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Magooda, Ahmed; Litman, Diane – Grantee Submission, 2021
This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data. We introduce a method of data synthesis with paraphrasing, a data augmentation technique with sample mixing, and curriculum learning with two new difficulty…
Descriptors: Data Analysis, Synthesis, Documentation, Models
Philip I. Pavlik; Luke G. Eglington – Grantee Submission, 2023
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…
Descriptors: Students, Models, Regression (Statistics), Alternative Assessment
Philip I. Pavlik; Luke G. Eglington – International Educational Data Mining Society, 2023
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…
Descriptors: Students, Models, Regression (Statistics), Alternative Assessment
Adam C. Sales; Ethan Prihar; Johann Gagnon-Bartsch; Ashish Gurung; Neil T. Heffernan – Grantee Submission, 2022
Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and…
Descriptors: Research Methodology, Educational Experiments, Causal Models, Computation
Magooda, Ahmed; Elaraby, Mohamed; Litman, Diane – Grantee Submission, 2021
This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target…
Descriptors: Data Analysis, Synthesis, Documentation, Training
Bulathwela, Sahan; Verma, Meghana; Pérez-Ortiz, María; Yilmaz, Emine; Shawe-Taylor, John – International Educational Data Mining Society, 2022
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…
Descriptors: Video Technology, Lecture Method, Data Analysis, Prediction
Cai, Zhiqiang; Siebert-Evenstone, Amanda; Eagan, Brendan; Shaffer, David Williamson – Grantee Submission, 2021
When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of…
Descriptors: Coding, Artificial Intelligence, Models, Probability
Hutt, Stephen; Das, Sanchari; Baker, Ryan S. – International Educational Data Mining Society, 2023
The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two…
Descriptors: Data Analysis, Decision Making, Data Collection, Foreign Countries
Marwan, Samiha; Shi, Yang; Menezes, Ian; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2021
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…
Descriptors: Expertise, Models, Feedback (Response), Identification
Moore, Russell; Caines, Andrew; Elliott, Mark; Zaidi, Ahmed; Rice, Andrew; Buttery, Paula – International Educational Data Mining Society, 2019
Educational systems use models of student skill to inform decision-making processes. Defining such models manually is challenging due to the large number of relevant factors. We propose learning multidimensional representations (embeddings) from student activity data -- these are fixed-length real vectors with three desirable characteristics:…
Descriptors: Models, Knowledge Representation, Skills, Artificial Intelligence
Jiang, Weijie; Pardos, Zachary A. – International Educational Data Mining Society, 2020
Data mining of course enrollment and course description records has soared as institutions of higher education begin tapping into the value of these data for academic and internal research purposes. This has led to a more than doubling of papers on course prediction tasks every year. The papers often center around a single prediction task and…
Descriptors: Course Descriptions, Models, Prediction, Course Selection (Students)
Chaudhry, Ritwick; Singh, Harvineet; Dogga, Pradeep; Saini, Shiv Kumar – International Educational Data Mining Society, 2018
Interactive learning environments facilitate learning by providing hints to fill the gaps in the understanding of a concept. Studies suggest that hints are not used optimally by learners. Either they are used unnecessarily or not used at all. It has been shown that learning outcomes can be improved by providing hints when needed. An effective…
Descriptors: Student Behavior, Prediction, Models, Intelligent Tutoring Systems
Švábenský, Valdemar; Baker, Ryan S.; Zambrano, Andrés; Zou, Yishan; Slater, Stefan – International Educational Data Mining Society, 2023
Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and…
Descriptors: Generalization, Computer Mediated Communication, MOOCs, State Universities
Chung, Cheng-Yu; Hsiao, I-Han – International Educational Data Mining Society, 2021
The distributed practice effect suggests that students retain learning content better when they pace their practice over time. The key factors are practice dosage (intensity) and timing (when to practice and how in between). Inspired by the thriving development of image recognition, this study adopts one of the successful techniques,…
Descriptors: Models, Drills (Practice), Pacing, Computer Uses in Education
Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas – International Educational Data Mining Society, 2023
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)…
Descriptors: Computer Science Education, Data Analysis, Programming, Coding