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Shuanghong Shen; Qi Liu; Zhenya Huang; Yonghe Zheng; Minghao Yin; Minjuan Wang; Enhong Chen – IEEE Transactions on Learning Technologies, 2024
Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In…
Descriptors: Student Behavior, Electronic Learning, Data Analysis, Models
Xiao, Hui; Hu, Wenshan; Liu, Guo-Ping – IEEE Transactions on Learning Technologies, 2023
In conventional laboratories, engineering students must attend in person to conduct experiments with real equipment in a physical place, where their work is mainly assessed through self-reports and attendance records. By comparison, online labs can record and analyze students' activities and behaviors automatically. Thus, this article proposes a…
Descriptors: Electronic Learning, Science Laboratories, Engineering Education, Distance Education
Ntourmas, Anastasios; Dimitriadis, Yannis; Daskalaki, Sophia; Avouris, Nikolaos – IEEE Transactions on Learning Technologies, 2022
One of the main challenges of massive open online courses (MOOCs) is the effective facilitation of learners in the course forum. The more learners participating in the forum, the more difficult it is for instructors to provide timely support. The effective intervention of teaching assistants (TAs) can play a crucial role in mitigating this issue;…
Descriptors: Online Courses, Teaching Assistants, Large Group Instruction, Electronic Learning
Kim, Hodam; Chae, Younsoo; Kim, Suhye; Im, Chang-Hwan – IEEE Transactions on Learning Technologies, 2023
Owing to the rapid development of information and communication technologies, online or mobile learning content is widely available on the Internet. Unlike traditional face-to-face learning, online learning exhibits a critical limitation: real-time interactions between learners and teachers are generally not feasible in online learning. To…
Descriptors: College Students, Control Groups, Attention, Comprehension
Nabizadeh, Amir Hossein; Goncalves, Daniel; Gama, Sandra; Jorge, Joaquim – IEEE Transactions on Learning Technologies, 2022
The main challenge in higher education is student retention. While many methods have been proposed to overcome this challenge, early and continuous feedback can be very effective. In this article, we propose a method for predicting student final grades in a course using only their performance data in the current semester. It assists students in…
Descriptors: College Students, Prediction, Grades (Scholastic), Game Based Learning
Atapattu, Thushari; Falkner, Katrina; Thilakaratne, Menasha; Sivaneasharajah, Lavendini; Jayashanka, Rangana – IEEE Transactions on Learning Technologies, 2020
The substantial growth of online learning, and in particular, through massively open online courses (MOOCs), supports research into nontraditional learning contexts. Learners' confusion is one of the identified aspects which impact the overall learning process, and ultimately, course attrition. Confusion for a learner is an individual state of…
Descriptors: Electronic Learning, Online Courses, Psychological Patterns, Learning Processes
Che, Xiaoyin; Yang, Haojin; Meinel, Christoph – IEEE Transactions on Learning Technologies, 2018
Textbook highlighting is widely considered to be beneficial for students. In this paper, we propose a comprehensive solution to highlight the online lecture videos in both sentence- and segment-level, just as is done with paper books. The solution is based on automatic analysis of multimedia lecture materials, such as speeches, transcripts, and…
Descriptors: Online Courses, Comparative Analysis, Lecture Method, Multimedia Materials
Moreno-Marcos, Pedro Manuel; Alario-Hoyos, Carlos; Munoz-Merino, Pedro J.; Kloos, Carlos Delgado – IEEE Transactions on Learning Technologies, 2019
This paper surveys the state of the art on prediction in MOOCs through a systematic literature review (SLR). The main objectives are: first, to identify the characteristics of the MOOCs used for prediction, second, to describe the prediction outcomes, third, to classify the prediction features, fourth, to determine the techniques used to predict…
Descriptors: Prediction, Large Group Instruction, Online Courses, Educational Research
Uto, Masaki; Nguyen, Duc-Thien; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2020
With the wide spread large-scale e-learning environments such as MOOCs, peer assessment has been popularly used to measure the learner ability. When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner's assessment workload. However, in such cases, the peer assessment…
Descriptors: Item Response Theory, Electronic Learning, Peer Evaluation, Accuracy
Brinton, Christopher G.; Rill, Ruediger; Ha, Sangtae; Chiang, Mung; Smith, Robert; Ju, William – IEEE Transactions on Learning Technologies, 2015
We present the design, implementation, and preliminary evaluation of our Adaptive Educational System (AES): the Mobile Integrated and Individualized Course (MIIC). MIIC is a platform for personalized course delivery which integrates lecture videos, text, assessments, and social learning into a mobile native app, and collects clickstream-level…
Descriptors: Individualized Instruction, Electronic Learning, Online Courses, Student Attitudes
Alario-Hoyos, Carlos; Pérez-Sanagustin, Mar; Delgado-Kloos, Carlos; Parada G., Hugo A.; Muñoz-Organero, Mario – IEEE Transactions on Learning Technologies, 2014
This paper presents an in-depth empirical analysis of a nine-week MOOC. This analysis provides novel results regarding participants' profiles and use of built-in and external social tools. The results served to detect seven participants' patterns and conclude that the forum was the social tool preferred to contribute to the MOOC.
Descriptors: Profiles, Online Courses, Computer Science Education, Distance Education
Auvinen, Tapio; Hakulinen, Lasse; Malmi, Lauri – IEEE Transactions on Learning Technologies, 2015
In online learning environments where automatic assessment is used, students often resort to harmful study practices such as procrastination and trial-and-error. In this paper, we study two teaching interventions that were designed to address these issues in a university-level computer science course. In the first intervention, we used achievement…
Descriptors: Student Behavior, Electronic Learning, Online Courses, Computer Assisted Testing
Grunewald, Franka; Meinel, Christoph – IEEE Transactions on Learning Technologies, 2015
The use of video lectures in distance learning involves the two major problems of searchability and active user participation. In this paper, we promote the implementation and usage of a collaborative educational video annotation functionality to overcome these two challenges. Different use cases and requirements, as well as details of the…
Descriptors: Web Based Instruction, Lecture Method, Video Technology, Notetaking
Anwar, M.; Greer, J. – IEEE Transactions on Learning Technologies, 2012
This research explores a new model for facilitating trust in online e-learning activities. We begin by protecting the privacy of learners through identity management (IM), where personal information can be protected through some degree of participant anonymity or pseudonymity. In order to expect learners to trust other pseudonymous participants,…
Descriptors: Computer Mediated Communication, Discussion, Client Server Architecture, Online Courses
Cocea, M.; Weibelzahl, S. – IEEE Transactions on Learning Technologies, 2011
Learning environments aim to deliver efficacious instruction, but rarely take into consideration the motivational factors involved in the learning process. However, motivational aspects like engagement play an important role in effective learning-engaged learners gain more. E-Learning systems could be improved by tracking students' disengagement…
Descriptors: Prediction, Electronic Learning, Online Courses, Delivery Systems