@inproceedings{reichert_sthapit_tabarsi_limke_price_barnes_2024, title={Experience Helps, but It Isn't Everything: Exploring Causes of Affective State in Novice Programmers}, url={https://doi.org/10.1145/3626253.3635508}, DOI={10.1145/3626253.3635508}, abstractNote={Affective state, referring to an individual's feeling, can impact students' confidence and retention in CS, particularly for novice programmers. However, little research has been conducted to examine how moments that occur during programming impact students' affective states in real-time. In this pilot study, seven undergraduate students in an introductory block-based programming course completed a programming assignment and were surveyed and interviewed about their experience and self-efficacy as programmers. While programming, students periodically recorded their affective states via a popup in the programming environment. We performed retrospective think-aloud interviews with students afterward, asking them to watch and reflect on recordings of their programming. We subsequently analyzed student interviews using thematic analysis to derive 206 codes. These codes were grouped into three areas that impacted affect: the environment, objective progress, and perceptions during programming. To explore why students responded as they did to moment occurrence, we further categorized students based on four dimensions: programming experience, assignment completion, confidence, and the impact of the programming session on self-efficacy. Our initial results suggest that while certain moments elicit similar affective states among students, the interaction of the aforementioned four dimensions may have a higher impact on novices' affective states during programming. We conclude with recommendations for educators to improve students' affective states during and after programming.}, author={Reichert, Heidi and Sthapit, Sandeep and Tabarsi, Benyamin T. and Limke, Ally and Price, Thomas and Barnes, Tiffany}, year={2024}, month={Mar} }
 @article{tabarsi_reichert_lytle_catete_barnes_2024, title={Scaffolding Novices: Analyzing When and How Parsons Problems Impact Novice Programming in an Integrated Science Assignment}, url={https://doi.org/10.1145/3632620.3671110}, DOI={10.1145/3632620.3671110}, journal={20TH ANNUAL ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH, ICER 2024, VOL 1}, author={Tabarsi, Benyamin and Reichert, Heidi and Lytle, Nicholas and Catete, Veronica and Barnes, Tiffany}, year={2024}, pages={42–54} }
 @article{tabarsi_reichert_qualls_price_barnes_2023, title={Exploring Novices' Struggle and Progress during Programming through Data-Driven Detectors and Think-Aloud Protocols}, ISSN={["1943-6092"]}, DOI={10.1109/VL-HCC57772.2023.00029}, abstractNote={Many students struggle when they are first learning to program. Without help, these students can lose confidence and negatively assess their programming ability, which can ultimately lead to dropouts. However, detecting the exact moment of student struggle is still an open question in computing education. In this work, we conducted a think-aloud study with five high-school students to investigate the automatic detection of progressing and struggling moments using a detector algorithm (SPD). SPD classifies student trace logs into moments of struggle and progress based on their similarity to prior students' correct solutions. We explored the extent to which the SPD-identified moments of struggle aligned with expert-identified moments based on novices' verbalized thoughts and programming actions. Our analysis results suggest that SPD can catch students' struggling and progressing moments with a 72.5% F1-score, but room remains for improvement in detecting struggle. Moreover, we conducted an in-depth examination to discover why discrepancies arose between expert-identified and detector-identified struggle moments. We conclude with recommendations for future data-driven struggle detection systems.}, journal={2023 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING, VL/HCC}, author={Tabarsi, Benyamin and Reichert, Heidi and Qualls, Rachel and Price, Thomas and Barnes, Tiffany}, year={2023}, pages={179–183} }