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Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
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…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
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Akpinar, Nil-Jana; Ramdas, Aaditya; Acar, Umut – International Educational Data Mining Society, 2020
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern…
Descriptors: Learning Strategies, Blended Learning, Learning Analytics, Student Behavior
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Fonseca, Samuel C.; Pereira, Filipe Dwan; Oliveira, Elaine H. T.; Oliveira, David B. F.; Carvalho, Leandro S. G.; Cristea, Alexandra I. – International Educational Data Mining Society, 2020
As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as 'Online Judges'. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for…
Descriptors: Programming, Introductory Courses, Computer Science Education, Automation
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Goutte, Cyril; Durand, Guillaume – International Educational Data Mining Society, 2020
Learning curves are an important tool in cognitive diagnostics modeling to help assess how well students acquire new skills, and to refine and improve knowledge component models. Learning curves are typically obtained from a model estimated on real data obtained from a finite, and usually limited, sample of students. As a consequence, there is…
Descriptors: Learning, Models, Computation, Statistical Analysis
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Dermy, Oriane; Brun, Armelle – International Educational Data Mining Society, 2020
Analyzing students' activities in their learning process is an issue that has received significant attention in the educational data mining research field. Many approaches have been proposed, including the popular sequential pattern mining. However, the vast majority of the works do not focus on the time of occurrence of the events within the…
Descriptors: Learning Analytics, Time, College Freshmen, Intervals
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Ma, Boxuan; Taniguchi, Yuta; Konomi, Shin'ichi – International Educational Data Mining Society, 2020
Recommending courses to students is a fundamental and also challenging issue in the traditional university environment. Not exactly like course recommendation in MOOCs, the selection and recommendation for higher education is a non-trivial task as it depends on many factors that students need to consider. Although many studies on this topic have…
Descriptors: Course Selection (Students), College Students, Online Courses, Student Attitudes
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Yao, Mengfan; Sahebi, Shaghayegh; Behnagh, Reza Feyzi – International Educational Data Mining Society, 2020
Student procrastination, as the voluntary delay of intended work despite expecting to be worse off for the delay, is an important factor with potentially negative consequences in student well-being and learning. In online educational settings such as Massive Open Online Courses (MOOCs), the effect of procrastination is considered to be even more…
Descriptors: Large Group Instruction, Online Courses, Student Behavior, Study Habits
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Bhatanagar, Sameer; Zouaq, Amal; Desmarais, Michel C.; Charles, Elizabeth – International Educational Data Mining Society, 2020
Online "Peer Instruction" has become prevalent in many "flipped classroom" settings, yet little work has been done to examine the content students generate in such a learning environment. This study characterizes a dataset generated by an open-source, web-based homework system that prompts students to first answer questions,…
Descriptors: Peer Teaching, Electronic Learning, Educational Technology, Web Based Instruction
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Forsyth, Carol; Andrews-Todd, Jessica; Steinberg, Jonathan – International Educational Data Mining Society, 2020
Collaborative problem solving (CPS) is considered a necessary skill for students and workers in the 21st century as the advent of technology requires more and more people to frequently work in teams. In the current study, we employed theoretically-grounded data mining techniques to identify four profiles of collaborative problem solvers…
Descriptors: Teamwork, Profiles, Problem Solving, Cooperative Learning
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Zhao, Yijun; Lackaye, Bryan; Dy, Jennifier G.; Brodley, Carla E. – International Educational Data Mining Society, 2020
Accurately predicting which students are best suited for graduate programs is beneficial to both students and colleges. In this paper, we propose a quantitative machine learning approach to predict an applicant's potential performance in the graduate program. Our work is based on a real world dataset consisting of MS in CS [Master of Science in…
Descriptors: Artificial Intelligence, College Admission, Masters Programs, Professional Education
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Dhavala, Soma; Bhatia, Chirag; Bose, Joy; Faldu, Keyur; Avasthi, Aditi – International Educational Data Mining Society, 2020
A good diagnostic assessment is one that can (i) discriminate between students of different abilities for a given skill set, (ii) be consistent with ground truth data and (iii) achieve this with as few assessment questions as possible. In this paper, we explore a method to meet these objectives. This is achieved by selecting questions from a…
Descriptors: Automation, Diagnostic Tests, Test Construction, Test Items
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Xu, Liangbei; Davenport, Mark A. – International Educational Data Mining Society, 2020
The goal of knowledge tracing is to track the state of a student's knowledge as it evolves over time. This plays a fundamental role in understanding the learning process and is a key task in the development of an intelligent tutoring system. In this paper we propose a novel approach to knowledge tracing that combines techniques from matrix…
Descriptors: Artificial Intelligence, Learning Analytics, Computer Assisted Instruction, Student Evaluation
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Klose, Mark; Desai, Vasvi; Song, Yang; Gehringer, Edward – International Educational Data Mining Society, 2020
Imagine a student using an intelligent tutoring system. A researcher records the correctness and time of each of your attempts at solving a math problem, nothing more. With no names, no birth dates, no connections to the school, you would think it impossible to track the answers back to the class. Yet, class sections have been identified with no…
Descriptors: Privacy, Learning Analytics, Data Collection, Information Storage
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Agarwal, Deepak; Baker, Ryan S.; Muraleedharan, Anupama – International Educational Data Mining Society, 2020
There has been considerable interest in techniques for modelling student learning across practice problems to drive real-time adaptive learning, with particular focus on variants of the classic Bayesian Knowledge Tracing (BKT) model proposed by Corbett & Anderson, 1995. Over time researches have proposed many variants of BKT with…
Descriptors: Intelligent Tutoring Systems, Models, Skill Development, Mastery Learning
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Brahman, Faeze; Varghese, Nikhil; Bhat, Suma; Chaturvedi, Snigdha – International Educational Data Mining Society, 2020
Despite several advantages of online education, lack of effective student-instructor interaction, especially when students need timely help, poses significant pedagogical challenges. Motivated by this, we address the problems of automatically identifying posts that express confusion or urgency from Massive Open Online Course (MOOC) forums. To this…
Descriptors: Automation, Online Courses, Discussion Groups, Identification
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