Students’ self-efficacy of computer through the use of cognitive thinking style
This study aimed at investigating students’ self-efficacy of computer skills using the cognitive thinking style at Sultan’s Qaboos University. The sample consisted of (164) students (48 males, 116 females). The self-efficacy scale of computer skills which was translated into Arabic and adapted for the Omani students was implemented along with the cognitive thinking style scale. One way analysis of variance (ANOVA) was used to calculate the students’ self-efficacy . For post comparisons purposes the Scheffe test was also used.
Results showed that abstract cognitive thinking style of self-efficacy in learning computer skills was higher than concrete and iconic styles. Findings also revealed significant differences in self-efficacy in learning computer skills favoring students with abstract, concrete , and iconic thinking styles. This justified the importance of the educational implications represented by the importance of the theoretical experiences in increasing the self-efficacy in learning various skills at the university or any other educational level.
Technology provides instructional designers with the ability of designing and evaluating the available instructional experiences in the teaching and learning environments. Also, the use of computers contributes in learning the most advanced experiences as the advanced thinking skills where various mental processes are employed. This justifies the importance of using the modeling processes in practicing new mental processes according to various social cognitive contexts.
Focusing on the teaching and learning experiences by the social cognitive theory has opened new horizons in benefiting of computers as an appropriate medium for learning and practicing- different skills such as the thinking skills and the skills of operating the knowledge tools and its channels. This idea was supported by the fact that individuals differ in their thinking styles generating the knowledge that they need (Bruner, 1986, 27). Relating the use of computers to personal variables such as motivation and thinking style may improve the learning effects caused by the use of technology that may provide a better understanding of the learning environment and the factors affecting it. There is still a need to examine computers’ effects caused by the learners’ personal variables. Understanding such effects may help in designing learning environments that are appropriate to the thinking level of the university students and to their cognitive preferences. This may contribute in developing students’ thinking and achievement (Dempsey and Sales, 1993).
Self-efficacy was based on the importance of classroom activities exerted by the learner in organizing the learning situation. It was also based on modeling the experience by a live person or presented by a computer. Observing a model may contribute in developing the learner’s self-efficacy, thinking style, and mental processes (Joo Lingen and Jon, 1994).
Students’ thinking style, abilities, and motivation interacts with their self-effcacy that may improve their performances regardless of the medium used. Thus encouraging them to observe a model in an appropriate way, leading to the improvement of their performances (Steinberg, 1989).
Studying the self-efficacy of computer skills through the use of cognitive thinking style that is based on Bruner’s theory (Bruner, 1973), is set on the assumption that self-efficacy is a personal effort that can be increased on purpose, according to the style used by the learner. This means that self-efficacy may be influenced by the mental efforts exerted by the individual in developing his/her skills. And since computer skills motivate the learner, it may motivate his/her self-efficacy demonstrating the best performance. Cognitive thinking styles are determined by the learner’s prior knowledge mental practices and experiences. They control the learner’s performances toward any experience in any situation. Thus, the difference in the cognitive thinking styles is divided into three levels (concrete, iconic, and abstract) that may make self-efficacy in learning computer skills different. Based on this point, the interest of this study becomes of great importance.
Bandura (1986) defined self-efficacy as a belief in one’s capability of performing a specific task. He also stated that when individuals are faced with live models that lead to active performances, their motivation increases. This definition was supported by Gist, Schworer, and Rosen(1989).
Cognitive thinking style directs students’ attention, senses and interaction toward the experiences that they are presented with. Thus, the preferences styles of the individuals are developed by integrating them into various situations with age (Heckler, Childers, and Houston, 1993, 119).
The conceptual meaning of the cognitive thinking style is determined, by the method used in integrating the knowledge and the experiences of the individual. This method is identified by three styles:
1. the concrete style (that deals with concrete and real experiences)
2. the iconic style that deals with experiences that depend on charts, illustrations and drawings
3. the abstract style that deals with experiences that depend on words and symbols. Bruner (1973) assumed that some cognitive thinking styles control the individuals when receiving comprehending, internalizing and integrating, experiences in their cognitive structure. According to this understanding, cognitive representations are usually considered a way of organizing the individual’s experiences to fit the general style that is used in most learning and teaching situations.
To date, reviews of the psychological and educational literature have showed little or no evidence of computer skills using the cognitive thinking style, but there is an interest in the cognitive processes and in the computer as a technological medium that may provide appropriate instructional environments.
Rieber and Kini (1995) reported that the use of computers in learning deductive thinking strategies is effective. They also reported that computer modeling in learning the advanced mental processing strategies was effective. Honebein, Carr and Duffy (1993) conducted a study to examine the method that can help the learner in modeling a solving problem skill using the computer as a learning medium. Findings showed that some skills were developed for those individuals who observed the expert model. They also documented the importance of the cognitive modeling and its role in integrating the observed experiences in the learner’s cognitive structure. Davidson (1990) indicated that the cognitive thinking style differs among individuals and may affect their learning and their performances in learning situations. He assumed that the cognitive thinking style refers to the individuals’ characteristics used in developing, processing and storing the knowledge. He also stated that research investigating the relationship between the cognitive thinking style and the individual’s performances and benefits of the activities of the computer in the learning situations is still limited.
The above shows that various thinking styles of the learners may affect the degree of their benefit from the models that are presented to them. It may also affect the degree of self-efficacy that individuals show in learning situations to develop their skills and performances. Thus, this study examines the self-efficacy of computer skills through the use of the cognitive thinking style for the students at Sultan Qaboos University.
STATEMENT OF THE PROBLEM
Cognitive thinking style is one of the main variables that determines the student’s interaction style with the experiences that he faces as the instruments of such interaction. The instruments used by the students differ in obtaining the knowledge, its comprehension, storage and integration in the available cognitive structure (Glover, Ronning and Bruning, 1990).
Thinking styles also differ in comprehending the knowledge or the skill based on differences of the cognitive structure of the student (Holyoak, 1987,293). Such styles determine the mental processing methods (Nyikos, 1987) that students use in solving a problem, or in writing a report, or in processing a computer issue or any other cognitive knowledge (Hwang, 1995, 40). Thus, cognitive psychologists assume that cognitive styles determine the method in which the individual can expand his/her cognitive modalities and skillful experiences. It also remains as one of the many methods used to achieve any task or training (Byrnes, 1992,29).
Cognitive thinking style, in its conceptual structure, determines its effects including its motivational and self-efficacy effects (Bandura, 1986). For instance, the individual who is controlled by sensory methods, focuses on the sensory tools in developing his knowledge, but an individual with iconic experiences, focuses on illustrations, mnemonics shapes, drawings, and pictures in understanding his/her experiences. Whereas, an individual with abstract thinking experiences uses words, numbers, symbols and ideas to conduct deductive processes, and devise the embedded knowledge (Bruner, 1973). This is related to the self-efficacy situation, and to the increase or the decrease of the effect that is exerted in the cognitive structure of the experiences. Thus, the problem of the statement is determined by examining the self-efficacy of computer skills using students’ cognitive thinking style at Sultan Qaboos University.
On the basis of the foregoing discussion, this empirical study aims at answering the following two questions:
1. Is there any significant difference among mean scores of self-efficacy of computer skills (starting skills, general frame skills, advanced skills, and files skills) according to students level of cognitive thinking style (concrete, iconic, abstract) in the college of education at Sultan Qaboos University?
2. Is there any significant difference among mean scores of self-efficacy for the computer general skill according to students’ cognitive thinking level (concrete, iconic, abstract) in the College of Education at Sultan Qaboos University?
IMPORTANCE OF THE STUDY
This study is considered important due to the fact that the Arabic educational and psychological literature is lacking studies that investigate the relationship of the cognitive styles and the modern technology represented here as the computer skills (Allison, 1995).
This study implemented the social cognitive theory as studied by Bandura (1986) who paid attention to the importance of the cognitive level in comprehending live or vicarious social experiences, and the results that follow such as vicarious reinforcement, or reciprocal learning, or modeling of experiences. These experiences may be modeled using the effectiveness of computer skills.
This study is testing the social cognitive theory in the contemporary era of computer learning (Torkzadeh and Koufteros, 1994, 820). And it will contribute in understanding the psychological effects of the self-efficacy and its effects in improving the learning of computer technology skills. This may help faculty members at the universities in increasing students’ contributions in learning computer skills. It may also draw instructors’ attention to the effectiveness of the students and their understanding of the learning objectives.
Finally, this study may draw the researchers’ attention to the importance of this kind of psychological factors in teaching and learning more advanced experiences and skills such as the computer skills.
Participants were 164 undergraduate freshmen students (48 males, 116 females) that were enrolled in the introductory computer course in the fall semester in the College of Education at Sultan Qaboos University.
The self-efficacy scale of computer skills was used. It consisted of starting skills, general frame skills, advanced skills and file skills as well as the total score of the computer skills. To ensure that computer skills were included in the scale, the researchers reviewed the course content and its objectives. The self-efficacy scale was implemented, then” scores of each student were obtained. The scale of the developed cognitive thinking style scale for the Omani students was also implemented to collect the required data in order to analyze it using the SPSS program.
To obtain the data, two scales were used to answer the research questions. They were the self-efficacy scale of computer skills and the cognitive thinking style scale.
First: The Self-efficacy scale of Computer skills
Murphy, Coover, and Owens (1989) constructed this measure in its original version based on reviewing the literature in education, educational psychology, educational technology including the analyses of micro and major computer studies. The measure was given to five experts who were teaching various courses in computers. The experts were asked to determine the suitability, clarity, and difficulty, of the items as well as the adequacy of the measure as a whole. Then, the measure was reviewed and changes were made according to experts’ notes. The modified version consisted of (30) items, each one of them started with the phrases “I feel confident that…”
A five point Likert scale was used with “high confidence” as (5), and “low confidence” as (1). High total score indicates the participants’ high confidence in his/her effectiveness in using computer skills. The data provided the researchers with reliable psychometric indicators that justify the results of the measure and its use.
The scale was translated into Arabic to facilitate the students’ comprehension of the instruction and the items. The Arabic version was given to a specialist in teaching methods of English language with good experience of computer tasks, to translate it into English language (back-up translation). The comparison of the translated version from the Arabic version and the English version showed high degree of conformity. This justified the use of the Arabic version in the Arabic environment. The developed measure was applied twice on a sample which consisted of (40) Omani students. There was a three weeks period between the two applications. Test-retest was used to compute the reliability coefficient. The reliability alpha was (0.82). Also, by calculating the correlation between the score on each item and the total score on the measure, internal consistency was found to range between (0.27 – 0.92). The Alpha was (0.81).
Finally, the researchers were confident of the results they obtained because of the procedures provided by the content analyses of the computer course given to the students as well as the back up translation of the measure which has reliability and validity indices.
Second The cognitive thinking style scale
The researchers reviewed the psychological and educational literature in the areas of learning styles, thinking styles, as well as the theoretical applications of cognitive learning and thinking (Esquired, 1995; Murray-Harvey, 1994; Ebert, 1994; Glover, Roming and Bruning, 1990; Holyoak, 1987). The researchers also reviewed the instruments and scales that have been developed in the cognitive areas of learning styles and cognitive thinking in order to construct the items of the scale used in this study.
The final version of the scale consisted of 30 items after all revisions were made including the content validity. Reliability was also obtained using testretest method. It was found to be (0. 844). Moreover, the scale was comprised of three levels in each item. The concrete cognitive thinking style was presented in the first level followed by the iconic in the second, and the abstract in the third. Scores were distributed on the scale according to the levels as follows: (30 and less) on the concrete; (31-62) on the iconic; and (63-93) on the abstract.
One Way Analysis of Variance (ANOVA) was used for each area of the selfefficacy skill , and for the total score of the general self-efficacy skill according to three cognitive thinking styles (concrete, iconic, abstract).
The Scheffe’ test was also used to examine the significant differences of the participants’ scores on the computer scale.
In order to answer the first question in the study, One Way Analysis of Variance (ANOVA) was used to find the effect of the self-efficacy of computer skills on each level of the cognitive thinking style at the level of 0.001. Findings indicated that there is a significant difference between the self-effcacy effect of starting computer skills and the concrete, iconic, and abstract thinking styles. Findings are shown in Table 1.
Results of the Scheffe’ test indicated that there is a significant difference between self-efficacy scores and high scores of the computer starting skills. Results showed that differences were significant regarding the performances of students of concrete, iconic and abstract cognitive thinking styles in favor of those with the abstract cognitive thinking style. (Table 2).
Findings supported the hypothesis stating that individuals differ in their thinking styles toward learning any skill including the computer skills (Murphy, Grover, and Owen, 1989).
The high self-efficacy scores of the starting computer skills for students with abstract cognitive thinking style in comparison to the computer may be attributed to the fact that students with abstract cognitive thinking style had higher theoretical experiences and knowledge. Whereas, students of concrete and iconic cognitive thinking style lack such experiences and knowledge (Murray, 1994). This agrees with the general trend of the cognitive thinking style where self-efficacy increases for those who have a solid theoretical basis of the skill need to be learned. The ANOVA shown in Table 3 revealed significant differences for the three cognitive thinking styles for the computer general frame self-efficacy skill, > 0.05.
In order to test the extent of the significant differences of the self-efficacy of the computer general frame skill, the Scheffe Test was used. As shown in Table 4, findings revealed significant differences in the self-efficacy of the computer general frame in favor of the abstract cognitive thinking style.
The findings of this study supported the preferability of the self-efficacy of students with various cognitive thinking styles. This may be attributed to the fact that individuals of various cognitive thinking styles differ in their motivation to interact with the computer general frame skills. This is in agreement with the logic of the thinking styles differences and their effect on the individuals’ motivation in learning the skills (Glover, et. al. 1990).
The findings also showed that differences were significant regarding self-efficacy of computer skills related to general framework skill in favor of the abstract cognitive thinking style.
The ANOVA shown in Table 5 revealed that there were significant differences in the self-efficacy of the computer advanced skills at the level of >00.1.
As shown in Table 6, findings of the Scheffe’ test indicated that there were significant differences in self-efficacy for the computer advanced skills favoring students with the abstract cognitive thinking style (M iconic = 11.58, M abstract = 13.22).
The results were in agreement with, the logical basis that the social cognitive self-efficacy theory is based on as assumed by Bandura (1986). That is, when abstract experiences of individuals with cognitive thinking style increase, the preferability of their self-efficacy and exerted efforts increase in learning iconic and abstract skills. The results also showed that self-efficacy requires more abstract theoretical experiences than other skills. This contributes in improving the performances of such skills (Salisburry, 1990, 25).
Findings also showed that differences in self-efficacy scores of computer files skills were significant at the level of >0.001 among the various levels of the cognitive thinking styles (concrete iconic,and abstract). Findings are shown in Table 7. Scheffe’ test was used to test the significance within the three cognitive thinking styles. As shown in Table 8, findings showed that there are differences between individuals of iconic and concrete cognitive thinking styles in favor of the iconic style (M con = 11. 88; M iconic = 13. 1), and between individuals of abstract cognitive thinking style and concrete and iconic respectively in favor of the abstract style (M concrete = 11.88, m abstract = 14.4) (M iconic = 13.1, M abstract = 14.4).
Findings showed the preferability of the self-efficacy for students of various cognitive thinking styles. This may attributed to the fact that the nature of the files skills requires parts of the concrete, iconic, and abstract information in its learning. Thus, the findings were consistent with the preferability of the cognitive thinking styles as well as the requirements of self-efficacy on the files skills that requires various experiences. The findings also indicated the preferability of differences between students of the concrete and the iconic and the abstract cognitive thinking levels in benefiting of the self-efficacy in improving their skills. The findings were also consistent with the theoretical basis that this study was based on (Rieber and Kini,1995, 138).
In order to answer the second question concerning testing students differences in self-efficacy of the general computer skills according to the three cognitive thinking styles (concrete, iconic and abstract), the ANOVA shown in Table 9, indicated significant differences in the general skill at the level >0.001.
As shown in Table 10 on the following page, findings of the Scheffe’ test indicated that there were significant differences in the general self-efficacy in the general computer skills between the concrete, the abstract cognitive thinking styles (Mcon = 61.43, Mabst. = 70.35) and between the iconic and the abstract cognitive thinking style (Miconic = 63.30, Mabst. =70.35) in favor of the abstract style.
Studying general self-efficacy and the differences among students’ performances of various cognitive thinking styles indicates that there are differences in the self-efficacy among students with concrete, iconic, and abstract cognitive thinking styles, confirming the preferability of the general self-efficacy of students with the abstract cognitive thinking style. This indicates the high effect of motivation represented in the general self-efficacy for students with the abstract thinking style on learning general computer skills. The findings were consistent with the theoretical basis of motivation and cognitive theories (Davidson, 1990, 37). That is, students of abstract cognitive thinking style are highly motivated and have higher self-efficacy than those students of concrete and iconic cognitive thinking styles. This was clearly indicated in showing higher motivation to comprehend the general computer skills as well as their control over the general skills. The findings of this study were consistent with the findings of many studies in the American and British Cultures (Bruner, 1973; 1986; Holyoak, 1987; Hwang, 1995).
The findings should be taken into consideration in the university learning and training situations when necessary computer skills are taught. Findings also showed the importance of the theoretical and the cognitive experiences that students of the cognitive thinking style have more of. Furthermore, the findings implied theoretical information-nation available for the students may contribute greatly in increasing their general self-efficacy when learning and mastering the computer skills. Finally, there is a dear need to conduct more research studies in the social cognitive theory as related to learning computer skills and other variables in the university learning.
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Allison, L. (1995). The status of computer technology in classrooms using the integrated thematic instruction model. International Journal of Instructional Media, Vol 22 (1), 33-43.
Bandura. A. (1986). Social foundation of thought and action: a social cognitive theory. Englewood Cliffs, Prentice-Hall.
Bruner, J. (1973). Beyond the information given: Studies in the psychology of knowing. New York: Norton Pub. Co.
Bruner, J. (1986). Actual minds, possible worlds. Cambridge, MA: Harvard University Press.
Byrnes, J. (1992). Categorizing and combining theories of cognitive development and learning. Educational Psychology Review, 4, 1-35.
Davidson, G. (1990). Matching learning styles with teaching styles: is it useful concept in instruction? Performance and Instruction, 29 (4), 36-38. Dempsey, J., and Sales, G. (eds.) (1993). Interactive instruction and feedback. Educational Technology Publications, Englewood Cliffs, NY Esquirel, G. (1995). Teacher behavior that foster creativity. Educational Psychology Review, VoL 7,(2),185-202.
Gist, M. Schworer, C. and Rosen, B. (1989). Effects of alternative training methods on self-efficacy and performance in computer software training. Journal of Applied Psychology, VoL 74( 6), 884-891.
Glover, J., Ronning, R., & Bruning,R. (1990). Cognitive Psychology for Teachers. New York, MacMillan Co.
Heckler, J. Childres, T.. and Houston, M. (1993) On the construct validity of the SOP scale. Journal of Mental Imagery, 13( 3&4), 119-132.
Holyoak, K. (1987) A connectionist view of cognition. Science, 236, 292-296. Honebein, P. Carr, A. Duffy T. (1993) The effects of modeling to aid problem solving in computer-based learning environment. 15th Annual National Convention of the Association for Educational Communications and Technology, AECT, 373-399. Hwang, A. (1995) Two traditions of systems thinking in instructional development. Edu
cational Technology, March- April. 40-42.
Joo -lingen W. and Jon, T. (1994) An extended dual search space model discovery learning. Paper presented at the annual meetings, of the American Educational Research Association, New Orleans.
Murphy, C., Coover, D. and Owen, S. (1989) Development and lidation of the computer self-efficacy scale. Educational and Psychological Measurement, 49, 893-899. Murray – Harvey, R. (I 994) Learning style and approaches to learning distinguishing between concepts and instruments. British Journal of Educational Psychology, 46, 373-388.
Rieber, L. and Kini”A. (1995). Using computer simulations in inductive learning strategies with children in science. International Journal of Instructional Media, VoL 22 (2), 135-144.
Salisbury, D. (I 990) Cognitive psychology and its implication for designing drill and practice programs for computers. Journal of Computer Based-Instruction,17, 23-30.
Steinberg, E. (1989) Cognition and learner control: A literature review 1977-1988. Journal of Computer-Based Instruction, 16,117-121.
Torkzadeh, G. & Koufteros, X. (1994) Factorial validity computer self-efficacy scale and the impact of a computer training. Educational and Psychological Measurement Vol. 54 (3), 813-821.
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