Call for Papers

Special Issue on Intelligent and Affective Learning Environments: New Trends and Challenges

Technology is playing an increasingly crucial role in the delivery of education, which in turn is driving research into finding ever better technological solutions. Traditional Intelligent Tutoring Systems (ITS) are able to support and control student’s learning on several levels but doesn’t provide space for student-driven learning and knowledge acquisition. From this perspective, Intelligent Learning Environments and similar tutoring systems have emerged as a kind of intelligent educational system which combines the features of traditional ITS and learning environments. This kind of educational system can be very helpful in supporting human learning by using Artificial Intelligent (AI) techniques, transforming information into knowledge, using it for tailoring many aspects of the educational process to the particular needs of each actor, and timely providing useful suggestions and recommendations (Brusilovsky et al, 1993; Carbonell, 1970; Clancey, 1979; Anderson et al, 1990; Aleven and Koedinger, 2002; Woolf, 2009). Intelligent Learning Environments have been successfully used and  applied in different fields of knowledge like medicine (Eliot et al, 1996), Electronic (Lajoie and Lesgold, 1992), Computer Programming (Mitrovic, 1998), military training (Chatham and Braddock, 2001) to mention but a few.

In recent years, ITS have incorporated the ability to recognize the student’s affective state, in addition to traditional cognitive state identification (Calvo and D’Mello, 2010, Wolf et al, 2009, Baker et al, 2010). These tutoring systems can detect affect and engagement by using different types of data sources like dialogs, speech, physiology, and facial expressions (Zeng et al, 2009; Calvo and D’Mello, 2010; Arroyo et al, 2009; Conati and Maclaren, 2009; Burleson, 2011). Moreover, these tutoring systems seek to change in students, negative emotional states (e.g. confused) into positive states (e.g. committed) in order to facilitate an appropriate emotional state for learning. Affective Tutoring Systems identify confusion, frustration, boredom, engagement, and other emotions prominent during learning activities (D’Mello and Graesser, 2012; D’Mello et al, 2014; Graesser and D’Mello, 2012). Students’ affect recognition can be implemented by different machine learning techniques like Bayesian Networks (Conati and Maclaren, 2009), Hidden-Markov Models (D’Mello and Graesser, 2010), or Neural Networks (Moridis and Economides, 2009). Although many works and studies have considered the development of affective tutoring systems, there is no research works yet especially in Intelligent and Affective Learning Environments, where all the involved components of the environment (the learning environment, the intelligent tutoring system, and/or the adaptive system) support the learning process. Taking this into account, there is a need to propose new approaches, techniques, methods, and processes in the field of Intelligent and Affective Learning Environments, with the purpose of considering cognitive and affective aspects in the teaching-learning and decision-taking processes.

The aim of this special issue is to collect innovative theoretical work and original applications on the field of Intelligent and Affective Learning Environments that recognize and respond to student emotions combining software and hardware methods. This special issue wants to focus on original scientific contributions in the form of theoretical, experimental research and case studies applying new perspectives on affect and learning or theories that integrate cognition with affect during learning. We also want to bring research on novel technologies that monitor emotions.

For this reason and given the research area and scope, we have considered that Journal of Educational Technology & Society is the perfect scenario for the publication of this special issue. Given the increasing number of conferences, workshops or tracks dedicated to Affect and Learning Technology topics in the last 5 years as the International Conferences ICACII, UMAP, IAED, ITS, and ICALT, we believe that the edition of a special issue covering the main technological aspects of this field is necessary and would be welcome by the scientific community.

Possible topics for research papers include, but are not limited to:

  • Modeling, Enactment and Intelligent Use of Emotion and Affect
  • Affect Detection ,Response, and Generation on learning
  • Affect Models and Theory
  • Affective Computing in Education
  • Affective Tutoring Systems
  • Affective learning Companions (Emotive Agents)
  • Student and Domain Models with Affect
  • Sentiment Analysis on Education Applications
  • Software Architectures for Affective Learning Systems
  • Intelligent Learning Management Systems
  • Affect in Game-Based Learning Environments
  • Mobile and Emotional Learning Applications
  • Affect Methodologies
  • Affect-Aware Learning Technologies
  • Authoring tools and Affect
  • Interaction Based-Affect Detection in Educational Software

Submission Guidelines and Other Considerations
This special issue will only publish regular research papers (up to 7000 words). Papers submitted must not have been published previously or under consideration for publication, though they may represent significant extensions of prior work. All submitted papers will go through a rigorous double-blind peer-review process (with at least three reviewers). The acceptance process will focus on those papers that address original scientific contributions in the form of theoretical and experimental research and case studies applying new perspectives on Intelligent and Affective Learning Environments.
An abstract submission is mandatory to allow editors a better assignment of reviewers. For this reason, authors which intent to submit a paper to this special issue should send an email with title and abstract to the Lead Guest Editor.

Before submission authors should carefully read over the journal’s Author Guidelines, which are located at Prospective authors should submit an electronic copy of their complete manuscript using EasyChair system at: according to the following timeline:

Abstract submission: April 6, 2015
Manuscript submission deadline: May 1, 2015
First Review to be completed (includes author notification): July 10, 2015
Deadline for receipt of revisions: August 15, 2015
Second review to be completed (includes author notification): September 20, 2015
Final version:  October 10, 2015
Publication: April, 2016

Alejandro Rodriguez-Gonzalez, Polytechnic University of Madrid, Spain
Carlos A. Reyes-García, INAOE, Mexico
Claude Frasson, University of Montreal, Canada
Cristina Casado Lumbreras, Universidad Internacional de La Rioja, Spain
Giner Alor-Hernández, Instituto Tecnológico de Orizaba, Mexico
Harald Holone, Østfold University College, Norway
Jaime Alberto Guzman-Luna, National University of Colombia, Colombia
Jose-Maria Alvarez-Rodriguez, Universidad Carlos III de Madrid, Spain
Juan Miguel Gomez Berbis, Universidad Carlos III de Madrid, Spain
María Lucía Barrón-Estrada, Instituto Tecnológico de Culiacán, Mexico
María Mercedes T. Rodrigo, Ateneo de Manila University, Philippines
Miguel Ángel Conde, Universidad de León, Spain
Mihai Dasc?lu, University “Politehnica” of Bucharest, Romania
Miroslav Minovi?, University of Belgrade, Serbia
Patricia Ordoñez de Pablos, Oviedo University, Spain
Rados?aw Michalski,Wroc?aw University of Technology,  Poland
Rafael Valencia-Garcia, Murcia University, Spain
Ramón Zatarain-Cabada, Instituto Tecnológico de Culiacán, Mexico
Ricardo Colomo-Palacios, Østfold University College, Norway
Rodney D. Nielsen, University of North Texas, USA
Roger Azevedo, North Carolina State University, USA
Roger Nkambou, University of Quebec at Montreal, Canada
Sanjay Misra, Atilim University, Turkey
Vladimir Stantchev, SRH University Berlin, Germany
Yasmín Hernández-Pérez, Instituto de Investigaciones Eléctricas, Mexico

Aleven, V., and Koedinger, K. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26(2), 147-179.
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Arroyo, I., Woolf, B., Cooper, D., Burleson, W., Muldner, K., Christopherson, R. (2009). Emotions sensors go to school. In: Proceedings of the 14th international conference on artificial intelligence in education, pp. 17-24. IOS press, Amsterdam.
Baker, R.S., D’Mello, S.K., Rodrigo, M.T., & Graesser, A.C. (2010). Better to be frustrated than  bored: The incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68, 223-241.
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Burleson, W. (2011). Advancing a Multi-Modal Real-Time Affective Sensing Research Platform. New Perspectives on Affect and Learning Technologies, Calvo, R. A. and D’Mello, S. (Ed.), Springer, New York.
Calvo, R., and D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing 1(1), 18–37.
Carbonell, J. R. (1970). AI in CAI: An artificial intelligence approach to computer-aided-instruction. IEEE Transactions on Man-Machine System, MMS, 11(4), 190-202.
Chatham, R. and Braddock, J. (2001). Training Superiority & Training Surprise. Final Report , Defense Science Board Task Force.
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Conati C. and Maclaren H. (2009). Empirically Building and Evaluating a Probabilistic Model of User Affect. User Modeling and User-Adapted Interaction, 19, 267-303.
D’Mello, S., and Graesser, A., (2010). Modeling cognitive-affective dynamics with Hidden Markov Models. In: Catrambone, R., Ohlsson, S. (Eds.), Proceedings of the 32nd Annual Cognitive Science Society. Cognitive Science Society, Austin, TX, pp. 2721–2726.
D’Mello, S. K., and Graesser, A. C. (2012). Emotions during learning with AutoTutor. Adaptive technologies for training and education, Durlach, P. J. and Lesgold, A.  (Ed.),  Cambridge, U.K. Cambridge University Press.
D’Mello, S. K., Lehman, B. Pekrun, R., & Graesser, A. C. (2014). Confusion Can be Beneficial For Learning, Learning and Instruction, 29(1), 153-170.
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Graesser, A. C., and D’Mello, S. K. (2012). Moment-to-moment emotions during reading. The Reading Teacher, 66, 238-242.
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Moridis, C. N., and Economides, A. A. (2009). Prediction of student’s mood during an online test using formula-based and neural network-based method. Computers & Education, 53(3), 644–652.
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Lead Guest Editor
Ramón Zatarain Cabada Ph.D., Division of Research and Postgraduate Studies, Instituto Tecnológico de Culiacán, Juan de Dios Bátiz, 310 Pte. CP 80220, Culiacán, Sinaloa, México.

Guest Editors
Giner Alor Hernández Ph.D., Division of Research and Postgraduate Studies, Instituto Tecnológico de Orizaba, Oriente 9 #82 Emiliano Zapata, CP. 94320, Orizaba, Veracruz, México.
María Lucía Barrón Estrada Ph.D., Division of Research and Postgraduate Studies, Instituto Tecnológico de Culiacán, Juan de Dios Bátiz, 310 Pte. CP 80220, Culiacán, Sinaloa, México.
Ricardo Colomo-Palacios Ph.D., Full Professor at the Computer Science Department of the Østfold University College, Norway. Email:
Hao-Chiang Koong Lin Ph.D., Dean, College of Innovative and Management, National Taipei University of Business, Dept. of Information and Learning Technologe, National University of Tainan.

Ramón Zatarain Cabada is a Professor and Researcher of the Master of Computer Science at the Instituto Tecnológico de Culiacán (ITC). He received the Master of Science and PhD degrees in Computer Science from Florida Institute of Technology. He is National Researcher recognized by the National Council of Science & Technology of Mexico (CONACYT), level II. His main research interests include artificial intelligence in Education, m and e-learning, and affective computing applied to education. He has authored and co-authored more than 60 scientific article and chapters in international journals and conference proceedings, and has been the director of 10 research projects granted by DGEST and CONACYT. He is founder and leader of the research body in software engineering at the ITC and has been organizing (chair) for 7 years the International Workshop of Intelligent Learning Environment (WILE 2012, WILE 2013, WILE 2014). He also has been a member of Program Committees for International Conferences ITS, MICAI, and WILE. Last, he has coached and advised 6 different national and international-winning projects in the field of computer software and contest programming (ACM-ICPC).

Giner Alor Hernández is a full-time researcher of the Division of Research and Postgraduate Studies of the Orizaba Technology Institute. He received a MSc and a PhD in Computer Science of the Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV), Mexico. He has headed 10 mexican research projects granted by CONACYT, DGEST and PROMEP. He is author/coauthor around 100 journal and conference papers in computer science. He has been a committee program member of around 30 international conferences sponsored by IEEE, ACM and Springer and editorial board member of 5 indexed journals. He has been guest editor of 3 JCR-indexed journals (CMMM, JMS). His research interests Web development, E-learning, M-learning. He is an IEEE and ACM Member. He is National Researcher recognized by the National Council of Science & Technology of Mexico (CONACYT).

María Lucía Barrón Estrada is a Professor and Researcher of the Master of Computer Science at the Instituto Tecnológico de Culiacán. She holds a degree in Computer Science from the Instituto Tecnológico de Culiacán, a M.Sc. in Computer Science from the Instituto Tecnológico de Toluca and a PhD in Computer Science from the Florida Institute of Technology. She is a National Researcher recognized by the National Council of Science & Technology of Mexico (CONACYT), Level II. She has authored and co-authored more than 50 scientific papers and chapters in international journals and conference proceedings, has been the director and co-director of 6 research projects granted by DGEST and CONACYT, and has been the coach of different project and student teams winning national and international software and programming competitions. For 7 years, she has organized as co-chair the MICAI Workshop on Intelligent Learning Environment (WILE 2012, WILE 2013, WILE 2014). Her main research interests are mobile, web based and hybrid learning. She also works on the implementation of authoring tools for intelligent tutoring systems and programming languages.? She is a member of the ACM association.

Dr. Ricardo Colomo-Palacios is a Full Professor at the Computer Science Department of the Østfold University College, Norway. Formerly he worked at Universidad Carlos III de Madrid, Spain. His research interests include applied research in Information Systems, software project management, people in software projects and social and Semantic Web. He received his PhD in Computer Science from the Universidad Politécnica of Madrid (2005). He also holds a MBA from the Instituto de Empresa (2002). He has been working as Software Engineer, Project Manager and Software Engineering Consultant in several companies including Spanish IT leader INDRA. He is also an Editorial Board Member and Associate Editor for several international journals and conferences and Editor in Chief of International Journal of Human Capital and Information Technology Professionals.

Dr. Hao-Chiang Koong Lin works as Dean of College of Innovative and Management, National Taipei University of Business, Taiwan. He is also a Professor in the Department of Information and Learning Technology, National University of Tainan. He also worked as a CIO and department chair in MingHsin University. He received his Ph.D. degree in Computer Science from National Tsing-Hua University, 1997. He has published more than 300 internationally refereed research papers focused on affective computing, learning and education technology, digital arts, interaction design, e-commerce, and artificial intelligence. He serves as a member of editorial review board for several international journals and many international conferences.

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