Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 7 |
Descriptor
Source
| Interactive Learning… | 7 |
Author
| Aguilar, J. | 1 |
| Akbar Bahari | 1 |
| Bashir, Faiza | 1 |
| Bei Fang | 1 |
| Buendia, O. | 1 |
| Chen, Chih-Ming | 1 |
| Chi-Tung Chen | 1 |
| Chih-Ming Chen | 1 |
| Gutiérrez, J. | 1 |
| Hao Zhang | 1 |
| Hsiao-Ting Tsai | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 7 |
| Reports - Research | 5 |
| Information Analyses | 1 |
| Reports - Evaluative | 1 |
Education Level
| High Schools | 3 |
| Secondary Education | 3 |
| Grade 11 | 1 |
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
| Taiwan | 2 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Bashir, Faiza; Warraich, Nosheen Fatima – Interactive Learning Environments, 2023
The study intends to identify the emerging themes of e-learning through the Semantic Web. To highlight the key challenges and explore the core benefits of this mesmerizing technology was another goal of the study. To get the set goals researchers systematically reviewed the literature in five leading databases, out of which one specialized…
Descriptors: Electronic Learning, Technology Uses in Education, Internet, Semantics
Jing Chen; Bei Fang; Hao Zhang; Xia Xue – Interactive Learning Environments, 2024
High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…
Descriptors: MOOCs, Potential Dropouts, Prediction, Artificial Intelligence
Ünal Çakiroglu; Seval Bilgi – Interactive Learning Environments, 2024
The aim of this explanatory study is to identify the causes of intrinsic cognitive load in programming process. For this purpose, a method based on two dimensions; programming knowledge types (syntactic, semantic, and strategic) and programming constructs was proposed. The proposed method was tested with high school students enrolled in Computer…
Descriptors: Cognitive Processes, Difficulty Level, Programming, Interaction
Akbar Bahari; Rui Li – Interactive Learning Environments, 2024
Given the important technology-assisted language learning (TALL)-empowered affordances, a comprehensive understanding of how these TALL tools facilitate learning across different language components, e.g., phonology, morphology, syntax, semantics, and pragmatics, could not only present the panoramic scenery of the subject matter but also inform…
Descriptors: Language Acquisition, Phonology, Morphology (Languages), Syntax
Chen, Chih-Ming; Li, Ming-Chaun; Huang, Ya-Ling – Interactive Learning Environments, 2023
By applying two-mode social networks and Chinese word segmentation technologies, a novel visualization tool, the instant semantic analysis and feedback system (ISAFS), is designed in this study to present the semantic networks of co-words and non-co-words used in learners' discussion processes and assist learners in grasping the discussion…
Descriptors: Foreign Countries, High School Students, Semantics, Feedback (Response)
Chi-Tung Chen; Chih-Ming Chen; Hsiao-Ting Tsai – Interactive Learning Environments, 2024
This study utilised the instant semantic analysis and feedback system (ISAFS) to assist learners in the online discussion learning activities of socio-scientific issues (SSIs) and to document their learning process behaviours for behavioural analyses. The aim was to understand the learners' discussion behaviours during the ISAFS assisted learning…
Descriptors: Behavior Patterns, Electronic Learning, Discussion, Instructional Effectiveness
Aguilar, J.; Buendia, O.; Pinto, A.; Gutiérrez, J. – Interactive Learning Environments, 2022
Social Learning Analytics (SLA) seeks to obtain hidden information in large amounts of data, usually of an educational nature. SLA focuses mainly on the analysis of social networks (Social Network Analysis, SNA) and the Web, to discover patterns of interaction and behavior of educational social actors. This paper incorporates the SLA in a smart…
Descriptors: Learning Analytics, Cognitive Style, Socialization, Social Networks

Peer reviewed
Direct link
