A Study on General and Specific Programming Self-Efficacy with Antecedents from the Social Cognitive Theory

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1 A Study on General and Specific Programming Self-Efficacy with Antecedents from the Social Cognitive Theory 1 Jungwoo Lee, 2 Jun-Gi Park, 3 Yoosun Hwang, 1 Yonsei University, jlee@yonsei.ac.kr 2 Yonsei University, Warren.pak@gmail.com, 3 Joongbu University, yshwang@joongbu.ac.kr Abstract Programming skill is becoming more and more important as the information revolution progresses into a knowledge-based smart society. Concept of self-efficacy, which has been known to be critical in raising general competences of work and life, can easily be extended into the learning of programming languages. A survey was conducted to study the mechanisms related to general and specific programming self-efficacy and their antecedents: mastery experience, vicarious learning, verbal persuasion and computer affect. Findings confirm the strong path from general programming selfefficacy towards specific programming self-efficacy indicating the generality of self-efficacy applicable across domains and disciplines. Among four antecedents of programming self-efficacy generally identified in the social cognitive theory literature, mastery experience and vicarious learning are found to be important in reinforcing the programming self-efficacy while verbal persuasion and computer affect seems to be not so strong in forming the self-efficacy towards programming. Further implications and limitations are discussed at the end. Keywords: Social Cognitive Theory, Self-Efficacy, General Programming Self-Efficacy, Specific Programming Self-Efficacy, Mastery Experience, Vicarious Learning, Verbal Persuasion, Computer Affect 1. Introduction As computing and networking technologies becomes more and more pervasive in our life, the work that we are doing for living also becomes more and more attached to information and communication technologies. This leads to the increasing number of knowledge-based jobs while the knowledge component of individual s work is also increasing [1]. With the proliferation of technologies around, people are also becoming more technology savvy compared to previous generations [2]. In this regard, now, people in domains other than computer science and engineering are learning computers and programming while demand for professionally skilled programmers with inept ability to understand businesses and technologies is continuously increasing[3]. Review of literature on programming reveals continuous call for more rigorous empirical work that is theory driven in order to further our understanding of how to produce and train good programmers who may function as consultants between technology and businesses [2, 4-6]. Understanding factors that influence the learning outcome in programming has been one of the goals of computer education. One of the key concepts in effective learning at the individual level is the self-efficacy proposed in the social cognitive theory - a belief in one s capability of performing specific tasks. Social cognitive theory argues that self-efficacy influence an individual's outcome expectations [7]. The same theory also predicts that self-efficacy perceptions will influence individuals' actual ability to perform the behavior. It is now adopted and used widely across academic disciplines. Various empirical studies in a variety of domains have validated the model of self-efficacy. Also, as a key construct in the social cognitive theory, self-efficacy has been widely studied in education area including computer training. The computer self-efficacy is found to be an important determinant of task motivated behavior and subsequent performance in using computers [8-11]. This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2012S1A3A ). Corresponding author Journal of Next Generation Information Technology(JNIT) Volume 4, Number 8, October

2 As computer programming at large is becoming a norm rather than an exception for nowadays knowledge workers, self-efficacy would become more and more important beyond simple usage efficacy of computers (computer self-efficacy) but also more knowledge based activities such as indepth programming (programming self-efficacy). Here, we intend to investigate the self-efficacies related to computer programming and antecedents influencing the level of these programming selfefficacies. Programming self-efficacy is measured in two dimensions general and specific while the impacts of four critical antecedents of self-efficacy - are tested using data collected in a survey of students in programming classes. 2. Programming self-efficacy Bandura [7] defines self-efficacy as people s judgments of their capabilities to organize and execute courses of action required to attain designated types of performance (p.154). Although selfefficacy would not fully determine the actual behavior but it is an effective cognitive path towards explaining human behaviors. Individuals with weaker self-efficacy will be most likely to give up in a difficult situation compared to ones with stronger self-efficacy and try harder to overcome these challenges. General and Specific Self-Efficacy Self-efficacy is multifaceted and can be measured at different levels [12]. It is noted that task specific self-efficacy should be different from general self-efficacy, and there might be several middlerange efficacies in between as well as domain specific efficacies [13, 14]. In some sense, self-efficacy scales can be on a scale of continuum in terms of coverage or scope. For example, computer selfefficacy is a specific scale compared to general self-efficacy scale, but it is a general scale compared to spreadsheet self-efficacy. Another example would be the academic self-efficacy [15] which stands in between general self-efficacy and specific subject self-efficacy such as math. Accordingly, it is emphasized that self-efficacy should be regarded as having highly domain specific components rather than generalizable across domains, especially when measuring [7]. In case of computer self-efficacy, there have been numerous studies which analyze the role of specific selfefficacy in narrowly confined specific task areas like windows self-efficacy, lotus self-efficacy, excel self-efficacy [16, 17], as well as ones which produce more general measures of computer self-efficacy and use for general purpose [18-21]. Consistent with recent literature concerning multifaceted nature of self-efficacy, the general programming self-efficacy (GPSE) is distinguished from the languagespecific programming self-efficacy (SPSE) in this study. Relationship between general self-efficacy and specific self-efficacy Despite emphasis on domain specific characteristics of self-efficacy, attempts have been concerted to construct general self-efficacy not tied to situations or contexts [22, 23]. It is posited and tested that initial general computer self-efficacy beliefs will strongly predict subsequent specific computer selfefficacy beliefs [16]. Perceived self-efficacy would influence the judgment of one s capability of using computer and the task performances using computer technologies, which relates to computer self-efficacy [17]. In other words, in a novel context, general self-efficacy may have an impact on situational behavior. Bandura [24] also posited that the generality is the relative degree of specificity in self-efficacy. While GPSE is a generalized individual trait, SPSE refers to individual s feeling of self-efficacy relative to a specific programming environment [12, 16]. Literature on general self-efficacy suggested that initial general self-efficacy beliefs will strongly predict subsequent specific self-efficacy beliefs [16, 25]. Prior research on computer self-efficacy also points out that relationship between GPSE and SPSE is developed through training [19]. Thus, within a training context, it can be hypothesized that pre-training GPSE would be a predictor of SPSE throughout the training. This causal relationship illustrates the development of self-efficacy (i.e., SPSE) beliefs over time in specific domains via training, where individuals are progressively provided with greater opportunity for in-class experiences and practices with different languages and contexts and having opportunities to extend and apply their general self-efficacy (i.e., GPSE). 424

3 Antecedents of Self-Efficacy Since self-efficacy refers to one s psychological belief, to evaluate one s capability and to generate outcomes for the tasks would not always be going together. Efficacy beliefs might enhance one s optimistic view for the tasks, however, on the contrary, one s pessimistic perception might be caused depending on one s motivational processes; the goal, the situation, the emotional status [26]. Moreover, people perceive the different level of self-efficacy according to multiple task components and different ways of behaviors [27]. Therefore, the antecedents of self-efficacy can be taken into consideration to more specifically verify the influences of perceived self-efficacy on its relevant outcomes. According to Bandura [7, 24], there are four major sources or antecedents of self-efficacy expectations: mastery experience performance accomplishments, vicarious experience, verbal persuasion, and emotional arousal physiological states. The more dependable these sources, the greater are the changes in level of self-efficacy perception. These four sources are identified as the influencers in cognitive efficacy processing. Mastery Experience. Personal mastery experience of accomplishment is influential because it is coming from the personal and direct experiences. Successful performance of tasks raises efficacy expectations while failures, especially when repeated, lower the efficacy [4]. Impact of failure would be critical when it occur early in the course of events such as the first trial. After efficacy expectations are developed strong through successful repetitions, the impact of subsequent failures would become negligible [28]. Indeed, occasional failures later overcome by reinforced efforts may strengthen persistence if one finds that difficult obstacles can be mastered by trial and error. Vicarious Experience. Self-efficacy perception also comes not only from the directly experienced mastery but also from vicarious experience. Seeing and hearing that others perform successfully can generate efficacy expectations in observers in the sense that they can also perform successfully. People persuade themselves that if others can do it, they should be able to achieve similar level of performance [24]. Vicarious experience, relying on social inferences, is considered a less dependable source than direct and personal experience. Therefore, the efficacy induced by modeling others are weaker and vulnerable [29]. Intuitively, while one observes others perform a specific task, he/she either strengthens or weakens its self-efficacy during the course of the observation depending upon the perceived difficulties of the task and the performance of the observed. Vicarious experience of seeing others overcome difficulties by determined and concerted efforts would be more influencing than from observing facile performances by adept ones [30]. Observing gains by serious coping behavior not only minimizes the distress but demonstrates that even the most feared can succeed through perseverance [31]. Several prior studies in computer self-efficacy (Gist, 1986; Gist et al., 1989; Mitchell et al., 1994; Compeau and Higgins, 1995), also, have shown that observing someone else performing successfully raises the self-efficacy to perform. In other words, as one acquires knowledge of performing a task while watching others doing it, the self-efficacy towards a task might not only be increased by observing easiness of the tasks but can also be attributable to domain specific knowledge acquisition concerning the task [32]. Verbal Persuasion. People can be talked into specific behavior. In this regard, verbal persuasion is widely used in influencing other people s behavior. People can be led into believing that they can perform successfully what they have been feared or overwhelmed in the past, henceforth verbal persuasion may affect self-efficacy. Self-efficacy induced in this manner is also likely to be much weaker because it does not provide an experiential base [24]. Facing fear of failure, suggestive persuasion can be easily extinguished. Several researches attest to the limitation of verbal persuasion in instilling outcome expectations in people simply by telling them[31, 33, 34]. Placebo conditions designed as suggestions for raising expectations is known to produce little behavioral changes [35]. Whether it is the credibility problem or weakness of suggestion would be a researchable issue. Emotional Arousal. When it comes to performing a task, a positive mood can boost the personal belief in self-efficacy, while anxiety can undermine the efficacy. A certain level of emotional arousal can create a heightened feeling that can contribute to stronger self-efficacy that may lead to better 425

4 performances [36]. In other words, emotional reactions to situations may play a role influencing selfefficacy. Moods, emotional states, physical reactions, and stress levels can all impact how a person feels about their coping abilities in a specific situation [17]. A person who becomes extremely nervous before speaking in public may develop a weak sense of self-efficacy in these situations. Conversely, one may be feeling relaxed before a new situation and raise the level of self-efficacy. Physiological cues are the weakest influence of the four presented here. In this regard, Bandura [24] noted that the perception and interpretation is more important that the simple intensity of emotional reactions. Therefore, emotional arousal or physiological state is another source of information that can affect the perceived self-efficacy in coping with threatening situations. 3. Research model Through a carefully designed computer laboratory experiment and several proposed hypotheses, we will try to answer the following research question: Will mastery experience, vicarious learning, verbal persuasion and emotional affect influence an individual's general and specific programming selfefficacy? The context where we will conduct the empirical study is the learning of a graphical programming language, Visual Basic. Based on the proposal made by the original social cognitive theorists, four proposed antecedents are adopted, modified and included in our model for testing programming self-efficacy. Figure 1 depicts our model. On the basis of the foregoing discussion, this study rested the following five hypotheses. Figure 1. Research model Hypothesis 1: Individuals with positive programming experience will exhibit higher general programming self-efficacy. Hypothesis 2: Individuals with vicarious experience with programming will exhibit higher general programming self-efficacy Hypothesis 3: Individuals with verbal persuasion about programming will exhibit higher general programming self-efficacy Hypothesis 4: Individuals with high computer affection will exhibit higher general programming selfefficacy Hypothesis 5: Individuals with high general programming self-efficacy will demonstrate higher specific programming self-efficacy. 426

5 4. Method and procedure Sample The sample consisted of programming students registered for visual basic programming classes. Respondents completed self-reported questionnaire at the last week of the course. Measure was taken at the end of the course so that subjects can actually answer questions specifically related to what they have learned. A total of 279 responses were received among 335 registered students. Due to missing data, 250 responses were used in the analysis. Measures Table 1 lists the construct definitions of instruments and the relevant literature. In this study, items used to operationalize the constructs included in each investigated model were mainly adapted from previous studies and modified for use in the programming context. This study measure six constructs: mastery experience, vicarious learning, verbal persuasion, computer affection, general programming self-efficacy and specific programming self-efficacy. For each item, confidence level was asked to be expressed using a 5-point Likert-type scale: strongly disagree, disagree, neutral, agree, and strongly agree. Table 1. Constructs and measurement items Constructs Items References Mastery Experience I have always had a natural talent for computer programming I receive good grades in my computer programming classes [19, 37, 38] Vicarious Learning Verbal Persuasion Computer Affect GPSE SPSE 5. Analysis and results I was always viewed by others as a good programmer People I look up to (like parents, friends, or teachers) are good at using computers and programming I have close friends who are good at computer programming My friends have encouraged me to take computer and programming classes My teacher praised my computer skills in front of others I like working with computers Once I start working on the computer, I find it hard to stop I could complete a programming project... even if there was no one to tell me what to do as I go.. even if I had never used the programming language before... if I had only the language reference manuals for help Using Visual Basic language, I can... comprehend a long, complex multi form program... organize and design my program in a modular manner... mentally trace through the execution of a long, complex, multi-form program given to me... rewrite lengthy confusing portions of code to be more readable and clear [17, 39] [10, 38, 40] [41, 42] [12, 17, 43] [16, 21, 44] PLS analysis is employed for data analysis here because it can evaluate a theoretical structural model and a measurement model simultaneously (Chin et al., 2003). SmartPLS software was used in a two-stage approach of measurement and structural model testing. Measurement model 427

6 Assessment of the measurement model involves evaluations of reliability, convergent validity, and discriminant validity of the construct measures. Generally, if the factor loading of each measurement item onto its construct is more than 0.7, the measurement item is considered valid. Factor loadings of more than 0.7 for each measurement item indicate the convergent validity, as can be seen in Table 2, except VP 1. To evaluate discriminant validity, the average variance extracted (AVE) can be used. The square root of AVE for each construct should be greater than the correlations among the constructs in order for measures to be discriminantly valid. Table 3 shows the correlations among the constructs, and the values in the diagonal are the square roots of the AVE. Square-rooted AVEs were greater than all of the other correlation coefficients. Thus, it can be concluded that the measurement model demonstrated adequate discriminant validity. Reliability was examined using Cronbach s α and composite reliability. As indicated in Table 2, Cronbach s α exceeded for each construct. AVE indicates the amount of variance in the measurement items accounted for by the latent construct. It is generally more conservative than Cronbach s α and should be 0.5 or higher to establish the composite reliability of measures. As shown in Table 3, all of the AVE values are greater than Therefore, it can be concluded that the individual measurement items reliably measure the relevant constructs. Table 2. Factor loadings and reliability Construct Item Mean SD Loadings Cronbach's α CR Mastery Experience (ME) ME ME ME Vicarious Learning (VL) VL VL Verbal Persuasion (VP) VP VP Computer Affect (CA) CA CA General PSE GPSE GPSE GPSE Specific PSE SPSE SPSE SPSE SPSE Table 3. Discriminant validity AVE ME VL VP CA GPSE SPSE Mastery experience Vicarious learning Verbal persuasion Computer affect General PSE Specific PSE Structural model In the next stage, the proposed hypotheses were tested using a bootstrap significance test for inter-variable paths using PLS. Results of the PLS analysis are presented in Figure 2. The path coefficients presented are the standardized beta coefficients from the PLS analysis. 428

7 A Study on General and Specific Programming Self-Efficacy with Antecedents from the Social Cognitive Theory ns 2 2 ns Figure 2. Structural model results Confirming hypotheses 1 and 2, the mastery experience and vicarious learning seem to maintain a statistically significant and positive relation to GPSE (β = 0.193, p<0.01) and (β = 0.259, p<0.001), respectively. Interestingly, vicarious learning may have exhibited a bit higher coefficient towards the GPSE. This might be because the subjects are students who do not have field experience yet. As most of students are still under auspices of parents and teachers, vicarious learning is still more popular for them, than practical and direct experience. Also, paths from verbal persuasion and computer affection to GPSE is found to be statistically insignificant (β = , p>0.1, and β = 0.011, p>0.1). Therefore, H1 and H2 are supported as expected but H3 and H4 are not. Concerning H5, GPSE is found to have a significant positive relationship with SPSE (β = 0.402, p<0.001) as anticipated. Therefore, H5 is supported. Squared multiple correlations (R2) for endogenous constructs are presented in the figure within the boxes of dependent constructs. R2 measures the percent of variance explained by independent constructs in the model. Independent constructs GPSE is found to explain a substantial portion of the variance in dependent constructs. All four antecedents explained 11.6% of variances in GPSE while GPSE explains about 16.1% of variances in SPSE. 6. Discussion This study is designed to explicate an empirical mechanisms related to general and specific programming self-efficacy and their antecedents. Four antecedents of general programming selfefficacy were posited in the research model: mastery experience, vicarious learning, verbal persuasion and computer affect. Also, specific programming self-efficacy is hypothesized to be influenced by the different levels of general programming self-efficacy. Analysis of data collected from two hundred and fifty students of visual basic programming confirms that existence of strong path from general programming self-efficacy towards specific programming self-efficacy, suggesting training and education raising the level of generalized self-efficacy may influence and raise the level of specific programming self-efficacy. In other words, generalized training raising the general self-efficacy level would impact how people perform specific tasks successfully. Among four antecedents of programming self-efficacy proposed in the social cognitive theory literature, mastery and vicarious experiences are found to be statistically significant in reinforcing the programming self-efficacy while verbal persuasion and computer affect seems to be not so strong in forming the self-efficacy towards programming. Maybe, because the programming tasks are knowledge intensive tasks, experiences need to be directly personally experienced personally or observe, at least, close acquaintances to be successful in programming, while rather indirect measures, such as verbal persuasion and emotional around might not be influential as in other domains. For practice, to produce effective and efficient programmers, focus of training would better be on actual hands-on practicum expanding direct and indirect exposure to the language use. Additionally, it can be 429

8 inferred that different perceptions or affects would exist for distinct types of programming tasks. Thus, to define the characteristics of programming tasks can help to provide more detailed explanation for the relationship among antecedents, programming self-efficacy and task outcomes. This research has attempted to demonstrate the utility of self-efficacy in programming context. The concept of self-efficacy was borrowed from social psychology, to understand individual behavior in learning programming. As the findings of this study is limited by the cross-sectionality of the data collected, a longitudinal observation of changes in different self-efficacy while programmers grow and mature is needed to confirm the findings of this study and further our understanding of cognitive learning mechanisms in programming context. 7. References [1] T. L. Griffith, J. E. Sawyer, and M. A. Neale, "Virtualness and knowledge in teams: Managing the love triangle of organizaitons, individuals, and information technology," MIS Quarterly, vol. 27, pp , [2] M. Resnick, J. Maloney, A. Monroy-HernÁNdez, N. Rusk, E. Eastmond, K. Brennan, et al., "Scratch: Programming for All," Communications of the ACM, vol. 52, pp , [3] R. D. Johnson, G. M. Marakas, and J. W. Palmer, "Beliefs about the social roles and capabilities of computing technology: development of the computing technology continuum of perspective," Behaviour & Information Technology, vol. 27, pp , [4] S. Bergin, "Predicting introductory programming performance: A multi-institutional multivariate study," Computer Science Education, vol. 16, pp , [5] S. Talja, "The Social and Discursive Construction of Computing Skills," Journal of the American Society for Information Science & Technology, vol. 56, pp , [6] M.-T. Tsai and N.-C. Cheng, "Programmer perceptions of knowledge-sharing behavior under social cognitive theory," Expert Systems with Applications, vol. 37, pp , [7] A. Bandura, Social Foundations of Thought and Action. Englewood Cliffs, NJ: Prentice Hall, [8] S. Qin, T. Qiang, and W. Kanliang, "The Impact of Computer Self-Efficacy and Technology Dependence on Computer-Related Technostress: A Social Cognitive Theory Perspective," International Journal of Human-Computer Interaction, vol. 27, pp , [9] H. Holden and R. Rada, "Understanding the Influence of Perceived Usability and Technology Self-Efficacy on Teachers' Technology Acceptance," Journal of Research on Technology in Education, vol. 43, pp , [10] J.-Y. Lai, "How reward, computer self-efficacy, and perceived power security affect knowledge management systems success: An empirical investigation in high-tech companies," Journal of the American Society for Information Science & Technology, vol. 60, pp , [11] R. Karsten and D. Schmidt, "Business Student Computer Self-Efficacy: Ten Years Later," Journal of Information Systems Education, vol. 19, pp , [12] G. M. Marakas, M. Y. Yi, and R. D. Johnson, "The Multilevel and Multifaceted Character of Computer Self-Efficacy: Toward Clarification of the Construct and an Integrative Framework for Research," Information Systems Research, vol. 9, pp , [13] K. M. Lee, "MUD and Self Efficacy," Educational Media International, vol. 37, pp , [14] M. Gist and T. Mitchell, "Self-Efficacy: A Theoretical Analysis of Its Determinants and Malleability," Academy of Management Review, vol. 17, p. 183, [15] M. Bong and E. M. Skaalvik, "Academic Self-Concept and Self-Efficacy: How Different Are They Really?," Educational Psychology Review, vol. 15, pp. 1-40, [16] R. Agarwal, V. Sambamurthy, and R. M. Stair, "Research Report: The Evolving Relationship Between General and Specific Computer Self-Efficacy--An Empirical Assessment," Information Systems Research, vol. 11, p. 418, [17] D. R. Compeau and C. A. Higgins, "Computer Self-Efficacy: Development of a Measure and Initial Test," MIS Quarterly, vol. 19, pp , [18] J. Lee, "Discriminant analysis of technology adoption behavior: A case of Internet technologies in small businesses," Journal of Computer Information Systems, vol. 44, pp ,

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