their own predic-tion about their final course grade at the beginning of the semester. In particular, we study students’learning self-efficacy, that is, their confidence in themselves to learn in a CS1 course and outcomeexpectancy, that is their expected final grade in the course. We use the term learning self-efficacybecause it refers to students’ confidence measured at the beginning of the course. It’s a proxyfor their perceived ability to solve problems and learn to program. By taking factors like gender,prior programming experience, and GPA, we are interested in analyzing which factors influence astudent’s outcome expectancy and their learning self-efficacy at the beginning of a CS1 course.2 Background and Related WorkVarious instruments
are poorpredictors of students’ learning gains due, in part, to students’ inability to accurately assess theirlearning as socio-cognitive elements such as a students’ self-efficacy beliefs may distort theirperceptions of their own learning, causing some to overestimate their learning gains while others,with lower self-efficacy beliefs, underreport their learning gains (Lattuca, 2023).We contend that this issue is particularly important in computer science (CS) education, whereautograded assignments are a growing approach to delivering students, instructors, andresearchers feedback on written coding assignments (Haldeman et al., 2018). That is, autogradersmay falsely suggest that students who have developed and implemented working code
personnel, and others. In our approach, first, we devised course-specificmentoring objectives through literature surveys and pre-course surveys. To achieve theseobjectives, we created a set of mentoring activities. We also designed evaluation metrics to assesswhether there are any changes in students' perceptions toward computing programs and perceivedimpacts on students' self-efficacy and sense of belonging over time. Through these analyses, wetried to measure whether we need to design course-level-specific mentoring to help ourunderrepresented students attain their computing careers. We believe our mentoring should helpour underrepresented and predominantly major students who may hesitate to pursue a computingprogram or require enhanced self
phase, we now conduct weekly reviews of rules notebooksto understand patterns of misunderstanding, using these and other observations to responsively developlater class activities. In the conceptualization and investigation phases, during which we offer students previously gener-ated code examples, learning activities now include guided questions pointing students to explore specificconcepts (e.g., syntax, data structures, error messages) as well as to report on their understanding of thoseconcepts in open ended responses. Our future work will continue to study the results of applying this pedagogical strategy. We willcollect more data, including surveying students to measure self-efficacy and other indicators of studentaffect and collect
Sensor Networks in Health Care 72 2013 System STEM Outreach: Assessing Computational Thinking and 49 2017 Problem Solving Cloud Computing in Computer Science and Engineering 30 2012 Education Survey of Cybersecurity Education through Gamification 24 2016 The Impact of STEM Experiences on Student Self-Efficacy 22 2016 in Computational Thinking Gamification-Based Cyber-Enabled Learning Environment 20 2016 of Software Testing Exploring Computing Identity and Persistence Across 18 2019 Multiple Groups Using Structural Equation
, but also the environmental and behavioral influences[27]. Based on Bandura’s self-efficacy [28], it considers the factors that may have an impact onand determine performance, including interactions with others and personal achievements, andhow they may contribute. Ultimately, this leads to outcome experiences — the perceived resultsof taking certain actions [26]. It seeks to describe how interventions and activities can enhancepersonal mastery experiences. Furthermore, in the context of computing, it has been shown todescribe not only interests, but also choice goals [27].An overview of the framework, as it pertains to our study, is presented in Figure 1. We considerlearning experiences in terms of professional and technical skill development
groups in computer science programs and careers have been suggested. Lackof access to computing technology, inadequate K-12 preparation, lack of role-models, stereotypethreat, and lower self-efficacy have all been identified as reasons non-majority students do notenter or eventually leave computing programs [8]-[19]. Specifically in STEM fields anddisciplines, non-majority students’ sense of belonging is imperative to their retention and successwithin STEM programs and is associated with a variety of positive outcomes for individualsincluding: increased GPA, increased self-reported health and well-being, and increased academicscores [20], [21]. Yet, in direct opposition to non-majority students cultivating this sense ofbelonging, or fit, in
acceptance that a student receives from variouspersonal stakeholders, such as family, peers, and mentors. Finally, competence/performance isthe closest construct to a student’s feeling of self-efficacy and indicates their level ofself-confidence in their knowledge and abilities in computing. While the four sub-constructsmeasure distinguishable aspects of a student’s sense of identity in a field, they also influence eachother in a dynamic manner based on a student’s unique environment and context [24], a facet weindicate with bi-directional arrows.Students’ computing identity has previously been used as a measure of persistence [27] and alsotheir career choice [24]. We applied the computing identity framework to study students’ ties tothe discipline
computing.The first metaphor, the pipeline, focuses on students’ progression through an educational systemtoward the computing workforce. It emphasizes student retention, aiming to address the issue ofindividuals dropping out of the pipeline before reaching professional roles. Lee [19] emphasizesthat this metaphor highlights the deficits of students who do not continue along the pipeline,often implying that these individuals lack the necessary skills or attributes to remain in thecomputing field. For instance, the “leaky” pipeline metaphor might attribute theunderrepresentation of women in computing to a lack of self-efficacy or skills to sustain theirinterest and commitment to computing careers (e.g., [20]). We also align with scholars whocritique the
pursue a professional computing industry careerpathway [11]. Factors hindering computing students from pursuing internships are studentinterest in internships include, lower self-efficacy, the challenging application process forinternships, and other priorities such as family, focusing on their GPA, etc. [12]. Less frequently,students may consider going into business for themselves as an entrepreneurship pathway. Jobmarket conditions and socioeconomic status are primary factors influencing the students’decision to pursue entrepreneurship [4], [13]. Finally, though perhaps not exhaustively, studentscan consider attending graduate school and conducting research through a master’s or Ph.D.degree. Students’ interest and actual enrollment in graduate
performance, including motivation, self-efficacy, values,curiosity, and, most importantly, learning environments. Learning is a cognitive phenomenon thatdiffers from person to person. There is no doubt, however, that learning through hands-onexperience is an effective method of retaining information. Undergraduate students in this digitalage have grown up with technology and come from an education system that encourages criticalthinking, hands-on learning, teamwork, design skills, problem solving, and experiential learning[1]. Most students today are visual and interactive learners, and research in educational theory andcognitive psychology shows that this type of learning is one of the most effective methods forteaching students of all ages how to
graduate school, and I Am First program for first generation students. Inaddition, the BE-TEC program is extending or adapting successful evidence-based practicesfrom its Track 1 program. The planned support services and programs have been selected toincrease academic learning, completion, and career or graduate school placement, as well as toassist in soft-skills development which is so important for graduates such as communication,teamwork, self-efficacy, leadership, and knowledge integration.NSF BE-TEC Program AssessmentTo assess the outcome of our NSF BE-TEC program, a study has been started by the institution’sBusiness Intelligence and Research Services to compare the NSF BE-TEC students to twocontrol groups: UVU students
debugging has also been tested, finding relationships betweensystematic debugging exposure and students' self-efficacy and effective debugging ([22], [23]).Debugging and students’ performancePrevious research has established the complexity and multiple factors that influence studentsdebugging performance. To date, several studies have focused on how the program errormessage influences students’ skills and strategies to debug [24], the time novice students take todebug a problem by using counting error compilers [13], identifying how visual attention couldalso impact students debugging performances [25] and the type of high or lower achieversinfluence students’ strategies and performance on debugging [13], [26].Studies have shown that students spend
include subscales that assess research abilities, leadership potential, self-efficacy,sense of one’s identity as a scientist, plans to attend graduate school, plans to pursue engineering,mentorship connections, attitudes toward research, etc. The conclusions drawn from the SageFoxassessment report are presented in this section and available on the program website [15].REU HighlightsThe data collected during the four years of the program shows that the program has beensuccessful during the pandemic and beyond. The results from the survey suggest that there hasbeen an increase in STEM knowledge, confidence, and high intention to pursue engineering as adegree. Even though the program has been successful and met its goals, the data results showthat
] A. Robins, J. Rountree, and N. Rountree, “Learning and teaching programming: A reviewand discussion,” Computer Science Education, vol. 13, no. 2, pp. 137–172, Jun. 2003, doi:https://doi.org/10.1076/csed.13.2.137.14200.[13] S. Katz, D. Allbritton, J. Aronis, C. Wilson, and M. L. Soffa, “Gender, achievement, andpersistence in an undergraduate computer science program,” ACM SIGMIS Database: theDATABASE for Advances in Information Systems, vol. 37, no. 4, pp. 42–57, Nov. 2006, doi:https://doi.org/10.1145/1185335.1185344.[14] G.Y. Lin, “Self-efficacy beliefs and their sources in undergraduate computing disciplines,”Journal of Educational Computing Research, vol. 53, no. 4, pp. 540–561, Nov. 2015, doi:https://doi.org/10.1177/0735633115608440.[15