Aims and Scope
The education sector is relying more and more on online learning. Educational and training institutions are being motivated to endorse online learning strategies thanks to its technical, economic, and operational feasibility. This has become even more important after the lockdown caused by the breakout of COVID-19, when even universities revised their educational strategy, moving to online teaching. Learners and teachers are benefiting from the flexibility, accessibility, and costs of learning and teaching online. Nonetheless, moving education online is bringing unprecedented challenges. For instance, learners may feel isolation online, massive content alternatives often overload learners and teachers who look for educational resources, and institutions are being challenged to ensure academic integrity in online exams. The increasing amount of learning-related data and high performance computing are enabling intelligent systems that can support stakeholders while facing current challenges. Bringing this intelligence to online education leads to a very wide range of advantages, e.g., avoiding manual error-prone tasks or providing learners with personalized guidance.
As artificial intelligence research and development is getting more mature and the corresponding outputs are being deployed at scale in real-world contexts, the crucial role of using automated systems in online education, as an additional support for stakeholders during decision making processes, becomes more evident nowadays. Current research has greatly expanded our understanding on such artificial intelligence, but there has been less work on how it applies to online education. Data, methods, tools, and applications in this area are still limited, though they promise to proliferate in next years. Further, more research and many questions remain to be answered to bridge technological, social, pedagogical, and ethical perspectives in these intelligent systems.
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.
Topics of Interest
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.
Submission Instructions
The submission system will be open for submissions to our Special Issue from September 15, 2020. When submitting your manuscript please select the article type “VSI: Int Sys Edu“. Please submit your manuscript before January 29, 2021. Please ensure you read the Guide for Authors before writing your manuscript <https://www.elsevier.com/journals/future-generation-computer-systems/0167-739x/guide-for-authors>. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage. Each manuscript can have no more than 14 pages (plus one page after revision) in double-column format, including all its content (e.g., figures, references, appendix, and so on).
All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers on the basis of relevance for the Special Issue, novelty/originality, significance, technical quality and correctness, quality and clarity of presentation, quality of references and reproducibility. The editors reserve the right to reject without review any submissions deemed to be outside the scope of the Special Issue. Authors are welcome to contact the Special Issue editors with questions about scope before preparing a submission. Once your manuscript is accepted, it will go into production, and will be published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Submissions must represent original material, that has not appeared elsewhere for publication and that is not under review for another refereed publication. If any portion of your submission has previously appeared in or will appear in a conference/workshop proceeding, you should notify this at the time of submission, make sure that the submission references the conference publication, and supply a copy of the conference version(s). Please also provide a brief description of the differences between the two manuscript (at least 30% of new material must be provided).
Important Dates
- Submission system open: September 15, 2020
- Manuscript submission due: January 29, 2021
- First round decision made: March 15, 2021
- Revised manuscript due: May 15, 2021
- Final decision made: June 15, 2021
- Final paper due: July 15, 2021
Guest Editors
Prof. Dr. Geoffray Bonnin, Université de Lorraine, Nancy, France. Email: [email protected]
Dr. Danilo Dessì, FIZ Karlsruhe Leibniz Institute for Information Infrastructure, Karlsruhe Institute of Technology AIFB, Karlsruhe, Germany. Email: [email protected]
Prof. Dr. Gianni Fenu, Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy. Email: [email protected]
Dr. Martin Hlosta, Faculty of Science, Technology, Engineering & Mathematics, The Open University, Milton Keynes, United Kingdom. Email: [email protected]
Dr. Mirko Marras – Managing Guest Editor, Digital Vocation, Education and Training (D-VET) Laboratory & Machine Learning for Education (ML4ED) Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Email: [email protected]
Prof. Dr. Harald Sack, FIZ Karlsruhe Leibniz Institute for Information Infrastructure, Karlsruhe Institute of Technology AIFB, Karlsruhe, Germany. Email: [email protected]
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