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Journal articles on the topic 'Computer self-efficacy'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Karsten, Rex, Atul Mitra, and Dennis Schmidt. "Computer Self-Efficacy." Journal of Organizational and End User Computing 24, no. 4 (October 2012): 54–80. http://dx.doi.org/10.4018/joeuc.2012100104.

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Computer self-efficacy (CSE) has been a popular and important construct in information systems research for more than two decades. Although CSE researchers have conducted extensive qualitative reviews, quantitative analyses are lacking for studies of the relationships between CSE and variables of interest. This study provides such a quantitative analysis. The authors meta-analyze 102 empirical CSE studies that reported 232 usable statistical relationships between CSE and seven correlates. Their main meta-analysis demonstrates that CSE is significantly correlated in the theoretically predicted direction with each of the seven correlates. They also quantitatively assess five study characteristics as potential moderators of the CSE-correlate relationships. The moderator analysis reveals complex patterns and indicates that more research is needed to investigate possible moderating effects.

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2

Yeşilyurt, Etem, Abdulhak Halim Ulaş, and Durdağı Akan. "Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education." Computers in Human Behavior 64 (November 2016): 591–601. http://dx.doi.org/10.1016/j.chb.2016.07.038.

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3

Arief, Ulfah Mediaty. "Effect of Computer Self-Efficacy on Teacher Performance using E-portfolio." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (February 28, 2020): 279–83. http://dx.doi.org/10.5373/jardcs/v12sp3/20201263.

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4

Downey Jr., James P., R. Kelly Rainer, and Summer E. Bartczak. "Explicating Computer Self-Efficacy Relationships." Journal of Organizational and End User Computing 20, no. 3 (July 2008): 22–40. http://dx.doi.org/10.4018/joeuc.2008070102.

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5

Cassidy, Simon, and Peter Eachus. "Developing the Computer User Self-Efficacy (Cuse) Scale: Investigating the Relationship between Computer Self-Efficacy, Gender and Experience with Computers." Journal of Educational Computing Research 26, no. 2 (March 2002): 133–53. http://dx.doi.org/10.2190/jgjr-0kvl-hrf7-gcnv.

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6

Aktag, Isil. "Changes in computer self-efficacy of pre-service teachers in physical education." International Journal of Academic Research 5, no. 6 (December 10, 2013): 169–77. http://dx.doi.org/10.7813/2075-4124.2013/5-6/b.27.

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7

Kin, Kenzi, Orochi Oki, and Raiden Rai. "Impact Of Computer Anxiety On Computer Self Efficacy." IAIC Transactions on Sustainable Digital Innovation (ITSDI) 2, no. 1 (October 25, 2020): 69–74. http://dx.doi.org/10.34306/itsdi.v2i1.354.

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Data innovation is as of now turning into a worldwide pattern, where it tends to be analyzed by the regular utilization of different incorporated exercises utilizing PC gadgets. When contrasted with manual frameworks, electronic frameworks additionally give a few favorable circumstances to its clients, for example, programmed posting, result volume, speed, mistake counteraction, etc. Aside from these preferences, frequently the outcomes to be accomplished in the utilization of frameworks are mechanized not accomplished, this is likewise because of the irregularity between the modernity of data innovation applied by the association and the individual abilities in its activity. This examination expects to affirm the impact of PC nervousness on PC self-adequacy on the representatives of the North Badung Primary Tax Office. This investigation utilized an immersed test, where the quantity of surveys that were practical to be broke down was 53 polls. Speculation testing utilizing t-test. The aftereffect of this investigation is that PC nervousness negatively affects PC self-viability with a Sig. adding up to 0,000. To additionally improve representative PC self-viability in utilizing PCs, associations should direct preparing on utilizing PC programs consistently. The preparation will legitimately expand the person's view of his capacity to finish undertakings utilizing PC help.

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8

Busch, Tor. "Gender Differences in Self-Efficacy and Attitudes toward Computers." Journal of Educational Computing Research 12, no. 2 (March 1995): 147–58. http://dx.doi.org/10.2190/h7e1-xmm7-gu9b-3hwr.

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This study is aimed to investigate gender differences regarding computer attitudes and perceived self-efficacy in the use of computers among 147 college students. At the end of a computer course, the students completed a questionnaire designed to measure self-efficacy, computer anxiety, computer liking, and computer confidence. The results revealed gender differences in perceived self-efficacy regarding completion of complex tasks in both word processing and spreadsheet software. No gender differences were found in computer attitudes or self-efficacy regarding simple computer tasks. Male students had previously had more computer experience in programming and computer games and reported that they had previously had more encouragement from parents and friends.

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9

Henry, John W., and Robert W. Stone. "Computer self-efficacy and outcome expectancy." ACM SIGCPR Computer Personnel 16, no. 4 (October 1995): 15–34. http://dx.doi.org/10.1145/219716.219722.

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10

Torkzadeh, Gholamreza, Xenophon Koufteros, and Kurt Pflughoeft. "Confirmatory Analysis of Computer Self-Efficacy." Structural Equation Modeling: A Multidisciplinary Journal 10, no. 2 (April 2003): 263–75. http://dx.doi.org/10.1207/s15328007sem1002_6.

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11

Zhang, Yixin, and Sue Espinoza. "Affiliations of Computer Self-Efficacy and Attitudes with Need for Learning Computer Skills." Journal of Educational Computing Research 17, no. 4 (December 1997): 371–83. http://dx.doi.org/10.2190/3gny-apr6-51q8-402n.

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This study investigated relationships concerning computer efficacy, attitudes toward computers, and need for learning computer skills. A total of 296 undergraduate students from a regional state university participated in this study in 1996. Three research questions were measured by Attitudes toward Computer Technologies and Confidence and Desired Knowledge with Computer Technologies. Statistical analyses consisted of simultaneous multiple regression, ANOVA and Tukey's HSD. ANOVA revealed that students from computer classes had more need for learning computing skills than students from a non-computer class. Multiple regression revealed that attitudes toward computers, and computer self-efficacy, were significant predictors of the need for learning computing skills between two groups of students, although students from computer classes demanded a higher level of computing skills than those from a non-computer class.

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12

Borinaga, Almila Amihan R., and Rodulfo T. Aunzo. "Correlation Between Computer Self-Efficacy Belief and Computer Value Belief during the New Normal." Journal La Edusci 2, no. 2 (July 29, 2021): 58–64. http://dx.doi.org/10.37899/journallaedusci.v2i2.378.

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In the light of the New Normal, the students' attention has moved to computer technology. Thus, this research study investigated the VSU-Isabel students' computer self-efficacy and computer value during the New Normal. The study employed quantitative-descriptive research utilizing the mean and standard deviation of the students' responses in the survey items. It also employed a correlational approach in establishing the relationship between students' computer self-efficacy and computer value. A convenience sampling technique was used to arrive at a sample of 687 student-respondents from 1st year to 5th year. The research study revealed that the students are undecided if they can use computers compared to the other students. Also, it was found out that students agreed about the value of computers. Additionally, the study revealed that there is a significant and low positive correlation, r (685) = .387, p = .01, between the students' computer self-efficacy belief and computer value belief during the New Normal. These findings recommend the expanded use of technology during the new normal such as using different available digital platforms to develop the computer self-efficacy of the students.

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13

Goh, Debbie, Christine Ogan, Manju Ahuja, Susan C. Herring, and Jean C. Robinson. "Being the Same Isn't Enough: Impact of Male and Female Mentors on Computer Self-Efficacy of College Students in It-Related Fields." Journal of Educational Computing Research 37, no. 1 (July 2007): 19–40. http://dx.doi.org/10.2190/3705-4405-1g74-24t1.

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This study investigates the relationship between computer self-efficacy, mentoring, and the gender of mentors and students. The decline of women in IT-related programs of U.S. universities has led scholars to suggest that making more female faculty mentors available could raise female students' computer self-efficacy. This could address women's computational reticence and encourage more women to enter and remain in the programs. A survey of students from IT-related programs in five U.S. universities shows that extent of mentoring received, gender of students, and time students spent on computers as teenagers were significant predictors of computer self-efficacy. Extent of mentoring, gender of students, and gender of mentors have significant main effects on computer self-efficacy. Students with male mentors reported significantly higher computer self-efficacy while students with female mentors reported lower computer self-efficacy. Female students with no mentors reported the lowest level of computer self-efficacy.

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14

Chu, Li-Li. "The Effects of Web Page Design Instruction on Computer Self-Efficacy of Preservice Teachers and Correlates." Journal of Educational Computing Research 28, no. 2 (March 2003): 127–42. http://dx.doi.org/10.2190/k79g-2pyy-vvu6-x988.

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This study tested the effects of Web page design instruction on improving computer self-efficacy of preservice teachers. A sample of 206 preservice teachers participated in this research. A pretest-posttest design experiment, including a 14-week instruction of Web page design, was conducted. The instruction of Web page design significantly improved the computer self-efficacy of preservice teachers. Various computer experiences, including weekly computer use, weekly Internet use, the use frequencies of word processing, e-mail, games, and presentation software, were significantly related to computer self-efficacy. The use frequencies of word processing and computer graphic software, weekly computer use, and age were the significant predictors of computer self-efficacy. The preservice teachers owning higher perception of computer self-efficacy revealed more confidence on Web page design self-efficacy. Generally, it is confirmed that most educators believe that increasing the opportunities to learn and use computers may facilitate the confidence and competence of preservice teachers.

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15

Hassona, Farida M., Aziza Z. F. Ali, and Shaimaa M. Nageeb. "Nursing Students’ Computer Self-Efficacy and Attitudes toward Its Use in The Health Care Setting: A Comparative Study." Evidence-Based Nursing Research 1, no. 4 (January 10, 2020): 9. http://dx.doi.org/10.47104/ebnrojs3.v1i4.90.

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Context: The use of technology and computers in health care has been reported to improve nurses' decision-making and competencies, which in turn increase the quality of health-care practice. Aim: of this study is to assess and compare nursing students’ computer self-efficacy and attitudes toward its use in a health care setting in the Faculty of nursing – Benha and Hail Universities. Methods: A descriptive comparative cross-sectional study design was used to achieve the current study aim. The study conducted in the Faculty of Nursing - Banha University, Egypt, and Faculty of Nursing – Hail University, Kingdom of Saudi Arabia. Quota sampling of 219 students was chosen as follows; 190 students from the Faculty of Nursing - Banha University and 29 students from the Faculty of Nursing – Hail University. A structured self-administered questionnaire covering sociodemographic data, the pretest for attitudes toward computers in healthcare, and computer self-efficacy scales were the tool used to collect the study data. Results: 33% of nursing students from Benha University were have a realistic view of current computer capabilities in health care. In contrast, 28% of nursing students from Hail had a very positive view of computer use in health care. Nursing students at Hail University have the highest mean score compared to Benha nursing students (79.45±15.85 & 61.2±7.25, respectively) in their attitudes toward computer and computer self-efficacy. Benha nursing students have moderate computer self-efficacy compared with Hail nursing students who have a high computer self-efficacy level. A highly statistically significant relationship was detected between students' attitudes toward computers and computer self-efficacy (p-value=0.000). Also, there was a highly positive, statistically significant correlation between the demographic variables of nursing students and both students’ attitudes toward computers in health care and computer self-efficacy (p-value =0.00). Conclusion: Nursing students in both universities have a positive attitude toward computer use. A significant difference between the mean scores of the two groups in their attitude toward computer and self-efficacy was detected, with a highly statistically significant correlation between both students’ attitudes and their self-efficacy. Also, a significant relationship revealed between the studied students’ demographics and their attitude and self-efficacy. Future studies are recommended to evaluate existing technologies in terms of acceptance, effectiveness, and efficiency in real-life settings and to examine its effect on patient outcomes. Nurse educators should design training courses and educational programs to enhance computer self-efficacy beliefs among nursing students.

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Busch, Tor. "Gender, Group Composition, Cooperation, and Self-Efficacy in Computer Studies." Journal of Educational Computing Research 15, no. 2 (September 1996): 125–35. http://dx.doi.org/10.2190/kqjl-rtw1-vvuy-bhlg.

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This study aimed to investigate whether gender, group composition, or self-efficacy in computing has any impact on cooperation, giving or getting task-related help, and level of activity in student groups. The groups were established during a computer course among 150 college students in business administration. According to our results, students with low self-efficacy in computing, and students in groups with a majority of females, cooperated more than any other categories in their work with computers. Furthermore, students with high self-efficacy in computing, and students with a high degree of previous computer experience, offered more task-related help to other students than did the rest. In terms of gender, the level of activity was evaluated as highest in majority-female or majority-male groups. Moreover, female students had significantly lower self-efficacy in computing, less previous computer experience, and they had received less previous encouragement to work with computers. Finally, female students were receiving more task-related help, while at the same time giving less task-related help than male students.

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17

Ryu, Seoungho. "The Meaning of Self-efficacy and ‘Computer Game Self-efficacy’ in Modern Society." Society and Theory 11 (November 30, 2007): 169. http://dx.doi.org/10.17209/st.2007.11.11.169.

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18

Achim, Nurˋain, and Arraqib Al Kassim. "Computer Usage: The Impact of Computer Anxiety and Computer Self-efficacy." Procedia - Social and Behavioral Sciences 172 (January 2015): 701–8. http://dx.doi.org/10.1016/j.sbspro.2015.01.422.

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19

Semerci, Ali, and Hafize Keser. "Examining the instructors computer self-efficacy perceptions." World Journal on Educational Technology 7, no. 3 (December 30, 2015): 186. http://dx.doi.org/10.18844/wjet.v7i3.206.

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In this study, instructors’ computer self-efficacy perceptions are examined in terms of variables such as age, gender, academic status, teaching experience, computer literacy and computer skills. The study is a descriptive one in which relational scanning method has been utilized. The study has been carried out within the framework of the data obtained from 172 police instructors who work in 28 Police Vocational Schools of Higher Education across the country. The data were collected by using a survey and “The Computer Self-efficacy Perception Scale”. A significant difference has been found between the self-efficacy perceptions of the instructors and their computer skills and computer training that they received. In addition to this, it has been seen that there was not any significant difference between self-efficacy perceptions of instructors and age, gender, position status, teaching experience and computer literacy. This is the first study carried out for determining the self-efficacy perceptions of police instructors. The results should be considered during the police trainer development trainings. Keywords: self-efficacy, self-efficacy perceptions, training, education, police education.

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20

Semerci, Ali, and Hafize Keser. "Examining the instructors computer self-efficacy perceptions." World Journal on Educational Technology 7, no. 3 (December 30, 2015): 186. http://dx.doi.org/10.18844/wjet.v7i3.162.

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<p>In this study, instructors’ computer self-efficacy perceptions are examined in terms of variables such as age, gender, academic status, teaching experience, computer literacy and computer skills. The study is a descriptive one in which relational scanning method has been utilized. The study has been carried out within the framework of the data obtained from 172 police instructors who work in 28 Police Vocational Schools of Higher Education across the country. The data were collected by using a survey and “The Computer Self-efficacy Perception Scale”. A significant difference has been found between the self-efficacy perceptions of the instructors and their computer skills and computer training that they received. In addition to this, it has been seen that there was not any significant difference between self-efficacy perceptions of instructors and age, gender, position status, teaching experience and computer literacy. This is the first study carried out for determining the self-efficacy perceptions of police instructors. The results should be considered during the police trainer development trainings. </p><p> </p><p>Keywords: self-efficacy, self-efficacy perceptions, training, education, police education.</p><p> </p>

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21

Sukoco, Heru. "Mathematics Self-Efficacy Scale Based on Computer." Indonesian Journal of Mathematics Education 2, no. 1 (April 30, 2019): 43. http://dx.doi.org/10.31002/ijome.v2i1.1248.

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The results of the PISA (Program for International Student Assessment) survey in 2012-2015 on the achievement of mathematical competencies of Indonesian students showed a significant increase, but the overall achievement was still below the average of the countries belonging to the Organization for Economic Co-operation and Development (OECD). Furthermore, the results of the Trends in International Mathematics and Science Study (TIMSS) report showed that many students like and feel good about mathematics, but their confidence in their mathematical abilities was quite low. Many studies reveal the close association of Mathematics Self-Efficacy (MSE) with the performance/achievement of students' mathematical competencies. In 2015, the PISA survey was done using computerization except in 15 countries, one of them was in Indonesia. Therefore, the results of this study are to produce the first computer-based MSE scale developed in Indonesia.

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22

Nap, Henk Herman, H. Paul De Greef, and Don G. Bouwhuis. "Self-efficacy support in senior computer interaction." International Journal of Cognitive Performance Support 1, no. 1 (2013): 27. http://dx.doi.org/10.1504/ijcps.2013.053553.

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23

Deniz, Beste Cevik KILIC. "Music teachers computer anxiety and self-efficacy." Educational Research and Reviews 10, no. 11 (June 10, 2015): 1547–59. http://dx.doi.org/10.5897/err2015.2235.

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Sheng, YiHua P., Michael Pearson, and Leon Crosby. "Organizational Culture and Employees' Computer Self-Efficacy." Information Resources Management Journal 16, no. 3 (July 2003): 42–58. http://dx.doi.org/10.4018/irmj.2003070103.

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25

Lin, ShinYi, and Richard C. Overbaugh. "Computer-mediated discussion, self-efficacy and gender." British Journal of Educational Technology 40, no. 6 (November 2009): 999–1013. http://dx.doi.org/10.1111/j.1467-8535.2008.00889.x.

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26

Gripenberg, Pernilla. "Computer self‐efficacy in the information society." Information Technology & People 24, no. 3 (August 23, 2011): 303–31. http://dx.doi.org/10.1108/09593841111158392.

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27

Moos, Daniel C., and Roger Azevedo. "Learning With Computer-Based Learning Environments: A Literature Review of Computer Self-Efficacy." Review of Educational Research 79, no. 2 (June 2009): 576–600. http://dx.doi.org/10.3102/0034654308326083.

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Although computer-based learning environments (CBLEs) are becoming more prevalent in the classroom, empirical research has demonstrated that some students have difficulty learning with these environments. The motivation construct of computer-self efficacy plays an integral role in learning with CBLEs. This literature review synthesizes research that has empirically examined factors related to computer self-efficacy and the relationship between computer self-efficacy, learning outcomes, and learning processes with CBLEs. Results indicate that behavioral and psychological factors are positively related to computer self-efficacy. Students who receive behavioral modeling report significantly higher computer self-efficacy than do students who receive the more traditional instruction-based method when learning with CBLEs. Computer self-efficacy is related both to learning outcomes and to learning processes with CBLEs. This review also offers theoretical and methodological issues for future research in the area of computer self-efficacy.

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28

Shiue, Ya-Ming. "The Effects of Cognitive Learning Style and Prior Computer Experience on Taiwanese College Students' Computer Self-Efficacy in Computer Literacy Courses." Journal of Educational Technology Systems 31, no. 4 (June 2003): 393–409. http://dx.doi.org/10.2190/8qcn-63qg-dppq-5l7n.

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This study examines the influences of learners' characteristics on the development of computer self-efficacy using the GOMS (goal, operators, methods, and selection rules) model. Students' learning style, prior computer course experience, and computer self-efficacy were measured in the pretest, and computer self-efficacy was measured in the posttest to examine the development of computer self-efficacy during the fall of 2001 in a computer introductory course in a non-traditional Taiwanese University. Results indicate that there were statistically significant effects of learning style and prior computer course experiences on computer self-efficacy. The follow-up analysis revealed that the Converger learning style students (abstract learners) outperformed other learning style students (concrete learners). Furthermore, a positive linear relationship was found between computer self-efficacy and the number of prior computer courses taken, which indicates that the more courses students had taken, the higher the level of computer self-efficacy the students developed. The result did support the GOMS model as a useful theoretical model to improve the computer literacy instruction.

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Kim, Sunha, Mido Chang, Namok Choi, Jeehyun Park, and Heejung Kim. "The Direct and Indirect Effects of Computer Uses on Student Success in Math." International Journal of Cyber Behavior, Psychology and Learning 6, no. 3 (July 2016): 48–64. http://dx.doi.org/10.4018/ijcbpl.2016070104.

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To promote the academic success of middle school students, the authors examined the effects of computer uses on mathematics self-efficacy and mathematics performance of students, paying focused attention to immigrant students. They analysed the effects of computer use for schoolwork and gaming of middle school students applying a Structural Equation Modeling (SEM) to Trends in International Mathematics and Science Study (TIMSS) USA. The results showed that when students frequently used computer for schoolwork, they revealed high mathematics self-efficacy, which in turn led to high mathematics performance. On the other hand, the students that used computer for gaming frequently indicated low mathematics self-efficacy and mathematics performance. The authors' study results highlight the importance of guiding students to use computers properly, which is directly and indirectly associated with students' self-efficacy and performance in mathematics.

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Stockham, Melia K., and Mary Lind. "Digital Natives, Work Values, and Computer Self Efficacy." International Journal of Strategic Information Technology and Applications 9, no. 1 (January 2018): 1–22. http://dx.doi.org/10.4018/ijsita.2018010101.

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Generational differences in the workplace have been the subject of much discussion for the field of management. In many cases, the technology savvy of the youngest generation in the workplace, coined “digital natives,” is the motivation behind organizational decision-making. However, little empirical evidence exists as to whether it is their comfort and confidence using technology that truly sets digital natives apart from their generational predecessors known as “digital immigrants.” Work values, those areas of importance that enhance satisfaction and engagement in the workplace, are rooted in the belief that there is a structure to basic human values. This study connects the theories of work values, generational differences, and computer self-efficacy to investigate if computer self-efficacy interacts with digital native status to influence differences in work values.

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Downey, James P., and R. Kelly Rainer Jr. "Accurately Determining Self-Efficacy for Computer Application Domains." Journal of Organizational and End User Computing 21, no. 4 (October 2009): 21–40. http://dx.doi.org/10.4018/joeuc.2009062602.

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Computer self-efficacy (CSE) has been used in many studies as a predictor of individual competence or performance, usage behavior, and a variety of attitudes. Although CSE has been effective in explaining a variety of human computing interactions, there have been a number of studies in which the relationship was weak or nonexistent. One reason for such findings concerns how CSE is operationalized. Many (if not most) leading cognitive theorists (Bandura, 1997; Gist & Mitchell, 1992; Marakas et al., 1998) rather emphatically state that actual tasks must be used to most accurately determine an individual’s perception of ability (i.e., self-efficacy) for some task or domain. They suggest that using tasks, of incremental difficulty level within the intended domain, most accurately presents an individual’s self-efficacy and leads to stronger relationships with outcomes such as competence or performance. Yet one of the most utilized measures of self-efficacy uses levels of assistance (GCSE of Compeau & Higgins, 1995a), and not tasks. This study examines which methodology provides a stronger relationship with competence and performance. Using a sample of 610, self-efficacy (using both methodologies) and competence or performance were measured for six different application domains. Results indicate that for domains in which individual’s had lower ability, actual tasks were superior. For domains of higher ability, however, levels of assistance yielded stronger relationships. This study clarifies the relationship between self-efficacy and performance as an individual moves from low to high ability as a result of training or experience. Implications and suggestions for further study are included.

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Johnson, Richard D., Yuzhu Li, and James H. Dulebohn. "Unsuccessful Performance and Future Computer Self-Efficacy Estimations." Journal of Organizational and End User Computing 28, no. 1 (January 2016): 1–14. http://dx.doi.org/10.4018/joeuc.2016010101.

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Using data from 100 individuals, this study examined the role of performance attributions (stability and locus of causality) and computer self-efficacy (CSE) for spreadsheets and databases in the training context. The results show that both self-efficacy and attributions (locus of causality and stability) for unsuccessful performance on one software package affected future efficacy estimations for both the same software package (spreadsheet) as well as for a related software package (database). These findings extend previous research by illustrating that through the generality of CSE estimations, users' performance on one software package are related to self-efficacy estimations on a different, distally similar, software application. This suggests that trainers and managers cannot overlook the importance of self-efficacy generality in the design of technology training initiatives. Early, unsuccessful experiences for those with limited technology experience can make it more challenging to adapt to, and learn to use, new technologies.

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ÜNLÜ, Hüseyin, Emin SUE, and Murat ERDOGDU. "COMPUTER SELF-EFFICACY OF PROSPECTIVE PHYSICAL EDUCATION TEACHERS." Journal on Efficiency and Responsibility in Education and Science 7, no. 2 (July 31, 2014): 33–36. http://dx.doi.org/10.7160/eriesj.2014.070202.

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34

Deng, Xiaodong, William Doll, and Dothang Truong. "Computer self-efficacy in an ongoing use context." Behaviour & Information Technology 23, no. 6 (November 2004): 395–412. http://dx.doi.org/10.1080/01449290410001723454.

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35

Wang, Jiahao. "From Self-efficacy to Human-Computer Interaction Design." Journal of Physics: Conference Series 1168 (February 2019): 032060. http://dx.doi.org/10.1088/1742-6596/1168/3/032060.

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36

Strong, Diane M., Mark T. Dishaw, and D. Brent Bandy. "Extending task technology fit with computer self-efficacy." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 37, no. 2-3 (September 19, 2006): 96–107. http://dx.doi.org/10.1145/1161345.1161358.

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37

Saleh, Hanadi Kassem. "Computer self-efficacy of university faculty in Lebanon." Educational Technology Research and Development 56, no. 2 (December 19, 2007): 229–40. http://dx.doi.org/10.1007/s11423-007-9084-z.

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38

Igbaria, M. "The effects of self-efficacy on computer usage." Omega 23, no. 6 (December 1995): 587–605. http://dx.doi.org/10.1016/0305-0483(95)00035-6.

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39

Widiyasari, Rena, and Bety Nur Achadiyah. "Computer Anxiety, Computer Self-Efficacy dan Perceived Usefulness oleh Pelaku UMKM." Jurnal Akuntansi Aktual 5, no. 3 (February 28, 2019): 203–14. http://dx.doi.org/10.17977/um004v5i32019p203.

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40

George Saadé, Raafat, and Dennis Kira. "Computer Anxiety in E-Learning: The Effect of Computer Self-Efficacy." Journal of Information Technology Education: Research 8 (2009): 177–91. http://dx.doi.org/10.28945/166.

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41

Hasan, Bassam. "The influence of specific computer experiences on computer self-efficacy beliefs." Computers in Human Behavior 19, no. 4 (July 2003): 443–50. http://dx.doi.org/10.1016/s0747-5632(02)00079-1.

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Tsai, Meng-Jung, Ching-Yeh Wang, and Po-Fen Hsu. "Developing the Computer Programming Self-Efficacy Scale for Computer Literacy Education." Journal of Educational Computing Research 56, no. 8 (January 16, 2018): 1345–60. http://dx.doi.org/10.1177/0735633117746747.

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Computer programming has been gradually emphasized in recent computer literacy education and regarded as a requirement for all middle school students in some countries. To understand young students’ perceptions about their own learning in computer programming, this study aimed to develop an instrument, Computer Programming Self-Efficacy Scale (CPSES), for all students above middle school levels. Based on Berland and Lee’s computational thinking framework, this study developed the CPSES items at a literacy level and finally the instrument included the five subscales: Logical Thinking, Algorithm, Debug, Control, and Cooperation. An exploratory factor analysis and reliability tests were conducted in this study. The reliability alpha was .96 for the overall scale, and ranged from .84 to .96 for the subscales. This study also confirmed the positive correlation between computer programming experience and computer programming self-efficacy. In addition, for low- and middle-experienced learners, significant gender differences were found in two subscales: Algorithm and Debug. The CPSES can be applied as an evaluation tool in computer education, robotics education, as well as integrated STEM or STEAM education in which computer programming was regarded as a part of computer literacy.

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Ng, Ying Chu. "Levels of computer self-efficacy, computer use and earnings in China." Economics Letters 90, no. 3 (March 2006): 427–32. http://dx.doi.org/10.1016/j.econlet.2005.10.004.

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Jan, Shazia K. "The Relationships Between Academic Self-Efficacy, Computer Self-Efficacy, Prior Experience, and Satisfaction With Online Learning." American Journal of Distance Education 29, no. 1 (January 2, 2015): 30–40. http://dx.doi.org/10.1080/08923647.2015.994366.

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Downey, James P., and Mark McMurtrey. "Introducing task-based general computer self-efficacy: An empirical comparison of three general self-efficacy instruments." Interacting with Computers 19, no. 3 (May 2007): 382–96. http://dx.doi.org/10.1016/j.intcom.2006.11.001.

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Spence, Dianna J., and Ellen L. Usher. "Engagement with Mathematics Courseware in Traditional and Online Remedial Learning Environments: Relationship to Self-Efficacy and Achievement." Journal of Educational Computing Research 37, no. 3 (October 2007): 267–88. http://dx.doi.org/10.2190/ec.37.3.c.

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This research applied Bandura's (1986) social cognitive theory to examine engagement with courseware in traditional and online remedial mathematics learning environments. The study investigated the relationship of courseware engagement to age, computer self-efficacy, computer playfulness, and self-efficacy for self-regulated mathematics learning. The study also analyzed mathematics achievement in terms of engagement, age, gender, mathematics grade self-efficacy, and self-efficacy for self regulated mathematics learning. Participants were 88 students in a traditional environment and 76 students in an online environment. The two groups differed significantly in age, mathematics grade self-efficacy, computer self-efficacy, computer playfulness, courseware engagement, and mathematics achievement. When controlled for age, all significant differences found between the two groups persisted. When controlled for mathematics self-efficacy, the groups no longer differed significantly in mathematics achievement, but all other significant differences remained. Computer self-efficacy and computer playfulness each had a significant association with courseware engagement in the online environment, but not in the traditional environment. Regression models revealed that course setting, age, and self-efficacy for self-regulation jointly predicted engagement with courseware. Both mathematics grade self-efficacy and age jointly predicted achievement. These findings support the views that mathematics self-efficacy is among the most significant predictors of mathematics achievement; computer self-efficacy and computer playfulness are associated with courseware engagement; and self-regulation is an important component of e-learning. Implications for researchers and educators are discussed.

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Noor Hayati Mohamed Zawawi, Siti, and Rashidah Abdul Rahman. "An Empirical Study On Computer Literacy Among Graduating Students In The Bachelor Of Accountancy Programs Of Malysian Public Higher Institutions." Journal of Financial Reporting and Accounting 1, no. 1 (January 6, 2003): 81–104. http://dx.doi.org/10.1108/19852510380000669.

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Information age in the 21st century is witnessing the dramatic increase in computer‐related jobs. Many employers are now recruiting candidates who have knowledge and experience in information technology or those who are computer literate. Therefore, potential candidates should m aster at least some level of computer literacy. Thus, the m ain objective of this study is to analyse the level of computer literacy among graduating Bachelor of Accountancy students in Malaysian public higher institutions. Based on the findings, computer literacy can be group into basic, advanced, self‐efficacy and technical level. Evidence shows that, graduating accountancy students are computer literate at the basic, advanced and self‐efficacy level. Significant difference is also found in the students’ perceptions on Accounting Information System (AIS) suggesting that the educators should give due attention to AIS courses, particularly in enhancing students’ computer literacy level. There are also evidence that the level of basic and advanced computer literacy of male students are significantly higher than female students; those who woned computers are significantly more computer literate in terms of basic and advanced computer literacy than those who did not own computers; and finally, previous computer experience is not generally related to computer achievement, except for self‐efficacy computer literacy level.

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Thinakaran, Rajermani, Rosmah Ali, and Wan Nor Al-Ashekin Wan Husin. "A Case Study of Undergraduate Students Computer Self-Efficacy from Rural Areas." International Journal of Engineering & Technology 7, no. 3.20 (September 1, 2018): 270. http://dx.doi.org/10.14419/ijet.v7i3.20.19164.

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In students’ learning process, self-efficacy plays an important part. In Malaysia, there are a number of researches on students’ self-efficacy. However, none of the studies focuses on undergraduate students’ computer self-efficacy from the rural areas. This article presents a quantitative research on undergraduate students’ perceived computer self-efficacy. A total of 128 first semester undergraduate students participated in the survey, employing a 27-item questionnaire measuring computer self-efficacy. The items were pilot-tested before being administered to the respondents. Outcome of the research show that computer self-efficacy level is high for basic and advanced skills. The result of the research shows that rural community has accepted the ICT as part of their lifestyle.

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Alqusyairi, M. Syaif Amrullah, Suherman Suherman, and Farida Farida. "HUBUNGAN LITERASI LINGKUNGAN DAN LITERASI MATEMATIS TERHADAP KEMAMPUAN COMPUTER SELF EFFICACY." Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 2, no. 2 (August 30, 2021): 153–65. http://dx.doi.org/10.46306/lb.v2i2.62.

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The purpose of this study is to determine the relationship between environmental literacy and mathematical literacy on the ability of students' Computer Self Efficacy. This quantitative study uses a Correlational Design research design. The data collection techniques in this study were environmental literacy tests, mathematical literacy tests, and a Computer Self Efficacy ability questionnaire. The data analysis technique used is Simple and Multiple Linear Regression. Based on the results obtained from research and calculations of Simple and Multiple Linear Regression analysis, it was concluded that there is a relationship between environmental literacy and the ability of Computer Self Efficacy of students, there is a relationship between mathematical literacy and the ability of Computer Self Efficacy of students, and there is a relationship between environmental literacy and mathematical literacy with the ability of students' Computer Self Efficacy. The magnitude of the influence of environmental literacy and mathematical literacy on the ability of Computer Self Efficacy is 39.2%.

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Awofala, Adeneye Olarewaju, Oladiran Stephen Olabiyi, Awoyemi Abayomi Awofala, Abayomi A. Arigbabu, Alfred O. Fatade, and Uchenna Nkiruka Udeani. "Attitudes toward Computer, Computer Anxiety and Gender as determinants of Pre-service Science, Technology and Mathematics Teachers’ Computer Self-efficacy." Digital Education Review, no. 36 (December 31, 2019): 51–67. http://dx.doi.org/10.1344/der.2019.36.51-67.

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The study investigated attitudes towards computer and computer anxiety as determinants of computer self-efficacy among 2100 pre-service science, technology and mathematics (STM) teachers from the University of Lagos of Nigeria using the quantitative research method within the blueprint of the descriptive survey design. Data collected were analysed using the descriptive statistics of percentages, mean, and standard deviation and inferential statistics of independent samples t-test, Pearson product moment correlation coefficient and multiple regression analysis. Finding revealed significant correlations between computer attitudes, computer anxiety and computer self-efficacy. Gender differences in attitude toward computer, computer self-efficacy and computer anxiety among pre-service STM teachers were significant. Affective component, perceived control component, and perceived usefulness component, behavioural intention component, gender, and computer anxiety made statistically significant contributions to the variance in pre-service STM teachers’ computer self-efficacy. The study recommended among others that academic institutions should pay more attention to this computer anxiety and adopt proper ways of reducing the computer anxiety, so that positive e-learning experiences can be created for pre-service STM teachers.

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