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…

We propose an innovative, effective, and data-agnostic method to train a deep-neural network model with an extremely small training dataset, called VELR (Voting-based Ensemble Learning with Rejection). In educational research and practice, providing valid labels for a sufficient amount of data to be used for supervised learning can be very costly…

Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners' future performance. This is…

Teachers often rely on the use of a range of open-ended problems to assess students' understanding of mathematical concepts. Beyond traditional conceptions of student open-ended work, commonly in the form of textual short-answer or essay responses, the use of figures, tables, number lines, graphs, and pictographs are other examples of open-ended…

This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for…

There is a growing need to empirically evaluate the quality of online instructional interventions at scale. In response, some online learning platforms have begun to implement rapid A/B testing of instructional interventions. In these scenarios, students participate in series of randomized experiments that evaluate problem-level interventions in…

Vocabulary proficiency diagnosis plays an important role in the field of language learning, which aims to identify the level of vocabulary knowledge of a learner through his or her learning process periodically, and can be used to provide personalized materials and feedback in language-learning applications. Traditional approaches are widely…

In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design and which is based on the explainable k-nearest neighbor algorithm,…

Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term…

Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to "detector rot." We compare the…

Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…

Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond…

We explore multi-modal machine learning-based approaches (facial expression recognition, auditory emotion recognition, and text sentiment analysis) to identify "negative moments" of teacher-student interaction during classroom teaching. Our analyses on a large (957 videos, each 20min) dataset of classroom observations suggest that: (1)…