Use-Modify-Create (UMC) has gained recognition as a viable scaffolding approach for student programming activities, but little is known about how UMC could support CS learning in game-based learning environments. We designed and developed a game to teach middle grade students (ages 11-13) CS through block-based programming challenges. The game integrates a UMC pedagogical framework to promote successful student outcomes for a wide variety of student abilities, including those without prior programming experience. Utilizing a mixed-methods research design, we investigated how the game influenced student learning of CS concepts and the role of UMC on the problem-solving strategies students applied to complete the game. In particular, we were interested in how prior experience would moderate these outcomes. Results from a multilevel model of students' pre-and post-assessment scores (N = 77) on a CS concepts assessment indicated that all students, regardless of prior programming experience, showed significant learning gains from pre to post after playing the game. Qualitative results revealed that the UMC scaffolding progression provided students, particularly those with little to no prior programming experience, with the foundational knowledge needed to progress through the game levels and challenges. Specifically, we found that the Use phases of the game reduced novice students' cognitive load and facilitated the necessary CS conceptual understanding to solve the open-ended programming tasks encountered in the game's Modify and Create phases. Our findings demonstrate the efficacy of UMC to support the learning of novice programmers in a game-based learning environment while not to the detriment of those more experienced.

Computational modeling provides an excellent vehicle for raising scientific awareness of emergent and topical phenomena such as COVID-19. Now more than ever, it is crucial to provide students with factual information about how diseases spread and how their own actions can impact that spread. In order to both encourage computational thinking skills and build scientific knowledge of the COVID-19 pandemic, we have created a series of programming activities through which students construct their own computational models based on the emerging scientific consensus around COVID-19. Students are able to model everyday situations such as being in a crowded area or going to stores while unknowingly infected, and immediately see the consequences of those actions. By including accurate scientific variables such as the reproductive number of the virus, incubation period, and period of communicability, students are able to create their own epi-curves that demonstrate the severity of the disease and provide students with visual representation of how quickly COVID-19 spreads. We also use the scientific model and associated modeling activities to reinforce best practices at home and in the community. Finally, this curriculum development effort demonstrates how block-based computational modeling activities lend themselves to agile curricular re-design around emerging and topics of local interest

Block-based programming environments are widely used by novices who are learning computer science. However, even in block-based coding environments that have been carefully developed to serve novices, students frequently struggle and require additional support. A promising avenue to provide this support is the use of intelligent tutoring systems, which offer adaptive hints to assist learners. In order to provide students with the adaptive hints they need, we must investigate their help-seeking behaviors and identify patterns surrounding their need for support. In this experience report, we examine data collected from 174 college students in an introductory engineering course, who used an intelligent block-based coding environment to learn computer science. These students made more than 1,000 hint requests, which we represent in two-dimensional space along axes of elapsed time and code completeness. Analysis revealed five major clusters of hint requests, which we further characterized through qualitative examination of the coding trajectories that preceded each hint request. We also analyzed how students' incoming knowledge and perceived computer skill were related to their help-seeking behaviors. Students with higher incoming knowledge requested hints when their code was more complete than students with lower incoming knowledge. Students with high perceived computer skill asked for hints when their code was less complete than those with low perceived computer skill. The results presented here provide insight into student help-seeking behavior in computer science education, informing CS educators and system designers on how best to develop support strategies.

Collaborative learning has demonstrated benefits for girls in computer science [7] and this may be a way to help address the gender gap in CS. Research indicates that while collaborating, boys often express more individualistic ideas whereas girls tend to be more supportive [1]. It is important for students to regulate their learning in collaborative learning environments because they need to negotiate group goals and diverse approaches to the task [3], and the use of open-ended tasks with multiple solution paths are common [4]. There is minimal research in CS education on regulation of learning (e.g., [5,6,]). Co-regulated learning is the process in which an other helps regulate the learning of a student [2] self such as by asking questions that prompt the student to monitor and evaluate (e.g., "What do you already know about 'if' blocks that would help here?''). In this way, thinking and reasoning through the problem is shared by the group members [2] self.

Computer science (CS) is widely recognized as a field with a significant gender gap despite the growing prevalence of computing. Several factors including CS attitudes, exposure to CS, experience with computer programming, and confidence in using computers are understood to be correlated with the low participation of women in CS. These factors also play an important role in students' interest in CS careers and are particularly crucial during secondary school. However, there is a dearth of research that examines differences in how these factors are inter-correlated for younger students (ages 11-13). The purpose of this study was to generate and test a statistical model that demonstrates the inter-correlation amongst these factors with respect to gender. A total of 260 middle school students participated in this study. Four instruments measuring students' CS attitudes, confidence in using computers, CS conceptual understanding, and prior experience with CS-related activities were used. Structural equation modeling was utilized to test the hypothesized model. The findings showed that previous participation in CS-related activities had a significant direct effect on CS attitudes and confidence in using computers, but the effect on students' CS conceptual understanding was indirect. We also found that in a female specific model, previous participation had a significantly stronger direct effect on CS attitudes compared to its effect in a male specific model. The importance of providing more CS-related experience, especially to female students, as well as suggestions on activities that promote gender equity in the field are discussed.