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This project utilizes technologies to overcome challenges in implementing research-supported pedagogical interventions in an open-access institution. The target course is an introductory programming course (CS1) with large enrollment, though the principles can be generalized to other ITEC and STEM courses. CS1 is widely recognized as a barrier course, with national failure rates often exceeding 30%. 

Introductory programming (CS1) is widely regarded as one of the most difficult courses in computing education, with reported failure rates ranging from 30% to 50% in the literature. A meta-analysis found an average global failure rate of 33%, underscoring the persistent challenges students face in learning to program. At Georgia Gwinnett College, Programming Fundamentals (our CS1) shows similar DFW rates. 

Fair and consistent assessment of student learning is critical in educational settings, particularly when evaluating the impact of instructional innovations. Although widely used for efficiency, output-based auto-grading often falls short in capturing partial understanding—limiting its effectiveness for measuring learning gains. This paper presents an empirical evaluation of a rubric-based, question-focused, double-grading protocol for written-response (WR) coding questions in pre- and post-tests from a large introductory programming course. This work provides both methodological insights and practical guidance for scaling reliable grading of WR coding questions. To balance efficiency and accuracy, each grader scored a specific question item across all submissions, with two graders assigned per item. Adjudication was triggered when score differences exceeded a 20% threshold. Intraclass Correlation Coefficient (ICC) analysis identified two items with initially low inter-rater reliability. After rubric clarification and regrading, reliability improved substantially, with ICC values ranging from 0.892 to 0.967 (all data) and 0.831 to 0.875 (excluding zero scores). We describe the iterative development of the assessment process and show how this structured approach—combined with ICC analysis as a diagnostic tool and targeted adjudication—achieves strong inter-grader reliability. The framework is scalable and robust for WR coding question evaluation in CS1 settings and is adaptable to a range of instructional contexts. These findings support instructors and researchers seeking consistent, practical methods for assessing WR student work in programming courses. 

Using gaming as a pedagogical approach to engage students when teaching programming has been a strategy used by coding educators. Even though initially developed as a domain-specific language built on top of Java for creative coding applications, Processing has matured into a robust framework. However, it can also be used for students to easily learn the core concepts of Object-Oriented Programming, even if they are not familiar with Java. Processing is a simple programming environment that was created to make it easier to develop visually oriented applications with an emphasis on animation and providing users with instant feedback through interaction. The focus of the lesson is two-fold: familiarize the student with the Processing environment, which supports graphical capabilities readily and to gently introduce to the concepts of a Class and an Object in Object-oriented Programming, using a simple “Serpentine-style” game. By integrating the action of a recognizable game, students can develop CS skills that encourage their engagement and continued attainment of a degree. 

https://dl.acm.org/doi/10.5555/3799943.3799951 

Introductory programming (CS1) remains a major barrier in computing education, with failure rates often exceeding 30%. To address both cognitive and motivational challenges, we developed the Synergy Approach, a six-component instructional model. This paper focuses on the first three core components: (1) Process-Oriented Guided Inquiry Learning (POGIL) for concept introduction, (2) autograded visual tracing exercises to reinforce semantic understanding, and (3) a guided framework for developing problem-solving strategies. These components target key learning stages and are supported by web-based tools, TracingQuiz for interactive code tracing and Desmos for team-based guided inquiry, to enable coordination, feedback, and scalability. Piloted in eight CS1 sections over two semesters, the integrated approach led to statistically significant learning gains. The treatment group outperformed the control by 10.21 points (~8.5% of maximum points) in the full dataset, and 5.15 points (~4.3%) in a subset limited to instructors who taught both groups. Boxplot analysis suggests the intervention benefited lower- to middle-performing students by raising the performance floor and reducing variability. Attitudinal outcomes were mixed but promising. While confidence and perceived usefulness declined slightly, treatment students reported larger gains in persistence and affective engagement, indicating increased resilience and emotional connection to programming. Post-survey evaluations also reflected strong student support for the tools: 65% rated TracingQuiz more than moderately effective for learning, and 54% did so for Desmos-supported peer interaction. These results support the value of a coordinated, technology enhanced instructional model in CS1. Future work will examine the remaining three motivation-focused components and investigate how factors such as instructional fidelity, course modality, and student background influence both learning and motivation outcomes. 

A novel two-photon direct laser writing-based hybrid strategy for 3D nanoprinting microfluidic vessels with sophisticated 3D architectures and custom-designed micropores. 

This project integrates high-impact practices tailored to diverse learning styles, forming a holistic approach for CS1. It addresses key implementation challenges and leverages automated tools to enhance the effectiveness of these practices. Specifically, the project incorporates Process-oriented Group Inquiry Learning (POGIL), auto-graded visualizer-based tracing exercises (TracingQuiz), and a POGIL-inspired problem-solving strategy called Iterative Problem-Solving Strategy Detection and Development (IPSSDD). Additional components include peer-recorded instructional videos, peer-created game development workshops, and semester-long team-based projects. By making implicit learning steps explicit, this approach effectively supports diverse learning styles. Our integration of high-impact practices and automated tools streamlines activities, enhances online engagement, and provides targeted feedback. Ultimately, this holistic approach aims to improve student engagement in CS1 and increase retention and graduation rates in the IT program. A pilot study conducted in Fall 2024 showed that an overwhelming majority of students felt the implemented components improved their understanding of programming concepts, increased their interest and motivation in programming, and provided more opportunities to interact with and receive support from peers.