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Achilleos, Achilleas P.; Mettouris, Christos; Yeratziotis, Alexandros; Papadopoulos, George A.; Pllana, Sabri; Huber, Florian; Jager, Bernhard; Leitner, Peter; Ocsovszky, Zsofia; Dinnyes, Andras – IEEE Transactions on Learning Technologies, 2019
Scientific and technological innovations have become increasingly important as we face the benefits and challenges of both globalization and a knowledge-based economy. Still, enrolment rates in STEM degrees are low in many European countries and consequently there is a lack of adequately educated workforce in industries. We believe that this can…
Descriptors: Social Media, STEM Education, Student Motivation, Foreign Countries
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Dong, Jian-Jie; Hwang, Wu-Yuin; Shadiev, Rustam; Chen, Ginn-Yein – IEEE Transactions on Learning Technologies, 2019
In this study, we developed an on-call-tutor system to facilitate peer-help activities. The system was implemented in a face-to-face heterogeneous classroom with 119 students from different departments who were not familiar with each other. Students learned Geographic Information System (GIS) in a computer classroom in two groups: students, who…
Descriptors: Peer Teaching, Synchronous Communication, Social Networks, Friendship
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Fincham, Ed; Gasevic, Dragan; Jovanovic, Jelena; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2019
Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather…
Descriptors: Study Habits, Learning Strategies, Independent Study, Educational Research
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Ruiperez-Valiente, Jose A.; Munoz-Merino, Pedro J.; Alexandron, Giora; Pritchard, David E. – IEEE Transactions on Learning Technologies, 2019
One of the reported methods of cheating in online environments in the literature is CAMEO (Copying Answers using Multiple Existences Online), where harvesting accounts are used to obtain correct answers that are later submitted in the master account which gives the student credit to obtain a certificate. In previous research, we developed an…
Descriptors: Computer Assisted Testing, Tests, Online Courses, Identification
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Chen, Weiyu; Brinton, Christopher G.; Cao, Da; Mason-Singh, Amanda; Lu, Charlton; Chiang, Mung – IEEE Transactions on Learning Technologies, 2019
We study learning outcome prediction for online courses. Whereas prior work has focused on semester-long courses with frequent student assessments, we focus on short-courses that have single outcomes assigned by instructors at the end. The lack of performance data and generally small enrollments makes the behavior of learners, captured as they…
Descriptors: Online Courses, Outcomes of Education, Prediction, Course Content
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Lavoue, Elise; Monterrat, Baptiste; Desmarais, Michel; George, Sebastien – IEEE Transactions on Learning Technologies, 2019
In spite of their effectiveness, learning environments often fail to engage users and end up under-used. Many studies show that gamification of learning environments can enhance learners' motivation to use learning environments. However, learners react differently to specific game mechanics and little is known about how to adapt gaming features to…
Descriptors: Educational Games, Educational Environment, Learner Engagement, Time on Task
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Hernandez, Josefina; Rodriguez, Fernanda; Hilliger, Isabel; Perez-Sanagustin, Mar – IEEE Transactions on Learning Technologies, 2019
The effectiveness of remedial mathematics courses in post-secondary education has been a controversial topic for years. Higher Education institutions need their students to have basic understandings of the subjects to be imparted in the first semesters, but since they come with different backgrounds and prior knowledge, this is not always possible…
Descriptors: Online Courses, Remedial Mathematics, Adoption (Ideas), Student Attitudes
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Ellis, Robert A.; Han, Feifei; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2019
Collaboration is an increasingly important and difficult skill for graduate engineers to develop. While universities provide some measures of collaboration ability of students on graduation, there is still some dissatisfaction with the level of preparedness of students for collaborative activity in the workplace. This paper presents a case study…
Descriptors: Engineering Education, College Freshmen, Blended Learning, Cooperation
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Smith, Andy; Leeman-Munk, Samuel; Shelton, Angi; Mott, Bradford; Wiebe, Eric; Lester, James – IEEE Transactions on Learning Technologies, 2019
Science learning is inherently multimodal, with students utilizing both drawings and writings to explain observations of physical phenomena. As such assessments in science should accommodate the many ways students express their understanding, especially given evidence that understanding is distributed across both drawing and writing. In recent…
Descriptors: Elementary School Students, Elementary School Science, Science Instruction, Writing (Composition)
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Rajendran, Ramkumar; Iyer, Sridhar; Murthy, Sahana – IEEE Transactions on Learning Technologies, 2019
The importance of affective states in learning has led many Intelligent Tutoring Systems (ITS) to include students' affective states in their learner models. The adaptation and hence the benefits of an ITS can be improved by detecting and responding to students' affective states. In prior work, we have created and validated a theory-driven model…
Descriptors: Feedback (Response), Individualized Instruction, Intelligent Tutoring Systems, Psychological Patterns
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Sun, Bo; Zhu, Yunzong; Xiao, Yongkang; Xiao, Rong; Wei, Yungang – IEEE Transactions on Learning Technologies, 2019
In recent years, computerized adaptive testing (CAT) has gained popularity as an important means to evaluate students' ability. Assigning tags to test questions is crucial in CAT. Manual tagging is widely used for constructing question banks; however, this approach is time-consuming and might lead to consistency issues. Automatic question tagging,…
Descriptors: Computer Assisted Testing, Student Evaluation, Test Items, Multiple Choice Tests
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Jimenez, Fernando; Paoletti, Alessia; Sanchez, Gracia; Sciavicco, Guido – IEEE Transactions on Learning Technologies, 2019
In the European academic systems, the public funding to single universities depends on many factors, which are periodically evaluated. One of such factors is the rate of success, that is, the rate of students that do complete their course of study. At many levels, therefore, there is an increasing interest in being able to predict the risk that a…
Descriptors: Prediction, Risk, Dropouts, College Students
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Olive, David Monllao; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – IEEE Transactions on Learning Technologies, 2019
A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a…
Descriptors: Prediction, Identification, At Risk Students, Online Courses
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Hung, Jui-Long; Shelton, Brett E.; Yang, Juan; Du, Xu – IEEE Transactions on Learning Technologies, 2019
Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction…
Descriptors: Prediction, Models, At Risk Students, Identification
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Wan, Han; Liu, Kangxu; Yu, Qiaoye; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2019
Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning…
Descriptors: Educational Practices, Learner Engagement, Identification, Study Habits
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