Self-efficacy is important for success in computing courses, but best practices for how computing instructors can support students' self-efficacy development are still a matter of ongoing development. CS students' judgments about their own abilities are often very self-critical and disagree with the ways that experts in CS would measure competency. Self-assessment scaffolding has been observed to improve learning outcomes and self-efficacy in various domains. However, these practices are not well known in CS education research, and there is a lack of empirically-based guidance about how to implement them. This project is devoted to developing a self-assessment scaffolding intervention for CS1 that is effective, efficient to implement, and usable for students. Improving pedagogical practices in introductory courses (CS1) will help improve the outcomes for everyone, but approaches targeting self-efficacy may particularly benefit students from minoritized groups in CS, such as women and students of color.

This poster describes a research project that is in the preliminary stages, working to develop a pedagogical intervention based on prior education research. Self-efficacy is known to be an important factor for success in computing. What is less known is how computing instructors can support students on this dimension. Prior research has suggested that self-assessment scaffolding can benefit students' self-efficacy and learning outcomes, so we are developing a pedagogical intervention to that end.

Educational stakeholders want to understand and overcome the well-documented racial and gender disparities within computer science education. There are many factors that influence students’ participation, performance, and persistence in CS courses, including motivational and affective factors. Prior research in CS education has documented the influence of these factors on students’ CS outcomes generally, and on URM students in particular. What has been less investigated, is how students’ motivational and affective experiences in CS develop and evolve from moment to moment, particularly for URM students. To better understand how these experiences develop, this paper presents the results of a study using intensive longitudinal methods which examined the differences in momentary experiences between racially underrepresented students and their represented peers in undergraduate introductory computer science courses. Using the Experience Sampling Method (ESM), we solicited responses on students’ momentary self-efficacy, interest, and affective experiences 8-18 times in each of two semesters from a total of 110 CS students, of which 19 identified as racially underrepresented.

There are significant participation gaps in computing, and the way to address these participation gaps lies not simply in getting students from underrepresented groups into a CS1 classroom, but supporting students to pursue their interest in computing further beyond CS1. There are many factors that may influence students’ pursuit of computing beyond introductory courses, including their sense that they can do what CS courses require of them (their self-efficacy) and positive emotional experiences in CS courses. When interest has been addressed in computing education, research has treated it mostly as an outcome of particular pedagogical approaches or curricula; what has not been studied is how students’ longer-term interest develops through more granular experiences that students have as they begin to engage with computing. In this paper, we present the results of a study designed to investigate how students’ interest in computing develops as a product of their momentary self-efficacy and affective experiences. Using a methodology that is relatively uncommon to computer science education—the experience sampling method, which involves frequently asking students brief, unobtrusive questions about their experiences—we surveyed CS1 students every week over the course of a semester to capture the nuances of their experiences. 74 CS1 students responded 14-18 times over the course of a semester about their self-efficacy, frustration, and situational interest. With this data, we used a multivariate, multi-level statistical model that allowed us to estimate how students’ granular, momentary experiences (measured through the experience sampling method surveys) and initial interest, self-efficacy, and self-reported gender (measured through traditional surveys) relate to their longer-term interest and achievement in the course. We found that students’ momentary experiences have a significant impact on their interest in computing and course outcomes, even controlling for the self-efficacy and interest students reported at the beginning of the semester. We also found significant gender differences in students’ momentary experiences, however, these were reduced substantially when students’ self-efficacy was added to the model, suggesting that gender gaps could instead be self-efficacy gaps. These results suggest that students’ momentary experiences in CS1, how they experience the course week to week, have an impact on their longer-term interest and learning outcomes. Furthermore, we found that male and female students reported different experiences, suggesting that improving the CS1 experiences that students have could help to close gender-related participation gaps. In all, this study shows that the granular experiences students have in CS1 matter for key outcomes of interest to computing education researchers and educators and that the experience sampling method, more common in fields adjacent to computer science education, provides one way for researchers to integrate the experiences students have into our accounts of why students become interested in computing.

While computer science (CS) education researchers have frequently examined what happens in courses, programs of study, or occupations in general, they have less frequently addressed finer-grained experiences that spark students' interest in CS. One excellent way to study these types of student experiences is the Experience Sampling Method (ESM). ESM involves collecting data on individuals' experiences at much more frequent intervals than traditional survey research. This aspect of ESM makes it well-suited to examine time-specific aspects of students' experiences, as well as changes due to the disruptive effects of COVID-19.

Around the United States, educators are teaching new computer science (CS) education standards, including in Tennessee, which announced its first K-8 CS standards in 2018. In Tennessee, the standards are now the official guidelines for CS education at the K-8 level, what barriers prevent CS teaching and learning to become a reality, especially for elementary and middle grades teachers, are unknown. We developed and administered a needs survey for K-8 teachers regarding CS education as a part of a broader community-engaged project. From 251 K-8 teachers' responses, we found that CS is important to them, but there are barriers to meeting the standards. Qualitative items about needs and barriers related to CS education teachers revealed a demand for professional development and training opportunities for teachers to learn about both the technical aspects of computing education and embedding computing ideas across the K-8 curriculum. We will discuss the implications of these findings and will describe how these results will enable our efforts to provide professional learning opportunities for educators.

This study examines the self-efficacy of prospective computer science teachers and the background factors that may be related to differences in their self-efficacy. The self-efficacy profiles of teachers were examined using cluster analysis, and three self-efficacy profiles were established. Then the differences between clusters were examined with respect to several categorical variables regarding their teaching background experiences. The results of this study show that teachers' self-efficacy for teaching computer science varies with their academic background, but not with other background characteristics like their amount of teaching experience or prior experience level with CS.

Research has repeatedly shown self-efficacy to be associated with course outcomes in CS and across other fields. CS education research has documented this and has developed CS-specific self-efficacy measurement instruments, but to date there have been only a few studies examining interventions intended to improve students’ self-efficacy in CS, and several types of self-efficacy interventions suggested by previous research remain to be tested in CS. This study attempts to address this lack of research by reporting on the results of a trial intervention intended to improve students’ self-efficacy in an introductory programming course. Students were recruited to complete a self-evaluation task, which previous research has suggested could have a beneficial impact on self-efficacy, which should in turn have a beneficial impact on course performance. Participating students’ course outcomes and self-efficacy were compared with those of the students who did not complete the self-evaluation task, using propensity score weighting adjustments to control for differences between the groups on entering characteristics and prior values of self-efficacy and course outcomes. We found that, whereas there was only marginal evidence for the self-evaluation intervention having a direct effect on self-efficacy, students who completed the self-evaluation task had significantly higher project scores during the weeks they were asked to complete it, compared to the students who did not participate. These findings suggest that there are potential benefits to incorporating self-evaluation tasks into introductory CS courses, although perhaps not by virtue of directly influencing self-efficacy.

This lightning talk describes a new grant-funded research project investigating how undergraduate computer science students' interest in CS develops. The goal of this project is to investigate how sustained, individual interest develops from finer-grained experiences of situational interest that students have in introductory CS courses. This project will accomplish this goal using the experience sampling method (Hektner, Schmidt, & Csikszentmihalyi, 2007), which is a longitudinal research method that asks participants to report on their immediate experiences at many occasions. Prior research has not adequately investigated what drives individual differences in CS interest at the level at which policymakers and educators can most effectively act, namely, at a situation-to-situation level, rather than in terms of what happens in courses, programs of study, or occupations in general and overall. Moreover, research has shown that some overall factors, such as competence-related beliefs and co-curricular supports, might be related to the development of sustained interest in CS (e.g., Lishinski, Yadav, Good, & Enbody, 2016). Thus, we will explore how students' initial interest, as well as their individual motivational characteristics, such as CS self-concept, and CS self-efficacy, relate to interest at a situation-to-situation level. Furthermore, we will also explore how contextual factors - those internal to students, such as how challenging they found the activities, as well as those external to students, such as the focus of each class - relate to students' situational interest. Overall, we hope to better understand how students' situational interest relates to changes in their longer-term, individual interest.

The entire enterprise of computer science education is predicated on the ability to develop and sustain students' interest in the subject. Given how fundamental this aspect of the educational process is, our understanding of what experiences are actually driving the development of interest in computer science remains far from complete. This BOF session seeks to inform the direction of a new research project dedicated to investigating this question, by eliciting a discussion from expert practitioners about what they know from their experiences about how interest in CS develops.

Previous research has found that programming assignments can produce strong emotional reactions in introductory programming students. These emotional reactions often have to do with the frustration of dealing with difficulties and how hard it can be to overcome problems. Not only are these emotional reactions powerful in and of themselves, they have also been shown to induce students to make self-efficacy judgments, which can in turn cause adaptive or maladaptive behaviors, depending on the valence of the judgment. These results have been found in previous qualitative research in programming, however, to date no one has done a larger scale quantitative examination of emotional reactions in introductory programming students. Furthermore, no one has tried to connect these emotional reactions systematically to student learning outcomes. Therefore, this study reports on the pilot use of a basic emotional reactions survey with a large class of undergraduate introductory programming students. Preliminary results are presented on how these emotional reactions affect students' course outcomes over the short and longer term.

Programming is a complex cognitive skill that develops over an extended period of time. The development of programming ability is the product of a number of different cognitive, affective, and dispositional factors. Furthermore, programming ability itself is a complex learning outcome that cannot be measured simply. Prior research on the individual factors that are associated with success in programming is extensive, but detailed pictures of the interactions over time of the many factors involved are rare to non-existent. My dissertation research focuses on building such a detailed picture of the factors that contribute to students developing programming ability. If these processes were better understood by CS education researchers, then interventions to improve student learning in introductory programming contexts could be more theoretically informed and effectively applied.

The problem of the lack of rigor in CS education research has frequently been discussed and examined. Previous reviews of the literature have examined rigor on both theoretical and methodological dimensions, among others. These reviews have also looked at differences in indicators of rigor between conference proceedings and journal publications. However, to date there is no comprehensive review that has examined the intersection of methodological and theoretical quality.

Previous research in computer science education has demonstrated the importance of motivation for success in introductory 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 strategies) interact in complex ways to influence students' affective 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 interact 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 outcomes. We found that student performance had the expected motivational and SRL precursors, but the interactions between these constructs revealed some unexpected relationships. Furthermore, we found that females' self-efficacy had a different connection to programming performance 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 relationships that are specific to CS.

Previous research has suggested that cognitive tests, including instruments seeking to measure problem solving, are significant predictors of students' programming performance. This paper seeks to expand upon this previous research by using a more theoretically grounded approach to measuring problem solving as a means of predicting performance in an introductory undergraduate programming course. Programming course performance has typically been measured by overall course grades; however, in this paper we used a more fine-grained approach to measuring student programming performance. Specifically, we utilized different types of course assignments (projects and tests) to measure programming outcomes. Results from this study indicate that problem solving ability significantly correlates with performance on programming assignments, but does not correlate with performance on multiple-choice exams.