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Background and Context Lopez and Lister first presented evidence for a skill hierarchy of code reading, tracing, and writing for introductory programming students. Further support for this hierarchy could help computer science educators sequence course content to best build student programming skill. Objective This study aims to replicate a slightly simplified hierarchy of skills in CS1 using a larger body of students (600+ vs. 38) in a non-major introductory Python course with computer-based exams. We also explore the validity of other possible hierarchies. Method We collected student score data on 4 kinds of exam questions. Structural equation modeling was used to derive the hierarchy for each exam. Findings We find multiple best-fitting structural models. The original hierarchy does not appear among the “best” candidates, but similar models do. We also determined that our methods provide us with correlations between skills and do not answer a more fundamental question: what is the ideal teaching order for these skills? Implications This modeling work is valuable for understanding the possible correlations between fundamental code-related skills. However, analyzing student performance on these skills at a moment in time is not sufficient to determine teaching order. We present possible study designs for exploring this more actionable research question. 

In Computer Science (CS) education, instructors use office hours for one-on-one help-seeking. Prior work has shown that traditional in-person office hours may be underutilized. In response many instructors are adding or transitioning to virtual office hours. Our research focuses on comparing in-person and online office hours to investigate differences between performance, interaction time, and the characteristics of the students who utilize in-person and virtual office hours. We analyze a rich dataset covering two semesters of a CS2 course which used in-person office hours in Fall 2019 and virtual office hours in Fall 2020. Our data covers students' use of office hours, the nature of their questions, and the time spent receiving help as well as demographic and attitude data. Our results show no relationship between student's attendance in office hours and class performance. However we found that female students attended office hours more frequently, as did students with a fixed mindset in computing, and those with weaker skills in transferring theory to practice. We also found that students with low confidence in or low enjoyment toward CS were more active in virtual office hours. Finally, we observed a significant correlation between students attending virtual office hours and an increased interest in CS study; while students attending in-person office hours tend to show an increase in their growth mindset. 

Classroom dashboards are designed to help instructors effectively orchestrate classrooms by providing summary statistics, activity tracking, and other information [12]. Existing dashboards are generally specific to an LMS or platform and they generally summarize individual work, not group behaviors. However, CS courses typically involve constellations of tools and mix on- and offline collaboration. Thus, cross-platform monitoring of individuals and teams is important to develop a full picture of the class. In this work, we describe our work on Concert, a data integration platform that collects data about student activities from several sources such as Piazza, My Digital Hand, and GitHub and uses it to support classroom monitoring through analysis and visualizations. We discuss team visualizations that we have developed to support effective group management and to help instructors identify teams in need of intervention. 

As enrollment in CS programs have risen, it has become increasingly difficult for teaching staff to provide timely and detailed guidance on student projects. To address this, instructors use automated assessment tools to evaluate students’ code and processes as they work. Even with automation, understanding students’ progress, and more importantly, if students are making the ‘right’ progress toward the solution is challenging at scale. To help students manage their time and learn good software engineering processes, instructors may create intermediate deadlines, or milestones, to support progress. However, student’s adherence to these processes is opaque and may hinder student success and instructional support. Better understanding of how students follow process guidance in practice is needed to identify the right assignment structures to support development of high-quality process skills. We use data collected from an automated assessment tool, to calculate a set of 15 progress indicators to investigate which types of progress are being made during four stages of two projects in a CS2 course. These stages are split up by milestones to help guide student activities. We show how looking at which progress indicators are triggered significantly more or less during each stage validates whether students are adhering to the goals of each milestone. We also find students trigger some progress indicators earlier on the second project suggesting improving processes over time.