There has been great progress towards Reinforcement Learning (RL) approaches that can achieve expert performance across a wide range of domains. However, researchers have not yet applied these models to learn expert models for educationally relevant tasks, such as those taught within tutoring systems and educational games. In this paper we explore…

How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports…

Constructing effective and well-balanced learning groups is important for collaborative learning. Past research explored how group formation policies affect learners' behaviors and performance. With the different classroom contexts, many group formation policies work in theory, yet their feasibility is rarely investigated in authentic class…

Deep Reinforcement Learning (DRL) has been shown to be a very powerful technique in recent years on a wide range of applications. Much of the prior DRL work took the "online" learning approach. However, given the challenges of building accurate simulations for modeling student learning, we investigated applying DRL to induce a…

Online learning has attracted a large number of participants and is increasingly becoming very popular. However, the completion rates for online learning are notoriously low. Further, unlike traditional education systems, teachers, if any, are unable to comprehensively evaluate the learning gain of each student through the online learning…

Student interactions with game-based learning environments produce a wide range of in-game problem-solving sequences. These sequences can be viewed as trajectories through a game's problem-solving space. In this paper, we present a general framework for analyzing students' problem-solving behavior in game-based learning environments by filtering…

Virtual internships are online simulations of professional practice where students play the role of interns at a fictional company. During virtual internships, participants complete activities and then submit write-ups in the form of short answers, digital notebook entries. Prior work used classifiers trained on participant data to automatically…

Studies comparing virtual and physical manipulative environments (VME and PME) in inquiry-based science learning have mostly focused on students' learning outcomes but not on the actual processes they engage in during the learning activities. In this paper, we examined experimentation strategies in an inquiry activity and their relation to…

The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…

The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…

The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…