Feedback on how students progress through completing subgoals can improve students' learning and motivation in programming. Detecting subgoal completion is a challenging task, and most learning environments do so either with "expert-authored" models or with "data-driven" models. Both models have advantages that are…

This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to…

This study examined the predictive power of students' demographic characteristics, reading attitudes, school characteristics, and teacher-informed reading activities on three metacognitive reading skills: understanding and remembering, summarizing, and assessing credibility and their influence on 15-year-old students' reading scores. The dataset…

Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced…

Despite calls to increase the focus on explainability and interpretability in EDM and, in particular, student success prediction, so that it becomes useful for personalized intervention systems, only few efforts have been undertaken in that direction so far. In this paper, we argue that this is mainly due to the limitations of current Explainable…

This project aims to gain insight into the mechanisms by which schools in highly challenging environments avoided learning loss--or even improved--during the pandemic. Using a unique dataset covering multiple levels of school, health, and environmental data, we examine which factors led schools to 'beat the odds' when it comes to learning…

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…

The application of item response theory (IRT) is almost universal in the development, implementation, and maintenance of large-scale assessments. Therefore, establishing the fit of IRT models to data is essential as the viability of calibration and equating implementations depend on it. In a typical test administration situation, measurement…

Lawson (1983a) offered that an individuals' experiences as students in schools are significant in shaping their views of teaching. Pajares (1992) argued that a teacher's perceptions of the classroom are a product of their experiences as students. The purpose of this study was to examine the relationship between physical education (PE) pre-service…

The intangible concept of data, as part of statistical literacy, can be complex for young children to grasp. Inquiry as a pedagogy has potential for supporting student development of statistical literacy as the investigation process is driven by the inquiry question. The aim of this paper is to gain insight into how a teacher's communication…

ITSs have the requirement to be adaptive to the student with AI. The classical ITS architecture defines three components to split the data and to keep it flexible and thus adaptive. However, there is a lack of abstract descriptions how to put adaptive behavior into practice. This paper defines how you can structure your data for case based systems…

Supervised machine learning has become one of the most important methods for developing educational and intelligent tutoring software; it is the backbone of many educational data mining methods for estimating knowledge, emotion, and other aspects of learning. Hence, in order to ensure optimal utilization of computing resources and effective…

The State Higher Education Executive Officers Association's (SHEEO) "Communities of Practice" project builds upon SHEEO's ongoing efforts to measure the capacity and effective use of state postsecondary data systems and provides states with opportunities to develop solutions to common issues with those systems. The sixth Community of…

The demand for privacy specialists is expected to increase, but there is a shortage of them to meet market demands. Certain ICT skills and competencies are required for professionals who develop, manage, and protect data that drive the digital world. The current study explores undergraduate students' attitude about different teaching strategies…

In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of…