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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.

 

While Reinforcement learning (RL), especially Deep RL (DRL), has shown outstanding performance in video games, little evidence has shown that DRL can be successfully applied to human-centric tasks where the ultimate RL goal is to make the \textit{human-agent interactions} productive and fruitful. In real-life, complex, human-centric tasks, such as education and healthcare, data can be noisy and limited. Batch RL is designed for handling such situations where data is \textit{limited yet noisy}, and where \textit{building simulations is challenging}. In two consecutive empirical studies, we investigated Batch DRL for pedagogical policy induction, to choose student learning activities in an Intelligent Tutoring System. In Fall 2018 (F18), we compared the Batch DRL policy to an Expert policy, but found no significant difference between the DRL and Expert policies. In Spring 2019 (S19), we augmented the Batch DRL-induced policy with \textit{a simple act of explanation} by showing a message such as \textit{"The AI agent thinks you should view this problem as a Worked Example to learn how some new rules work."}. We compared this policy against two conditions, the Expert policy, and a student decision making policy. Our results show that 1) the Batch DRL policy with explanations significantly improved student learning performance more than the Expert policy; and 2) no significant differences were found between the Expert policy and student decision making. Overall, our results suggest that \textit{pairing simple explanations with the Batch DRL policy} can be an important and effective technique for applying RL to real-life, human-centric tasks. 

This article provides an evidence-based discussion of an ongoing effort within the operations of hunger relief organizations to address diversity, equity, and inclusion (DEI) by sourcing and distributing more culturally relevant food. Through nearly 100 interviews with food bank personnel in diverse roles (from partner agency relations to executives) representing various regions of the United States, we explore the challenges faced by different functional units within the organization. These interviews indicate a shift to more inclusive language, more personalized metrics, and more inclusive operations. We critically analyze the related literature and identify opportunities for infusing DEI practices in the study of hunger relief supply chains.