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Does Self-Efficacy Correlate with Positive Emotion and Academic Performance in Collaborative Learning?
Abstract
This full research paper studies the correlation of self-efficacy in computer science as well as learning and social skills with students' academic performance and their emotions in collaborative learning environments. Self-efficacy is an essential part of social cognitive theory and provides the foundation for analyzing human thoughts, motivations, and actions. Studies show that students' successful performance and accomplishment are directly affected by the level of self-efficacy. Therefore, analyzing self-efficacy in engineering education is important since it can impact the learning process in academic settings as well as provide a metric to track for improvement. Social cognitive theories also emphasize that students' interaction with each other affects their learning process and how they perform in educational settings. In previous work [5], we analyzed students' conversations in low-stake teams in an introductory programming course (CS1) and observed a strong positive correlation between students' positive emotions while interacting with each other with their performance in the course. In this study, we focus on the correlation of self-efficacy with learner's emotion and performance. We measure students' self-efficacy with a standard instrument called "Student Attitudes Toward STEM (S-STEM) Survey". For this purpose, we asked the participants to self-report on a 5-point Likert-scaled survey including 20 questions. These 20 questions are grouped into 2 main categories of computer science and learning/social skills. Students' emotions were extracted from their speeches in teams by applying natural language processing (NLP) methods. The result of data analysis shows a statistically significant correlation between overall self-efficacy and performance in the course and positive emotions during the teamwork. We further investigate which category of self-efficacy questions most correlate with students' performance. The result shows self-efficacy in interpersonal skills and learning ability most impact students' performance.
... Such an advance would make a significant contribution in this field because positive feelings favor interaction, promoting collaboration and skills development. This has been pointed out in several studies, e.g., (Zhipeng et al., 2022) pointed out that to achieve satisfactory learning outcomes, students must maintain positive emotions during the CL process, and Dehbozorgi et al. (2021) said that there is a strong positive correlation between students' positive emotions while interacting with each other with their performance in the course. Sentiments, interaction, collaboration, and competencies are four elements connected, so it is necessary to analyze them together, and AI can support this task. ...
The diversity of topics in education makes it difficult for artificial intelligence (AI) to address them all in depth. Therefore, guiding to focus efforts on specific issues is essential. The analysis of competency development by fostering collaboration should be one of them because competencies are the way to validate that the educational exercise has been successful and because collaboration has proven to be one of the most effective strategies to improve performance outcomes. This systematic review analyzes the relationship between AI, competency development, and collaborative learning (CL). PRISMA methodology is used with data from the SCOPUS database. A total of 1,233 articles were found, and 30 passed the inclusion and exclusion criteria. The analysis of the selected articles identified three categories that deserve attention: the objects of study, the way of analyzing the results, and the types of AI that could be used. In this way, it has been possible to determine the relationship offered by the studies between skill development and CL and ideas about AI’s contributions to this field. Overall, however, the data from this systematic review suggest that, although AI has great potential to improve education, it should be approached with caution. More research is needed to fully understand its impact and how best to apply this technology in the classroom, minimizing its drawbacks, which may be relevant, and making truly effective and productive use of it.
... In recent years, with the rapid development of online tutoring platform, more and more college students have obtained freer online learning opportunities than traditional learning methods [1][2][3][4][5], but at the same time, college students have exposed problems such as lack of learning enthusiasm, insufficient learning continuity or learning confusion and increased psychological burden under the influence of negative emotion [6][7][8][9][10]. In order to avoid the above problems, whether from the perspective of learning or emotions, it is necessary to explore the influence of positive learning emotion on college students' classroom learning effect [11][12][13][14][15][16][17][18][19], so as to fully understand college students' online learning effect and emotional state, improve students' learning quality, and provide theoretical basis and ideas for improving teachers' teaching quality and making reasonable teaching decisions [20][21][22][23][24]. ...
Exploring the influence of positive learning emotion on college students' classroom learning effect facilitates fully understanding college students' online learning effect and emotional state, and is beneficial to improving students' learning quality and teachers' teaching quality. At present, few scholars have summative evaluation of students' classroom learning effect from the perspective of students' learning emotions and prove from the perspective of theory and practice that good emotional state is an important influencing factor to improve college students' classroom learning effect. Therefore, this article fully considers the positive learning emotion, and makes a research on the prediction of college students' classroom learning effect. Firstly, this article defines the behavior data of students in the online learning process based on learning emotions, and studies the correlation between college students' classroom learning behaviors based on Hawkes process. Then, based on the learning participation under the influence of different learning emotions, the online learning effect of students is quantified, and the prediction model of students' classroom learning is constructed by combining the learning behavior sequence analysis results represented by Hawkes process and the characteristic information of students themselves and courses. The experimental results verify the effectiveness of the model, and the significance test results confirm the positive effect of positive emotion on learning effect.
This full research paper studies affective states in students' verbal conversations in an introductory Computer Science class (CS1) as they work in teams and discuss course content. Research on the cognitive process suggests that social constructs are an essential part of the learning process [1]. This highlights the importance of teamwork in engineering education. Besides cognitive and social constructs, performance evaluation methods are key components of successful team experience. However, measuring students' individual performance in low-stake teams is a challenge since the main goal of these teams is social construction of knowledge rather than final artifact production. On the other hand, in low-stake teams the small contribution of teamwork to students' grade might cause students not to collaborate as expected. We study affective metrics of sentiment and subjectivity in collaborative conversations in low-stake teams to identify the correlation between students' affective states and their performance in CS1 course. The novelty of this research is its focus on students' verbal conversations in class and how to identify and operationalize affect as a metric that is related to individual performance. We record students' conversation during low-stake teamwork in multiple sessions throughout the semester. By applying Natural Language Processing (NLP) algorithms, sentiment classes and subjectivity scores are extracted from their speech. The result of this study shows a positive correlation between students' performance and their positive sentiment as well as the level of subjectivity in speech. The outcome of this research has the potential to serve as a performance predictor in earlier stages of the semester to provide timely feedback to students and enables instructors to make interventions that can lead to student success.
Efforts to retain underrepresented minority (URM) students in science, technology, engineering, and mathematics (STEM) have shown only limited success in higher education, due in part to a persistent achievement gap between students from historically underrepresented and well-represented backgrounds. To test the hypothesis that active learning disproportionately benefits URM students, we quantified the effects of traditional versus active learning on student academic performance, science self-efficacy, and sense of social belonging in a large (more than 250 students) introductory STEM course. A transition to active learning closed the gap in learning gains between non-URM and URM students and led to an increase in science self-efficacy for all students. Sense of social belonging also increased significantly with active learning, but only for non-URM students. Through structural equation modeling, we demonstrate that, for URM students, the increase in self-efficacy mediated the positive effect of active-learning pedagogy on two metrics of student performance. Our results add to a growing body of research that supports varied and inclusive teaching as one pathway to a diversified STEM workforce.
It is the role of the Higher Education Institutions (HEIs) to shape the values and character of the university students to prepare them in a life-changing professional career in the future. Knowledge and skills are significant aspects in every line of work but possessing the right attitude binds together all the positive thoughts leading towards the achievement of personal and professional growth. Several student programs and activities would address the needs of building the right marker of success in the quality of graduates that academic institutions yield year after year. This article provides several attitudes that would trigger positively the behavior of the students to become aware of the significant objectives of their college activities that would serve as their training ground leading towards their goal of achieving excellence. Looking forward to their future is the best way of honing their line of thinking and strengthening their feelings moving towards a commitment to quality.
Previous research in computer science education has demon- strated the importance of motivation for success in introduc- tory programming. Theoretical constructs from self-regulated learning theory (SRL), which integrates several different types of metacognitive processes, as well as motivational constructs, have proved to be important predictors of success in most academic disciplines. These individual components of self- regulated learning (e.g., self-efficacy, metacognitive strate- gies) interact in complex ways to influence students’ affec- tive states and behaviors, which in turn influence learning outcomes. These elements have been previously examined individually in novice programmers, but we do not have a comprehensive understanding of how SRL constructs inter- act to influence learning to program. This paper reports on a study that examined the interaction of self-efficacy, intrinsic and extrinsic goal orientations, and metacognitive strategies and their impact on student performance in a CS1 course. We also report on significant gender differences in the relationships between SRL constructs and learning out- comes. We found that student performance had the ex- pected motivational and SRL precursors, but the interac- tions between these constructs revealed some unexpected relationships. Furthermore, we found that females’ self- efficacy had a different connection to programming perfor- mance than that of their male peers. Further research on success in introductory programming should take account of the unique and complex relationship between SRL and student success, as well as gender differences in these rela- tionships that are specific to CS.
In a collaborative e-learning environment, students are usually involved in learning activities such as collaborative writing (e.g., an essay in Wiki form) and debates in the form of a chat or asynchronous forum. Capturing students' feelings and emotional states enclosed in such textual information is important, since sentiments can influence individual behavior and consequently group dynamics as well as individual and group performance. For this reason, emotion awareness becomes an important issue for both tutors and students themselves. Especially for tutors, if they have as accurate affective information as possible about the emotional state that every individual and group is encountered, they might be able to provide more effective feedback to the students that includes not only cognitive but also affective scaffold. A first important step to this endeavor is to develop an approach that enables labeling affective behavior and then presenting the emotional information to all the actors involved in the learning process.
Learning diaries are instruments through which students can reflect on their learning experience. Students' sentiments, emotions, opinions and attitudes are embedded in their learning diaries as part of the process of understanding their progress during the course and the self-awareness of their goals. Learning diaries are also a very informative feedback source for instructors regarding the students' emotional well-being. However the number of diaries created during a course can become a daunting task to be manually analyzed with care, particularly when the class is large. To tackle this problem, in this paper we present a functional system for analyzing and visualizing student emotions expressed in learning diaries. The system allows instructors to automatically extract emotions and the changes in these emotions throughout students' learning experience as expressed in their diaries. The emotions extracted by the system are based on Plutchik's eight emotion categories, and they are shown over the time period that the diaries were written. The potential impact and usefulness of our system are highlighted during our experiments with promising results for improving the communication between instructors and students and enhancing the learning experience.
L'auto-efficacité générale est mesurée grâce à une courte échelle composée de dix items. Son usage est très répandu et elle a été adaptée à plusieurs cultures. Cet article compare des versions qui ont été proposées à des étudiants: 430 Allemands, 952 Costariciens et 293 Chinois. lies validités internes sont respectivement de.84,.81 et.91. L'unidimensionnalité de l'échelle est réapparue dans tous les échantillons. L'équivalence items-modèle interlinguistique ne fut que modérément approuvé par des analyses factorielles de confirmation. On a trouvé des différences au niveau des moyennes des scores totaux entre les langues. De plus, une intéraction entre sexe et langue s'est manifestée. Des corrélations avec la dépression, l'anxiété' et l'optimisme ont fourni des éléments complémentaires en faveur de la validité de construction.
General self-efficacy is measured by a widely used parsimonious ten-item scale that was developed for use in several cultures. The present paper compares the verions that were examined in samples of 430 German, 959 Costa Rican, and 293 Chinese university students. The internal consistencies were.84,.81, and.91, respectively. The unidimensional nature of the scale was replicated in all samples. Multilingual item-pattern equivalence was only moderately supported by confirmatory factor analyses. Mean differences of sum scores between languages were found. Moreover, an interaction between gender and language emerged. Correlations with depression, anxiety, and optimism provided some further evidence for construct validity.
Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data
Spearman's rank correlation coefficient is a nonparametric (distribution-free) rank statistic proposed by Charles Spearman as a measure of the strength of an association between two variables. It is a measure of a monotone association that is used when the distribution of data makes Pearson's correlation coefficient undesirable or misleading. Spearman's coefficient is not a measure of the linear relationship between two variables, as some "statisticians" declare. It assesses how well an arbitrary monotonic function can describe a relationship between two variables, without making any assumptions about the frequency distribution of the variables. Unlike Pearson's product-moment correlation coefficient, it does not require the assumption that the relationship between the variables is linear, nor does it require the variables to be measured on interval scales; it can be used for variables measured at the ordinal level. The idea of the paper is to compare the values of Pearson's product-moment correlation coefficient and Spearman's rank correlation coefficient as well as their statistical significance for different sets of data (original - for Pearson's coefficient, and ranked data for Spearman's coefficient) describing regional indices of socio-economic development.
Bandura's self-efficacy theory, stemming from a social cognitive model of behavior, has been gaining empirical attention in the career literature. This paper reviews emerging findings applying self-efficacy theory to career-relevant behaviors; examines a number of conceptual and methodological issues arising from this work; and offers several directions for future research and theory on the career self-efficacy construct. Self-efficacy appears to offer promise in understanding certain career entry behaviors, such as college major choices and academic performance, though there has been little work relating self-efficacy to career development beyond college or exploring causal connections between self-efficacy and career behaviors. An expanded base for research on career self-efficacy is advocated.
Learning to program is a unique experience for each student, and it is not fully understood why one person in an introductory programming course learns to program better and more quickly than the next. Self-efficacy is an individual's judgment of his or her ability to perform a task within a specific domain [1]. A mental model is a person's internal (mental) representation of real world objects and systems [9]. Research has shown that high self-efficacy and a good mental model are important to knowledge acquisition and transfer. This research investigates the effects of students' self-efficacy and mental models of programming on learning to program. The results show that self-efficacy for programming is influenced by previous programming experience and increases as a student progresses through an introductory programming course. The results also show that the student's mental model of programming influences self-efficacy and that both the mental model and self-efficacy affect course performance.
Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for "getting the wrong answer".
Programming education currently begins at the elementary school age. In this paper we are exploring what affects the learning performance of young students in programming classes. We present the results collected during an eight-week experimental Scratch programming course run in elementary schools. We emphasize factors that have been found to affect learning performance in adult students, including self-efficacy and motivation, and measure how they affect students of this age group. We further explore the students' view of programming as a career path, and measure the effects of the course, their performance, and the stereotypes that they assume for computer scientists. We find that students' intrinsic and extrinsic motivation and previous programming experience are important factors, being strongly correlated with their self-efficacy and their inclination towards a CS career. For female students only, we also find CS career orientation to be strongly correlated with their self-efficacy.
Many college computer science majors have little or no pre-college computing experience. Previous work has shown that inexperienced students under-perform their experienced peers when placed in the same introductory courses, and are more likely to drop out of the CS program. However, not much is known about what, if any, differences may persist beyond the introductory sequence for students who remain in the program. We conducted a study across all levels of a CS program at a large public university in the United States to determine whether grade differences exist between students with and without pre-college experience, and if so, for what types of experiences. We find significant grade differences in courses at all levels of the program. We further find that students who took AP Computer Science receive significantly higher average grades---by up to a half grade---in nearly all courses we studied. Pre-college experience appears to have a weaker relationship with retention and with low-stakes assessment grades. We discuss the limitations of these findings and implications for high school and college level CS courses and programs.
The superior performance of students with prior exposure to programming has long been evident to faculty that teach introductory courses. In this paper we replicate previous studies that quantify the difference between students with and without previous programming experience, and we provide further focus on gender differences. Our research is based on an initial CS1 course that we divided into a section with students having previous programming experience (P) and two sections for students without (N). Both sections of CS1 were taught with the same curriculum and assessments. We find that the advantages of prior experience are substantial, with P students outscoring N students by more than 6% on exams and 10% on programming quizzes. However, the performance gap between P and N students narrows considerably by the end of the following CS2 course, with no significant difference in overall scores. Analyzing results by gender, our data shows that 22% of N students in CS1 are female versus only 12% of P students. However, the female students with prior exposure outperform their male peers in all areas. Another finding of our research is the confirmation of a significant difference in confidence between female and male students.
In this study, the effectiveness of training module on enhancing self-efficacy in teacher education students was investigated. Sixty-eight (68) teacher education students (M age = 22.74; SD = .57) participated in this study, 36 of whom were assigned to an experimental group and the other 32 were assigned to a control group. The training module on enhancing self-efficacy composed of 26 one-hour sessions was applied on experimental group. A pretest-posttest control group design was used in order to assess the effectiveness of the training module as well as to collect data. A General Self-efficacy Scale, a Social Self-efficacy Scale, and a Teacher Self-Efficacy Scale were used. The findings showed that this training module on enhancing social self-efficacy was effective on the teacher education students' general self-efficacy, social self-efficacy, and teacher self-efficacy beliefs.
Computing curricula are being developed for elementary school classrooms, yet research evidence is scant for learning trajectories that drive curricular decisions about what topics should be addressed at each grade level, at what depth, and in what order. This study presents learning trajectories based on an in-depth review of over 100 scholarly articles in computer science education research. We present three levels of results. First, we present the characteristics of the 600+ learning goals and their research context that affected the learning trajectory creation process. Second, we describe our first three learning trajectories (Sequence, Repetition, and Conditionals), and the relationship between the learning goals and the resulting trajectories. Finally, we discuss the ways in which assumptions about the context (mathematics) and language (e.g., Scratch) directly influenced the trajectories.
This study examined the relationship between computer self-efficacy and learning performance and investigated learning engagement as a mediator of this relationship. The theoretical background is a combination of the conservation of resources (COR) theory and a theoretically extended job demands–resources model (JD-R model) proposed by Crawford, LePine, and Rich (2010). A daily diary design was carried out with 121 late-middle- and old-aged job seekers attending 10 computer cram schools. Participants completed a baseline questionnaire, seven daily diary questionnaires, and seven daily end-of-class computer skills examinations over the course of 1 week (N = 121∗7 = 847 occasions). The results of multi-level analyses showed that 1) computer self-efficacy is positively related to learning performance, 2) computer self-efficacy is positively related to learning engagement, 3) learning engagement is positively related to learning performance, and 4) learning engagement fully mediates the relationship between computer self-efficacy and learning performance. The theoretical contributions, research limitations, implications for future research, and practical implications of this study are discussed.
This study examines student attitudes about graphs used in the macro- and microeconomic sections of the one-semester principles of economics course at a large southeastern state university. In addition, we investigate the relation between attitudes about graphs and student performance in the course, controlling for a variety of factors, such as SAT scores, college GPA, and a number of other student characteristics (e.g., sex, race, college major, and term standing). Employing a probit regression model, we also study the factors that predict student attitudes toward graphs. Our sample includes data collected from 663 students during the period 2000–2001.
Using an iterative design along with multiple methodological approaches and a large representative
sample, this study presents reliability, validity, and fairness evidence for two surveys measuring
student attitudes toward science, technology, engineering, and math (S-STEM) and interest in
STEM careers for (a) 4th- through 5th-grade students (Upper Elementary S-STEM) and (b) 6th through
12th-grade students (Middle/High S-STEM). Findings from exploratory factor analysis
(EFA) and confirmatory factor analysis (CFA) suggested the use of a four-factor structure to
measure student attitudes toward science, math, engineering/technology, and 21st century
skills. Subject matter experts and literature reviews provided evidence of content validity.
Reliability levels were high for both versions. Furthermore, both the Upper Elementary S-STEM
and Middle/High S-STEM Surveys demonstrated evidence of configural, metric, and scalar
invariance across grade levels, races/ethnicities, and genders. The findings support the validity
of interpretations and inferences made from scores on the instruments’ items and subscales.
In this review of 64 articles published since the year 2000, a strong association between self-efficacy and student learning outcomes was apparent. Self-efficacy is also related to other factors such as value, self-regulation and metacognition, locus of control, intrinsic motivation, and strategy learning use. The review revealed that university student self-efficacy is higher under certain conditions than others, and that it can be improved. Examples of teaching strategies that may be used to improve self-efficacy are outlined. In screening articles for inclusion in the review, several conflicting definitions of self-efficacy arose. Clarification on the meaning and scope of the self-efficacy term is provided. The interpretation of the results of some studies reviewed was limited by design or analysis issues. Suggestions for addressing these issues in future research and evaluation work is given.
This study investigates relationships between self-efficacy, self-regulated learning strategy use and academic performance. Participants were 96 undergraduate students working on projects with three subtasks (idea generation task, methodical task and data collection) in a blended learning environment. Task self-efficacy was measured with self-reports administered during each subtask. Learning strategies were assessed by counting each instance of strategy use as it occurred in peer-to-peer conversations typed into a computer software system. Results showed that for each subtask, learners with higher task self-efficacy had higher task performance. Those who used more learning strategies on each subtask also had higher performance. In turn, high performance was associated with high self-efficacy on subsequent subtasks. Surprisingly, results showed that task self-efficacy and learning strategy use were not significantly related during any subtask. Overall, results imply that task self-efficacy, learning strategy use and past performance are important predictors of task performance.
Research over the past fifty years into predictors of programming performance has yielded little improvement in the identification of at-risk students. This is possibly because research to date is based upon using static tests, which fail to reflect changes in a student's learning progress over time. In this paper, the effectiveness of 38 traditional predictors of programming performance are compared to 12 new data-driven predictors, that are based upon analyzing directly logged data, describing the programming behavior of students. Whilst few strong correlations were found between the traditional predictors and performance, an abundance of strong significant correlations based upon programming behavior were found. A model based upon two of these metrics (Watwin score and percentage of lab time spent resolving errors) could explain 56.3% of the variance in coursework results. The implication of this study is that a student's programming behavior is one of the strongest indicators of their performance, and future work should continue to explore such predictors in different teaching contexts.
We introduce the Progression of Early Computational Thinking (PECT) Model, a framework for understanding and assessing computational thinking in the primary grades (Grades 1 to 6). The model synthesizes measurable evidence from student work with broader, more abstract coding design patterns, which are then mapped onto computational thinking concepts.
We present the results of a pilot-test study of the PECT Model in order to demonstrate its potential efficacy in detecting both differences in computational thinking among students of various ages as well as any clear overall progressions in increasing computational sophistication. Results of this sort are vital for establishing research-based and age-appropriate curricula for students in the primary grades, i.e., developing non-trivial, challenging but not overly daunting lesson plans that utilize the cognitive development stage of each grade level most effectively.
Correlation is a statistical method used to assess a possible linear association between two continuous variables. It is simple both to calculate and to interpret. However, misuse of correlation is so common among researchers that some statisticians have wished that the method had never been devised at all. The aim of this article is to provide a guide to appropriate use of correlation in medical research and to highlight some misuse. Examples of the applications of the correlation coefficient have been provided using data from statistical simulations as well as real data. Rule of thumb for interpreting size of a correlation coefficient has been provided.
The purpose of this study was to investigate the degree to which A. Bandura's (1997) hypothesized sources of self-efficacy predict the science self-efficacy beliefs of middle school students (N = 319), to replicate previous findings that science self-efficacy predicts science achievement, and to explore how science self-efficacy and its antecedents differ by gender. Significant correlations were found between mastery experiences, vicarious experiences, social persuasions, physiological arousal, and self-efficacy. Only mastery experiences significantly predicted science self-efficacy. Girls reported stronger science self-efficacy than did boys. Findings support and extend the theoretical tenets of Bandura's social cognitive theory. © 2006 Wiley Periodicals, Inc. J Res Sci Teach 43: 485–499, 2006
Current research has found that students who experience emotional and behavioural difficulties (EBDs) in school are not identified early, their behaviours (and causes) are often misrepresented, and they may not receive appropriate intervention. In this paper we review current theories (and their shortcomings) on the identification and recommended actions for students who experience EBDs, acknowledging the importance of teachers'' initial identification (as schools are the social arena within which most students who experience EBDs are first identified). We suggest that teachers'' identification may not be informed by the full range of theories and explanations for EBDs and that they often disregard the emotional and relational aspect of EBDs which focus on individual students and fail to recognize the social context in which the students operate. These suggestions were supported in a questionnaire-based study. Teachers'' identification of EBDs coincides with predominant theoretical explanations based upon an individual''s inability to fit into society, seeing EBDs as a product rather than social process. Such a situation may encourage instability in classrooms and may not allow for adequate intervention for EBDs.
This paper presents a study being carried out at the Universidad Autónoma de Madrid to ascertain the influence of the way
students are grouped to do collaborative work (regarding intelligence and personality parameters) on the results they get.
Data about student’s personality are analysed along with information about group composition and student performance. The
results of this analysis are expected to throw light about the impact of personal traits and group formation on learning.
This information can be incorporated in collaborative systems as criteria for group formation, with the aim of favouring CSCL
situations in which students are prone to get better results.
Researchers have been working to understand the factors that may be contributing to low rates of participation by women and other minorities in the computer and information sciences (CIS). We describe a multivariate investigation of male and female university students’ orientation to CIS careers. We focus on the roles of self-efficacy and peer-based social support as social-cognitive variables in the students’ learning process. We explore these variables and their impact on career orientation through a set of overlapping regression models. In general, we find that strong social support and high self-efficacy are associated with strong orientation toward careers in CIS careers. However the influences of perceived self-efficacy and social support appear to differ for men and women: while low self-efficacy is tied to less social support for males, we see no similar tendency for females. In fact many females who report themselves as low in self-efficacy consider themselves to have high social support among their peers. We discuss the implications of our findings for programs aimed at outreach to both males and females regarding CIS education and careers.
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