@article{shabrina_mostafavi_chi_barnes_2023, title={Impact of Learning a Subgoal-Directed Problem-Solving Strategy Within an Intelligent Logic Tutor}, volume={13916}, ISBN={["978-3-031-36271-2"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-36272-9_32}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023}, author={Shabrina, Preya and Mostafavi, Behrooz and Chi, Min and Barnes, Tiffany}, year={2023}, pages={389–400} }
 @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={Abstract Learning 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.}, 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} }