The complex and dynamic nature of collaboration makes it challenging to find indicators of productive learning and quality collaboration. This exploratory study developed a collaboration metric to capture temporal patterns of joint attention (JA) based on log files generated as students interacted with an immersive astronomy simulation using…

In this paper, we describe a theoretically-grounded data mining approach to identify types of collaborative problem solvers based on students' interactions with an online simulation-based task about electronics concepts. In our approach, we developed an ontology to identify the theoretically-grounded features of collaborative problem solving…

Front camera data from tablets used in educational settings offer valuable clues to student behavior, attention, and affective state. Due to the camera's angle of view, the face of the student is partially occluded and skewed. This hinders the ability of experts to adequately capture the learning process and student states. In this paper, we…

Problem-solving skills in creative, open-ended domains are both important and little understood. These domains are generally ill-structured, have extremely large exploration spaces, and require high levels of specialized skill in order to produce quality solutions. We investigate problem-solving behavior in one such domain, the…

This paper attempts to quantify the accuracy limit of "nextitem-correct" prediction by using numerical optimization to estimate the student's probability of getting each question correct given a complete sequence of item responses. This optimization is performed without an explicit parameterized model of student behavior, but with the…

One of the most challenging tasks in the field of Educational Data Mining (EDM) is to cluster students directly based on system-student sequential moment-to-moment interactive trajectories. The objective of this study is to build a general temporal clustering framework that captures the distinct characteristics of students' sequential behaviors…

Educational games have become hugely popular, and educational data mining has been used to predict student performance in the context of these games. However, models built on student behavior in educational games rarely differentiate between the types of problem solving that students employ and fail to address how efficacious student problem…

A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…

Online programming discussion forums have grown increasingly and have formed sizable repositories of problem solving-solutions. In this paper, we investigate programming learners' information seeking behaviors from online discussion forums. We design engines to collect students' information seeking processes, including query formulation,…

During the semester break, 36 second-grade students accessed a set of resources and completed a series of online math activities focused on the application of the model method for arithmetic in two contexts 1) addition/subtraction and 2) multiplication/division. The learning environment first modeled and then supported the use of a scripted series…

The phenomenon of wheel spinning refers to students attempting to solve problems on a particular skill, but becoming stuck due to an inability to learn the skill. Past research has found that students who do not master a skill quickly tend not to master it at all. One question is why do students wheel spin? A plausible hypothesis is that students…

This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…

Inquiry skills are an important part of science education standards. There has been particular interest in verifying that these skills can transfer across domains and instructional contexts [4,15,16]. In this paper, we study transfer of inquiry skills, and the effects of prior practice of inquiry skills, using data from over 2000 middle school…

The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…

The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…