¤. .000. .16. ¤ ¤¤. p < .01. p < .001.. positive teacher behaviors on the posttest than on the pretest. In addition, those with high intrapersonal intelligence showed better improvement in teacher behaviors than their classmates who scored low on intrapersonal intelligence..

(10) 504. Y.-C. Yeh / Computers & Education 48 (2007) 495–507. Table 4 Main eVects intrapersonal-intelligence Group and Test on behavior change N Group Low High Test Pretest Posttest ¤ ¤¤. p. 2. 3.99¤. .047. .02. 33.28¤¤. .000. .16. M. SD. F(1, 175). 88 89. 52.49 56.45. 1.41 1.40. 177 177. 50.98 57.95. 0.98 1.32. p < .05. p < .001.. 5. Discussion and conclusions This study examined aptitude-treatment interaction eVects during a computer-simulated training session in an attempt to understand whether four teacher traits (CT-dispositions, thinking styles, CT-skills, and intrapersonal intelligence) would interact with the designed treatments and inXuence preservice teachers’ improvement of teacher behaviors during computer-simulated training. The Wndings support the hypothesis. Except for the executive thinking style, all targeted teacher traits had positive eVects on the preservice teachers’ behavior change. The CS-TGCTS program was designed to improve teacher behaviors by stimulating mindful learning, self-awareness, and reXective teaching; the signiWcant results found in this study suggest that important ATIs occur during computer-simulation training and inXuence its outcomes. Positive personal traits – including CT-dispositions, judicial and legislative thinking styles, CT-skills, and intrapersonal intelligence – inXuence how preservice teachers learn and adapt to information, feedback, and teaching practices. Interpreting aptitudes as personal readiness to proWt from particular treatment situations (Snow, 1991), this study shows that the teacher traits addressed here are important to preservice teachers’ professional growth. Therefore, teacher education programs should increase their eVorts to cultivate such positive teacher traits. The Wndings in this study also suggest that CT-dispositions and thinking styles contribute to expert thinking, teacher behaviors, and professional growth (Sato, Akita, & Iwakawa, 1993; Sternberg, 1997; Zhang, 2001). This study found that preservice teachers with high levels of CT-dispositions showed greater improvement in teacher behaviors than those with low levels. The development of expertize in any area requires deliberate, eVortful, and intense cognitive work (Wagner, 1997); not surprisingly, teaching is no exception. CT-dispositions contribute to expending mental eVort toward learning how to analyze complex classroom situations (Sears & Parsons, 1991). Such conscious exertion of mental eVort is essential to a teacher’s professional growth. In this study, those with a judicial thinking style beneWted most from the computer-simulated training, those with legislative thinking styles closely followed, and those with executive thinking styles lagged far behind (see F values in Table 2). Sato et al. (1993) suggested that expert teachers are characterized by thinking styles such as sensitivity, involvement in a situation, having multiple points of view, and having a wide perspective. In addition, such expert thinking is context relevant, in that a problem-framing approach is employed in constructing and reconstructing thoughts on.

(11) Y.-C. Yeh / Computers & Education 48 (2007) 495–507. 505. teaching. From Wndings and suggestions concerning teachers’ thinking styles (Chang, 1998; Sternberg, 1997; Zhang, 2001), we see that expert teachers’ thinking and behaviors demonstrate either judicial or legislative thinking styles. These thinking styles contribute to preservice teachers’ professional growth during teacher training. In this study, preservice teachers with good CT-skills and high intrapersonal intelligence showed greater improvement in teacher behaviors. These results support earlier Wndings (Garcia & Pintrich, 1992; Shepard et al., 1999) that CT-skills are eVective learning strategies and that intrapersonal intelligence functions as a strategic processing of executive control in problem-solving and teaching practices. Critical thinking involves the employment of cognitive and metacognitive skills that are required for self-regulation, self-assessment, and problem solving; intrapersonal intelligence concerns one’s self-awareness, reXective thinking, and the abilities to analyze performance and change. These abilities are essential to reXect upon one’s learning and to adapt one’s behaviors to enhance student learning. A professional teacher is one who continuously learns from teaching. Accordingly, teacher educators should develop preservice teachers’ capacity for being mindful, systematic, and reXective in their learning as well as in their teaching processes. In conclusion, the Wndings in this study conWrm the importance of four teacher traits on preservice teachers’ professional growth. Positive teacher traits, such as CT-dispositions, judicial or legislative thinking style, CT-skills, and intrapersonal intelligence relate to mindful learning, selfawareness, and reXective thinking in teaching practice. They inXuence the improvement in teacher behaviors that can be achieved during teacher training via a computer simulation. Moreover, computer simulations, which provide valuable information for teacher preparation, have proven eVective tools for understanding preservice teachers’ professional growth. Further studies can identify other personal traits and mechanisms that contribute to teachers’ professional growth via computer-simulated teaching programs.. Acknowledgments This research was supported in part by the National Science Council, Taiwan; Grant No.: NSC90-2520-S-110-001. I thank Dr. Carol Tomlinson and Cindy Strickland for their comments on this article.. References Abell, S. K., Bryan, L. A., & Anderson, M. A. (1998). Investigating preservice elementary science teacher reXective thinking using integrated media case-based instruction in elementary science teacher preparation. Science Teacher Education, 82, 491–509. Armstrong, T. (2000). Multiple intelligences in the classroom (2nd ed.). Alexandria, VA: Association for Supervision and Curriculum Development. Bailin, S., Case, R., Coombs, J. R., & Daniels, L. B. (1999). Common misconceptions of critical thinking. Journal of Curriculum Studies, 31(3), 269–283. Birman, B. F., Desimone, L., Porter, A. C., & Garet, M. S. (2000). Designing professional development that works. Educational Leadership, 57(8), 28–33. Browne, M. N., & Meuti, M. D. (1999). Teaching how to teach critical thinking. College Student Journal, 33(2), 162–170..

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