@article{shabrina_mostafavi_abdelshiheed_chi_barnes_2023, title={Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning Towards Improved Problem Solving}, volume={8}, ISSN={1560-4292 1560-4306}, url={http://dx.doi.org/10.1007/s40593-023-00338-1}, DOI={10.1007/s40593-023-00338-1}, abstractNote={AbstractLearning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal-labeled instructional materials in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. The backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic-proof construction. The training session involved backward worked examples (BWE) and problem solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students’ 1) experience with and engagement in learning backward strategy, 2) performance and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in posttest. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.}, journal={International Journal of Artificial Intelligence in Education}, publisher={Springer Science and Business Media LLC}, author={Shabrina, Preya and Mostafavi, Behrooz and Abdelshiheed, Mark and Chi, Min and Barnes, Tiffany}, year={2023}, month={Aug} }
 @article{dong_shabrina_marwan_barnes_2021, title={You Really Need Help: Exploring Expert Reasons for Intervention During Block-based Programming Assignments}, DOI={10.1145/3446871.3469764}, abstractNote={In recent years, research has increasingly focused on developing intelligent tutoring systems that provide data-driven support for students in need of assistance during programming assignments. One goal of such intelligent tutors is to provide students with quality interventions comparable to those human tutors would give. While most studies focused on generating different forms of on-demand support, such as next-step hints and worked examples, at any given moment during the programming assignment, there is a lack of research on why human tutors would provide different forms of proactive interventions to students in different situations. This information is critical to know to allow the intelligent programming environments to select the appropriate type of student support at the right moment. In this work, we studied human tutors’ reasons for providing interventions during two introductory programming assignments in a block-based environment. Three human tutors evaluated a sample of 86 struggling moments identified from students’ log data using a data-driven model. The human tutors specified whether and why an intervention was needed (or not) for each struggling moment. We analyzed the expert tags and their consensus discussions and extracted three main reasons that made the experts decide to intervene: “missing key components to make progress”, “using wrong or unnecessary blocks”, “misusing needed blocks”, “having critical logic errors”, “needing confirmation and next steps”, and “unclear student intention”. We use six case studies to illustrate specific student code trace examples and the tutors’ reasons for intervention. We also discuss the potential types of automatic interventions that could address these cases. Our work sheds light on when and why students might need programming interventions. These insights contribute towards improving the quality of automated, data-driven support in programming learning environments.}, journal={ICER 2021: PROCEEDINGS OF THE 17TH ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH}, author={Dong, Yihuan and Shabrina, Preya and Marwan, Samiha and Barnes, Tiffany}, year={2021}, pages={334–346} }