@article{cody_maniktala_lytle_chi_barnes_2021, title={The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-021-00237-3}, abstractNote={Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation from historical student data, as well as research showing positive impacts on student learning. However, there is a lack of research exploring the possibility of extending data-driven methods to provide higher-level assistance. Therefore, we modified our data-driven Next-Step hint generator to provide Waypoints, hints that are a few steps ahead, representing problem-solving subgoals. We hypothesized that Waypoints would benefit students with high prior knowledge, and that Next-Step hints would most benefit students with lower prior knowledge. In this study, we investigated the influence of data-driven hint type, Waypoints versus Next-Step hints, on student learning in a logic proof tutoring system, Deep Thought, in a discrete mathematics course. We found that Next-Step hints were more beneficial for the majority of students in terms of time, efficiency, and accuracy on the posttest. However, higher totals of successfully used Waypoints were correlated with improvements in efficiency and time in the posttest. These results suggest that Waypoint hints could be beneficial, but more scaffolding may be needed to help students follow them.}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Cody, Christa and Maniktala, Mehak and Lytle, Nicholas and Chi, Min and Barnes, Tiffany}, year={2021}, month={May} }
 @article{maniktala_cody_barnes_chi_2020, title={Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor}, volume={30}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-020-00213-3}, abstractNote={Within intelligent tutoring systems, considerable research has investigated hints, including how to generate data-driven hints, what hint content to present, and when to provide hints for optimal learning outcomes. However, less attention has been paid to how hints are presented. In this paper, we propose a new hint delivery mechanism called "Assertions" for providing unsolicited hints in a data-driven intelligent tutor. Assertions are partially-worked example steps designed to appear within a student workspace, and in the same format as student-derived steps, to show students a possible subgoal leading to the solution. We hypothesized that Assertions can help address the well-known hint avoidance problem. In systems that only provide hints upon request, hint avoidance results in students not receiving hints when they are needed. Our unsolicited Assertions do not seek to improve student help-seeking, but rather seek to ensure students receive the help they need. We contrast Assertions with Messages, text-based, unsolicited hints that appear after student inactivity. Our results show that Assertions significantly increase unsolicited hint usage compared to Messages. Further, they show a significant aptitude-treatment interaction between Assertions and prior proficiency, with Assertions leading students with low prior proficiency to generate shorter (more efficient) posttest solutions faster. We also present a clustering analysis that shows patterns of productive persistence among students with low prior knowledge when the tutor provides unsolicited help in the form of Assertions. Overall, this work provides encouraging evidence that hint presentation can significantly impact how students use them and using Assertions can be an effective way to address help avoidance.}, number={4}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Maniktala, Mehak and Cody, Christa and Barnes, Tiffany and Chi, Min}, year={2020}, month={Nov}, pages={637–667} }
 @article{maniktala_cody_barnes_chi_2021, title={Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor (September, 10.1007/s40593-020-00213-3, 2020)}, volume={31}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-020-00232-0}, abstractNote={A Correction to this paper has been published: https://doi.org/10.1007/s40593-020-00232-0}, number={1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Maniktala, Mehak and Cody, Christa and Barnes, Tiffany and Chi, Min}, year={2021}, month={Mar}, pages={154–155} }
 @article{cody_ford_siraj_2015, title={Decision Tree Learning for Fraud Detection in Consumer Energy Consumption}, DOI={10.1109/icmla.2015.80}, abstractNote={The electrical grid is transitioning to new smart grid technology. With smart meters becoming an essential feature in smart homes, concerns regarding smart meters and the vast amount of consumer data that it captures are on the rise. While access to this fine-grained energy consumption data captured by smart meters can potentially violate consumer privacy, advanced analysis of this data can help to protect the interest of both the consumer and the utility company by enabling fraud detection at either end. The use of machine learning techniques has been a very common approach to energy fraud detection. Patterns in energy consumption can be recognized and used to detect anomalous behavior. This research reports on a novel application of decision tree learning technique to profile normal energy consumption behavior allowing for the detection of potentially fraudulent activity.}, journal={2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)}, author={Cody, Christa and Ford, Vitaly and Siraj, Ambareen}, year={2015}, pages={1175–1179} }