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Dear colleagues,

Some of you—especially those with an interest in predictive learning analytics and educational data mining—might be interested to know that the April-June 2019 issue (vol. 12, no. 2) of the IEEE Transactions on Learning Technologies (TLT) has now been published. This is a themed special issue on "Early Prediction and Supporting of Learning Performance,” guest edited by Prof. Cristóbal Romero and Prof. Sebastian Ventura of the University of Córdoba, Spain.

The Guest Editorial to the special issue is free to download at https://doi.org/10.1109/TLT.2019.2908106 and provides an overview of the 11 articles that constitute the issue. The full issue is available through IEEE Xplore at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076 (Click on the "Current Issue" link). Most university libraries have subscriptions to TLT via the IEEE Xplore Digital Library, so you should have access if you are using a device connected to your campus network.

For your convenience, a listing of the issue's contents is appended below.

Our next special issue, which will likely be published in early 2020, is on the topic “Data Capture and Analysis for Supporting Learning Engagement.” The Guest Editors of that issue are Dr. Ilaria Torre of the University of Genoa, Italy; Dr. Olga C. Santos of UNED, Spain; and Prof. Abelardo Pardo of the University of South Australia.

Please feel free to share this message with others who might be interested, both individually and through the networks and associations of which you are a part.

Happy reading!

Kind regards,


Mark J. W. Lee
Editor-in-Chief
IEEE Transactions on Learning Technologies


*** IEEE Transactions on Learning Technologies Special Issue on Early Prediction and Supporting of Learning Performance ***

Guest Editorial: Special Issue on Early Prediction and Supporting of Learning Performance (OPEN ACCESS)
Cristóbal Romero, Sebastian Ventura
https://doi.org/10.1109/TLT.2019.2908106

Improving Predictive Modeling for At-Risk Student Identification: A Multistage Approach
Jui-Long Hung, Brett E. Shelton, Juan Yang, Xu Du
https://doi.org/10.1109/TLT.2019.2911072

Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning
Anthony F. Botelho, Ashvini Varatharaj, Thanaporn Patikorn, Diana Doherty, Seth A. Adjei, Joseph E. Beck
https://doi.org/10.1109/TLT.2019.2912162

A Quest for a One-Size-Fits-All Neural Network: Early Prediction of Students at Risk in Online Courses
David Monllaó Olivé, Du Q. Huynh, Mark Reynolds, Martin Dougiamas, Damyon Wiese
https://doi.org/10.1109/TLT.2019.2911068 

How Widely Can Prediction Models Be Generalized? Performance Prediction in Blended Courses
Niki Gitinabard, Yiqiao Xu, Sarah Heckman, Tiffany Barnes, Collin F. Lynch
https://doi.org/10.1109/TLT.2019.2911832

Interpretable Multiview Early Warning System Adapted to Underrepresented Student Populations
Alberto Cano, John D. Leonard
https://doi.org/10.1109/TLT.2019.2911079
 
Multiview Learning for Early Prognosis of Academic Performance: A Case Study
Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis
https://doi.org/10.1109/TLT.2019.2911581

Predicting the Risk of Academic Dropout With Temporal Multi-Objective Optimization
Fernando Jiménez, Alessia Paoletti, Gracia Sánchez, Guido Sciavicco
https://doi.org/10.1109/TLT.2019.2911070

Feature Extraction for Next-Term Prediction of Poor Student Performance
Agoritsa Polyzou, George Karypis
https://doi.org/10.1109/TLT.2019.2913358

An Early Feedback Prediction System for Learners At-Risk Within a First-Year Higher Education Course
David Baneres, M. Elena Rodríguez-Gonzalez, Montse Serra
https://doi.org/10.1109/TLT.2019.2912167

From Lab to Production: Lessons Learnt and Real-Life Challenges of an Early Student-Dropout Prevention System
Alvaro Ortigosa, Rosa M. Carro, Javier Bravo-Agapito, David Lizcano, Juan Jesús Alcolea, Óscar Blanco
https://doi.org/10.1109/TLT.2019.2911608

Pedagogical Intervention Practices: Improving Learning Engagement Based on Early Prediction
Han Wan, Kangxu Liu, Qiaoye Yu, Xiaopeng Gao
https://doi.org/10.1109/TLT.2019.2911284

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