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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
Syed, Munira; Chetlur, Malolan; Afzal, Shazia; Ambrose, G. Alex; Chawla, Nitesh V. – International Educational Data Mining Society, 2019
Understanding the affect expressed by learners is essential for enriching the learning experience in Massive Open Online Courses (MOOCs). However, online learning environments, especially MOOCs, pose several challenges in understanding the different types of affect experienced by a learner. In this paper, we define two categories of emotions,…
Descriptors: Online Courses, Electronic Learning, Affective Behavior, Emotional Response
Ishola, Oluwabukola Mayowa; McCalla, Gordon – International Educational Data Mining Society, 2017
Question and answer forums are becoming more popular as increasing numbers of lifelong learners rely on such forums to receive help about their learning needs. Stack Overflow (SO) is an example of such a forum used by millions of programmers. The ability of users to receive timely answers to questions is crucial to the sustainability of such…
Descriptors: Prediction, Peer Relationship, Lifelong Learning, Interviews
Mi, Fei; Faltings, Boi – International Educational Data Mining Society, 2017
Massive open online courses (MOOCs) have demonstrated growing popularity and rapid development in recent years. Discussion forums have become crucial components for students and instructors to widely exchange ideas and propagate knowledge. It is important to recommend helpful information from forums to students for the benefit of the learning…
Descriptors: Online Courses, Sequential Approach, Discussion Groups, Student Interests
Gitinabard, Niki; Barnes, Tiffany; Heckman, Sarah; Lynch, Collin F. – International Educational Data Mining Society, 2019
Students' interactions with online tools can provide us with insights into their study and work habits. Prior research has shown that these habits, even as simple as the number of actions or the time spent on online platforms can distinguish between the higher performing students and low-performers. These habits are also often used to predict…
Descriptors: Blended Learning, Student Adjustment, Online Courses, Study Habits
Wang, Xu; Yang, Diyi; Wen, Miaomiao; Koedinger, Kenneth; Rosé, Carolyn P. – International Educational Data Mining Society, 2015
While MOOCs undoubtedly provide valuable learning resources for students, little research in the MOOC context has sought to evaluate students' learning gains in the environment. It has been long acknowledged that conversation is a significant way for students to construct knowledge and learn. However, rather than studying learning in MOOC…
Descriptors: Online Courses, Discussion Groups, Student Behavior, Cognitive Processes
Bergner, Yoav; Kerr, Deirdre; Pritchard, David E. – International Educational Data Mining Society, 2015
Determining how learners use MOOCs effectively is critical to providing feedback to instructors, schools, and policy-makers on this highly scalable technology. However, drawing inferences about student learning outcomes in MOOCs has proven to be quite difficult due to large amounts of missing data (of various kinds) and to the diverse population…
Descriptors: Online Courses, Data Analysis, Discussion Groups, Outcomes of Education
Crossley, Scott; McNamara, Danielle S.; Baker, Ryan; Wang, Yuan; Paquette, Luc; Barnes, Tiffany; Bergner, Yoav – International Educational Data Mining Society, 2015
Completion rates for massive open online classes (MOOCs) are notoriously low, but learner intent is an important factor. By studying students who drop out despite their intent to complete the MOOC, it may be possible to develop interventions to improve retention and learning outcomes. Previous research into predicting MOOC completion has focused…
Descriptors: Online Courses, Large Group Instruction, Information Retrieval, Data Analysis
Lopez, M. I.; Luna, J. M.; Romero, C.; Ventura, S. – International Educational Data Mining Society, 2012
This paper proposes a classification via clustering approach to predict the final marks in a university course on the basis of forum data. The objective is twofold: to determine if student participation in the course forum can be a good predictor of the final marks for the course and to examine whether the proposed classification via clustering…
Descriptors: Classification, Prediction, Grades (Scholastic), College Freshmen