About |
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This Special Issue aims to present high-quality, high-impact, original research results reporting the current state of the art of online education systems empowered with artificial intelligence (e.g., machine/deep learning). We are interested in submissions covering different levels of the experimental pipeline, including but not limited to data collection, computational models, and applicative systems. We also invite prospective authors to share experience with dealing with online education in these months of COVID-19 emergency, technological changes that happened at the institution, and impact of the devised intelligent systems in their ecosystems. |
Call for Papers |
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We are interested in contributions targeting automated intelligent support in online education applications, focused but not limited to the following areas. We seek to receive papers that clearly state and contextualize how the proposed intelligent system or tool is integrated in the real-world scenario and concretely supports stakeholders during decision-making. If in doubt about suitability, please contact the Guest Editors. ● Data Set Collection○ New tools and systems for capturing educational data (e.g., eye-tracking, motion, physiological, etc.). ○ Proposals of procedures and tools to store, share and preserve learning and teaching traces. ○ Annotation standards and schemas for data that can be leveraged for machine learning. ○ Collecting and sharing data sets useful for applying machine learning in online education contexts. ● Model, Tool, and System Design○ Semantic-based retrieval of instructional materials to identify appropriate contents. ○ Tools for adaptive question-answering and dialogue or automatically generating test questions. ○ Personalized support tools and systems for communities of learners (e.g., recommendation). ○ Content analysis for exam scoring and/or assessment. ○ Behavioral and physiological analysis of learners while interacting in online education platforms. ○ Student engagement assessment via machine-learning techniques (e.g., sentiment analysis). ○ Systems that detect and/or adapt the platform to sentiment or emotional states of learners. ○ Techniques to provide automated proctoring support during online examinations. ○ Tools able to predict the dropout risk of learners along the educational path. ● Evaluation Protocol Design and Conduction○ Evaluation techniques relying on computational analyses in online education contexts. ○ Interpretability and/or fairness of the models and the resulting impact on real-world adoption. ○ Error analysis devoted to understanding, measuring, and managing uncertainty in model design. ○ Strategies to evaluate effectiveness and impact of intelligent systems on educational environments. ○ Exploration of cognition, affect, motivation, and attitudes of stakeholders, while deploying systems. ● Ethics and Privacy Investigation○ Analysis of issues and approaches to the lawful and ethical use of intelligent systems. ○ Tackling unintended bias and value judgements in intelligent systems. ○ Regulations and policies in data management ensuring privacy while designing intelligent systems. ○ Broad discussion on potential and pitfalls of intelligent systems for educational contexts. ○ Studies on how teachers can be made part of the loop as moderators instead of being replaced |
Credits and Sources |
| [1] Int Sys Edu 2021 : Special Issue on Advances in Intelligent Systems for Online Education |
