The ASSISTment Project Trying to Reduce Bottom-out hinting: Will telling students how many hints they have left help? By Yu Guo, Joseph E. Beck& Neil T. Heffernan, CS Department All Intelligent Tutoring Systems give help of one kind or another. Sometime students ask for more help than they probably need. We assume bottom-out hinting hurts learning. We wanted to build a very simple intervention to prevent students from gaming the system to reach a correct answer. Motivation  The ASSISTment System is a web-based assessment system that tutors students on math problems. The system is freely available at As of March 2007, 1,000’s of Worcester middle school students use ASSISTments every two weeks as part of their math class.  Teachers use the fine-grained reporting that the system provides to inform their instruction. Goal Background on ASSISTments Analysis Experiment Design Intervention: We simply told students the number of hints they had not yet seen so that they could see they were getting close to the bottom out hint. As shown here Experiment: We turned on the intervention on 10/29/2007 randomly in half classrooms, leaving the other classrooms act as a control for any existing trends over time in student behavior. We randomly select 50 students in the control and 50 in the experiment for analysis. On average the intervention led students to hit more bottom-out hints. Moreover, we found that the intervention had different effects for different population. Students that had lower and medium knowledge actually asked for more bottom-out hints while high knowledge subject “benefited” from the intervention The logistic regression shows that the change on average will lead to more bottom-out hints. Students with high knowledge asked less bottom-out hints while those with low and medium knowledge asked more. According to the results, we suggest ITSs to give the information of how many hints left to students with high knowledge and hide it from those with low and medium knowledge. This work has been accepted for publication at Proceedings of the 9th International Conference on Intelligent Tutoring Systems. CollaboratorsSponsors Our Hypothesis Some students might not want to be told the exact answer but the current system can never be sure if next time they ask for a hint, they will get told the exact answer.Our hypothesis is that the information of how many hints they have left will reduce the bottom-out hinting and thus make students think over problems before they go through all the hints to the bottom one. To determine if letting students know when the bottom-out hinting is getting close will cut down the incidence of bottom-out hinting. Conclusion We built a logistic regression model to predict students’ bottom-out hinting action. The data are shown as following. Control Group Students in the control group are shown the button saying “Request help.” In this case, they are not able to know how close they are to the bottom-out hint. Students in the experiment group are shown the information about how close they are to the bottom-out hint. 136 Experiment Group