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Xiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
Guiqin Liang; Chunsong Jiang; Qiuzhe Ping; Xinyi Jiang – Interactive Learning Environments, 2024
With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students…
Descriptors: Academic Achievement, Prediction, Engineering Education, Online Courses
Umer, Rahila; Susnjak, Teo; Mathrani, Anuradha; Suriadi, Lim – Interactive Learning Environments, 2023
Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…
Descriptors: Prediction, Student Behavior, Models, Academic Achievement
S. Sageengrana; S. Selvakumar; S. Srinivasan – Interactive Learning Environments, 2024
Students are termed "multitaskers," and it is likely that they easily fall prey to other subjects or topics that most interest them. They occasionally took heed or gave close and thoughtful attention to the lectures they were on. In the current educational system, our young generations receive materials from their leftovers, and their…
Descriptors: Electronic Learning, Dropouts, Student Behavior, Student Interests
Asselman, Amal; Khaldi, Mohamed; Aammou, Souhaib – Interactive Learning Environments, 2023
Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the student level leads researchers to enhance PFA by…
Descriptors: Algorithms, Artificial Intelligence, Factor Analysis, Student Behavior
Artur Strzelecki – Interactive Learning Environments, 2024
ChatGPT is an AI tool that assisted in writing, learning, solving assessments and could do so in a conversational way. The purpose of the study was to develop a model that examined the predictors of adoption and use of ChatGPT among higher education students. The proposed model was based on a previous theory of technology adoption. Seven…
Descriptors: Computer Software, Artificial Intelligence, Synchronous Communication, Technology Uses in Education
Selin Urhan; Oguzhan Gençaslan; Senol Dost – Interactive Learning Environments, 2024
ChatGPT, an artificial intelligence-supported chatbot, has become a resource in the field of education for students across various disciplines. The conversation that unfolds based on the user-generated questions and ChatGPT's responses implies the emergence of an argumentation between the student and ChatGPT. In this study, the argumentation…
Descriptors: Persuasive Discourse, Calculus, Mathematical Concepts, Artificial Intelligence
Shuai He; Yu Lu – Interactive Learning Environments, 2024
Currently, generative AI has undergone rapid development. Numerous studies have attested to the benefits of Gen AI in programming, mathematics and other disciplines. However, since Gen AI mostly uses English as the intrinsic training parameter, it is more effective in facilitating the teaching of courses that use international common notation, but…
Descriptors: Instructional Effectiveness, Technology Uses in Education, Artificial Intelligence, Humanities Instruction
Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
Premlatha, K. R.; Dharani, B.; Geetha, T. V. – Interactive Learning Environments, 2016
E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…
Descriptors: Electronic Learning, Profiles, Automation, Classification
Hsiao, Hsien-Sheng; Chang, Cheng-Sian; Lin, Chien-Yu; Hsu, Hsiu-Ling – Interactive Learning Environments, 2015
This study focused on an intelligent robot which was viewed as a language teaching/learning tool to improve children's reading ability, reading interest, and learning behavior. The iRobiQ, with its multimedia contents, was employed to encourage children to read, speak, and answer questions. Fifty-seven pre-kindergarteners participated in this…
Descriptors: Robotics, Artificial Intelligence, Teaching Methods, Reading Ability