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