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Showing 1 to 15 of 23 results Save | Export
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Gao, Zhikai; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany – International Educational Data Mining Society, 2021
As Computer Science has increased in popularity so too have class sizes and demands on faculty to provide support. It is therefore more important than ever for us to identify new ways to triage student questions, identify common problems, target students who need the most help, and better manage instructors' time. By analyzing interaction data…
Descriptors: Automation, Classification, Help Seeking, Computer Science Education
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Sense, Florian; Krusmark, Michael; Fiechter, Joshua; Collins, Michael G.; Sanderson, Lauren; Onia, Joshua; Jastrzembski, Tiffany – International Educational Data Mining Society, 2021
Cardiopulmonary resuscitation (CPR) is a foundational lifesaving skill for which medical personnel are expected to be proficient. Frequent refresher training is needed to prevent the involved skills from decaying. Regular low-dose, high-frequency training for staff at fixed intervals has proven successful at maintaining CPR competence but does not…
Descriptors: First Aid, Training, Artificial Intelligence, Prediction
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Wan, Qian; Crossley, Scott; Banawan, Michelle; Balyan, Renu; Tian, Yu; McNamara, Danielle; Allen, Laura – International Educational Data Mining Society, 2021
The current study explores the ability to predict argumentative claims in structurally-annotated student essays to gain insights into the role of argumentation structure in the quality of persuasive writing. Our annotation scheme specified six types of argumentative components based on the well-established Toulmin's model of argumentation. We…
Descriptors: Essays, Persuasive Discourse, Automation, Identification
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Rüdian, Sylvio; Pinkwart, Niels – International Educational Data Mining Society, 2021
Finding the optimal topic sequence of online courses requires experts with lots of knowledge about taught topics. Having a good order is necessary for a good learning experience. By using educational recommender systems across different platforms we have the problem that the connection to an ontology sometimes does not exist. Thus, the state of…
Descriptors: Online Courses, Sequential Approach, Educational Technology, Computer Uses in Education
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Wang, Chunpai; Zhao, Siqian; Sahebi, Shaghayegh – International Educational Data Mining Society, 2021
The state of the art knowledge tracing approaches mostly model student knowledge using their performance in assessed learning resource types, such as quizzes, assignments, and exercises, and ignore the non-assessed learning resources. However, many student activities are non-assessed, such as watching video lectures, participating in a discussion…
Descriptors: Models, Knowledge Level, Artificial Intelligence, Computer Uses in Education
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Zhao, Siqian; Wang, Chunpai; Sahebi, Shaghayegh – International Educational Data Mining Society, 2020
Students acquire knowledge as they interact with a variety of learning materials, such as video lectures, problems, and discussions. Modeling student knowledge at each point during their learning period and understanding the contribution of each learning material to student knowledge are essential for detecting students' knowledge gaps and…
Descriptors: Learning, Knowledge Level, Models, Instructional Materials
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Xiao, Yunkai; Zingle, Gabriel; Jia, Qinjin; Akbar, Shoaib; Song, Yang; Dong, Muyao; Qi, Li; Gehringer, Edward – International Educational Data Mining Society, 2020
Peer assessment adds value when students provide "helpful" feedback to their peers. But, this begs the question of how we determine "helpfulness." One important aspect is whether the review detects problems in the submitted work. To recognize problem detection, researchers have employed NLP and machine-learning text…
Descriptors: Peer Evaluation, Problems, Identification, Natural Language Processing
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Jia, Qinjin; Cui, Jialin; Xiao, Yunkai; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – International Educational Data Mining Society, 2021
Peer assessment has been widely applied across diverse academic fields over the last few decades, and has demonstrated its effectiveness. However, the advantages of peer assessment can only be achieved with high-quality peer reviews. Previous studies have found that high-quality review comments usually comprise several features (e.g., contain…
Descriptors: Peer Evaluation, Models, Artificial Intelligence, Evaluation Methods
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Quy, Tai Le; Roy, Arjun; Friege, Gunnar; Ntoutsi, Eirini – International Educational Data Mining Society, 2021
Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a "fair-representation" of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make…
Descriptors: Cluster Grouping, Artificial Intelligence, Mathematics, Computer Uses in Education
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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
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Slater, Stefan; Baker, Ryan S.; Wang, Yeyu – International Educational Data Mining Society, 2020
Feature engineering, the construction of contextual and relevant features from system log data, is a crucial component of developing robust and interpretable models in educational data mining contexts. The practice of feature engineering depends on domain experts and system developers working in tandem in order to creatively identify actions and…
Descriptors: Data Analysis, Engineering, Classification, Models
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Aulck, Lovenoor; Nambi, Dev; West, Jevin – International Educational Data Mining Society, 2020
Effectively estimating student enrollment and recruiting students is critical to the success of any university. However, despite having an abundance of data and researchers at the forefront of data science, traditional universities are not fully leveraging machine learning and data mining approaches to improve their enrollment management…
Descriptors: Resource Allocation, Scholarships, Artificial Intelligence, Data Analysis
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Mu, Tong; Jetten, Andrea; Brunskill, Emma – International Educational Data Mining Society, 2020
In some computerized educational systems, there is evidence of students "wheel-spinning," where a student tries and repeatedly fails at an educational task for learning a skill. This may be particularly concerning in low resource settings. Prior research has focused on predicting and modeling wheel-spinning, but there has been little…
Descriptors: Computer Uses in Education, Artificial Intelligence, Academic Failure, Automation
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Pugh, Samuel L.; Subburaj, Shree Krishna; Rao, Arjun Ramesh; Stewart, Angela E. B.; Andrews-Todd, Jessica; D'Mello, Sidney K. – International Educational Data Mining Society, 2021
We investigated the feasibility of using automatic speech recognition (ASR) and natural language processing (NLP) to classify collaborative problem solving (CPS) skills from recorded speech in noisy environments. We analyzed data from 44 dyads of middle and high school students who used videoconferencing to collaboratively solve physics and math…
Descriptors: Problem Solving, Cooperation, Middle School Students, High School Students
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Hsiao, I-Han, Ed.; Sahebi, Shaghayegh, Ed.; Bouchet, Francois, Ed.; Vie, Jill-Jenn, Ed. – International Educational Data Mining Society, 2021
For this 14th iteration of the International Conference on Educational Data Mining (EDM 2021), the conference was held completely online. EDM is organized under the auspices of the International Educational Data Mining Society and was meant to happen in Paris, France. The official theme of this year's conference was Shifting Landscape of…
Descriptors: Blended Learning, Distance Education, Learning Analytics, Educational Technology
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