Academic Works
*Aslan, S., Okur, E., Alyuz, N., Mete, S. E., Oktay, E., Genc, U., & Esme, A. A. (2017). Students' Emotional Self-labels for Personalized Models. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 550-551). ACM.
You can reach the conference paper from here.
*Aslan S., Reigeluth, C. M., & Mete, S. E. (2016). Transforming Classrooms into Learning Studios: What does it take to make classrooms a living space?. Educational Technology, 56 (5), 35-41.
You can reach the journal paper from here.
*Aslan, S., Mete, S. E., Okur, E., Oktay, E., Alyuz, N., Genc, U., Stanhill, D., & Esme, A. A. (2016). Human Expert Labeling Process (HELP): Towards a Reliable Higher-Order User State Labeling Process and Tool to Assess Student Engagement. Educational Technology, 57 (1), 53-59.
You can reach the journal paper from here.
*Okur, E., Alyuz, N., Oktay, E., Genc, U., Aslan, S., Mete, S. E., Arnrich, B., & Esme, A. A. (2016). Semi-supervised Model Personalization for Improved Detection of Learner’s Emotional Engagement. In Proceedings of the 18th ACM International Conference on Multimodal Interaction (pp. 100-107). ACM.
You can reach the conference paper from here.
*Mete, S. E., Altınışık, H. Z. (2016). Dyslexia and Technological Support for Learners with Dyslexia. Paper Presentation at 6th International Conference on “Innovations in Learning for the Future” 2016: Next Generation, Istanbul, Turkey.
You can reach the paper from here.
*Alyuz, N., Okur, E., Oktay, E., Genc, U., Aslan, S., Mete, S. E., Stanhill, D., Arnrich, B., & Esme, A. A. (2016). Towards an emotional engagement model: Can affective states of a learner be automatically detected in a 1:1 learning scenario?. In 24th ACM Conference on User Modeling, Adaptation and Personalization (UMAP) (Extended Proceedings). ACM.
You can reach the proceedings of conference from here.
*Aslan, S., Mete, S. E., Okur, E., Oktay, E., Alyuz, N., Genc, U., Stanhill, D., & Esme, A. A. (2016). Human Expert Labeling Process (HELP): Towards a Reliable Higher-Order User State Labeling by Human Experts. In Workshop Proceedings of International Conference on Intelligent Tutoring Systems (ITS) (pp. 156-165), Zagreb, Croatia.
You can reach the proceedings of conference from here.
*Aslan, S., & Mete, S. E. (2016). Evolution of Educational Technology: Educational Implications of Internet of Things (IoT) as a New Technology Paradigm. Paper Presentation at Association for Educational Communications and Technology (AECT) International Convention, Las Vegas.
You can reach (1) the program of conference from here.
(2) the presentation from here.
*Aslan, S., Reigeluth, C. M., Mete, S. E. (2016). Transforming Classrooms into Learning Studios: What does it take to make classrooms a living space? Paper Presentation at Association for Educational Communications and Technology (AECT) International Convention, Las Vegas.
You can reach the program of conference from here.
*Aslan, S., Alyuz, N., Esme, A. A., Mete, S. E., Oktay, E., & Okur, E. (2015). Multi-modal learner engagement detection and model personalization in 1:1 learning scenario. Intel EMEA Technology Conference, Istanbul, Turkey.
*Banas, J., Aslan, S., Blatt, L., Hale, P., Kopcha, T. J., Leary, H., Mete, S. E., Miller, C., Persichitte, K., & Polly, D. (2015). Building Bridges: Cooperative Learning and Shared Research between Academia and Our Schools. Paper presentation at the Annual International Meeting of the Association for Educational Communications and Technology (AECT), Indianapolis, IN.
You can reach the program of conference from here.
*Aslan, S., Alyuz, N., Esme, A. A., Mete, S. E., Oktay, E., & Okur, E. (2015). Investigating Student Engagement in 1:1 Learning. Paper presentation at the Annual International Educational Technology Conference (IETC), Istanbul, Turkey.
You can reach the presentation from here.
*Aslan, S., Ghobashy, D., Mete, S. E., Price, J. K., Roth, M., & Farraj, M. (2015) Education Transformation: A Proactive Approach for Schools to Change with Changes in Society. In Proceedings of Innovation Arabia 8 – Annual Conference 2015 (e-Learning Excellence) (pp. 45-61). Dubai, UAE.
You can reach the conference paper from here.
*Esme, A. A., Mete, S. E. & Oktay, E., & Aybat, B. (2014). 21th Century Education and Personalized Learning. In Proceedings of 11th Intel Education Summit, Istanbul, Turkey.
You can reach the proceedings of summit from here.
*Mete, S. E., Merter, K., & Samur, Y. (2014). Should there be a Limitation for Segmenting Principle in Multimedia Presentations? In Proceedings of 8th International Computer and Instructional Technologies Symposium (pp. 191-194).
You can reach the proceedings of symposium from here.
Patents
1) SYSTEM AND METHOD FOR IDENTIFYING LEARNER ENGAGEMENT STATES
Publication number: 20170039876
Abstract: Embodiments herein relate to identifying a learning engagement state of a learner. A computing platform with one or more processors running modules may receive indications of interactions of a learner with an educational program as well as indications of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program. A current learning engagement state of the learner may be identified based at least in part on the received indications by using an artificial neural network associated that is calibrated to the learner. The artificial neural network may be trained and updated in part by human observation and learner self-reporting of the learner's current learning engagement state.
Filed: August 6, 2015
Publication date: February 9, 2017
Inventors: NESE ALYUZ CIVITCI, EDA OKUR, ASLI ARSLAN ESME, SINEM ASLAN, ECE OKTAY, SINEM E. METE, DAVID STANHILL, VLADIMIR SHLAIN, PINI ABRAMOVITCH, EYAL ROND, ALEX KUNIN, ILAN PAPINI
2) USER STATE MODEL ADAPTATION THROUGH MACHINE DRIVEN LABELING
Publication number: 20170169715
Abstract: Embodiments herein relate to generating a personalized model using a machine learning process, identifying a learning engagement state of a learner based at least in part on the personalized model, and tailoring computerized provision of an educational program to the learner based on the learning engagement state. An apparatus to provide a computer-aided educational program may include one or more processors operating modules that may receive indications of interactions of a learner and indications of physical responses of the learner, generate a personalized model using a machine learning process based at least in part on the interactions of the learner and the indications of physical responses of the learner during a calibration time period, and identify a current learning state of the learner based at least in part on the personalized model during a usage time period. Other embodiments may be described and/or claimed.
Filed: December 9, 2015
Publication date: June 15, 2017
Inventors: NESE ALYUZ CIVITCI, EDA OKUR, ASLI ARSLAN ESME, SINEM ASLAN, ECE OKTAY, SINEM E. METE, HASAN UNLU, DAVID STANHILL, VLADIMIR SHLAIN, PINI ABRAMOVITCH, EYAL ROND
You can reach the conference paper from here.
*Aslan S., Reigeluth, C. M., & Mete, S. E. (2016). Transforming Classrooms into Learning Studios: What does it take to make classrooms a living space?. Educational Technology, 56 (5), 35-41.
You can reach the journal paper from here.
*Aslan, S., Mete, S. E., Okur, E., Oktay, E., Alyuz, N., Genc, U., Stanhill, D., & Esme, A. A. (2016). Human Expert Labeling Process (HELP): Towards a Reliable Higher-Order User State Labeling Process and Tool to Assess Student Engagement. Educational Technology, 57 (1), 53-59.
You can reach the journal paper from here.
*Okur, E., Alyuz, N., Oktay, E., Genc, U., Aslan, S., Mete, S. E., Arnrich, B., & Esme, A. A. (2016). Semi-supervised Model Personalization for Improved Detection of Learner’s Emotional Engagement. In Proceedings of the 18th ACM International Conference on Multimodal Interaction (pp. 100-107). ACM.
You can reach the conference paper from here.
*Mete, S. E., Altınışık, H. Z. (2016). Dyslexia and Technological Support for Learners with Dyslexia. Paper Presentation at 6th International Conference on “Innovations in Learning for the Future” 2016: Next Generation, Istanbul, Turkey.
You can reach the paper from here.
*Alyuz, N., Okur, E., Oktay, E., Genc, U., Aslan, S., Mete, S. E., Stanhill, D., Arnrich, B., & Esme, A. A. (2016). Towards an emotional engagement model: Can affective states of a learner be automatically detected in a 1:1 learning scenario?. In 24th ACM Conference on User Modeling, Adaptation and Personalization (UMAP) (Extended Proceedings). ACM.
You can reach the proceedings of conference from here.
*Aslan, S., Mete, S. E., Okur, E., Oktay, E., Alyuz, N., Genc, U., Stanhill, D., & Esme, A. A. (2016). Human Expert Labeling Process (HELP): Towards a Reliable Higher-Order User State Labeling by Human Experts. In Workshop Proceedings of International Conference on Intelligent Tutoring Systems (ITS) (pp. 156-165), Zagreb, Croatia.
You can reach the proceedings of conference from here.
*Aslan, S., & Mete, S. E. (2016). Evolution of Educational Technology: Educational Implications of Internet of Things (IoT) as a New Technology Paradigm. Paper Presentation at Association for Educational Communications and Technology (AECT) International Convention, Las Vegas.
You can reach (1) the program of conference from here.
(2) the presentation from here.
*Aslan, S., Reigeluth, C. M., Mete, S. E. (2016). Transforming Classrooms into Learning Studios: What does it take to make classrooms a living space? Paper Presentation at Association for Educational Communications and Technology (AECT) International Convention, Las Vegas.
You can reach the program of conference from here.
*Aslan, S., Alyuz, N., Esme, A. A., Mete, S. E., Oktay, E., & Okur, E. (2015). Multi-modal learner engagement detection and model personalization in 1:1 learning scenario. Intel EMEA Technology Conference, Istanbul, Turkey.
*Banas, J., Aslan, S., Blatt, L., Hale, P., Kopcha, T. J., Leary, H., Mete, S. E., Miller, C., Persichitte, K., & Polly, D. (2015). Building Bridges: Cooperative Learning and Shared Research between Academia and Our Schools. Paper presentation at the Annual International Meeting of the Association for Educational Communications and Technology (AECT), Indianapolis, IN.
You can reach the program of conference from here.
*Aslan, S., Alyuz, N., Esme, A. A., Mete, S. E., Oktay, E., & Okur, E. (2015). Investigating Student Engagement in 1:1 Learning. Paper presentation at the Annual International Educational Technology Conference (IETC), Istanbul, Turkey.
You can reach the presentation from here.
*Aslan, S., Ghobashy, D., Mete, S. E., Price, J. K., Roth, M., & Farraj, M. (2015) Education Transformation: A Proactive Approach for Schools to Change with Changes in Society. In Proceedings of Innovation Arabia 8 – Annual Conference 2015 (e-Learning Excellence) (pp. 45-61). Dubai, UAE.
You can reach the conference paper from here.
*Esme, A. A., Mete, S. E. & Oktay, E., & Aybat, B. (2014). 21th Century Education and Personalized Learning. In Proceedings of 11th Intel Education Summit, Istanbul, Turkey.
You can reach the proceedings of summit from here.
*Mete, S. E., Merter, K., & Samur, Y. (2014). Should there be a Limitation for Segmenting Principle in Multimedia Presentations? In Proceedings of 8th International Computer and Instructional Technologies Symposium (pp. 191-194).
You can reach the proceedings of symposium from here.
Patents
1) SYSTEM AND METHOD FOR IDENTIFYING LEARNER ENGAGEMENT STATES
Publication number: 20170039876
Abstract: Embodiments herein relate to identifying a learning engagement state of a learner. A computing platform with one or more processors running modules may receive indications of interactions of a learner with an educational program as well as indications of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program. A current learning engagement state of the learner may be identified based at least in part on the received indications by using an artificial neural network associated that is calibrated to the learner. The artificial neural network may be trained and updated in part by human observation and learner self-reporting of the learner's current learning engagement state.
Filed: August 6, 2015
Publication date: February 9, 2017
Inventors: NESE ALYUZ CIVITCI, EDA OKUR, ASLI ARSLAN ESME, SINEM ASLAN, ECE OKTAY, SINEM E. METE, DAVID STANHILL, VLADIMIR SHLAIN, PINI ABRAMOVITCH, EYAL ROND, ALEX KUNIN, ILAN PAPINI
2) USER STATE MODEL ADAPTATION THROUGH MACHINE DRIVEN LABELING
Publication number: 20170169715
Abstract: Embodiments herein relate to generating a personalized model using a machine learning process, identifying a learning engagement state of a learner based at least in part on the personalized model, and tailoring computerized provision of an educational program to the learner based on the learning engagement state. An apparatus to provide a computer-aided educational program may include one or more processors operating modules that may receive indications of interactions of a learner and indications of physical responses of the learner, generate a personalized model using a machine learning process based at least in part on the interactions of the learner and the indications of physical responses of the learner during a calibration time period, and identify a current learning state of the learner based at least in part on the personalized model during a usage time period. Other embodiments may be described and/or claimed.
Filed: December 9, 2015
Publication date: June 15, 2017
Inventors: NESE ALYUZ CIVITCI, EDA OKUR, ASLI ARSLAN ESME, SINEM ASLAN, ECE OKTAY, SINEM E. METE, HASAN UNLU, DAVID STANHILL, VLADIMIR SHLAIN, PINI ABRAMOVITCH, EYAL ROND