An ambient agent model for reading companion robot

Reading is essentially a problem-solving task. Based on what is read, like problem solving, it requires effort, planning, self-monitoring, strategy selection, and reflection. Also, as readers are trying to solve difficult problems, reading materials become more complex, thus demands more effort and...

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Main Author: Ali, Hayder Mohammed Ali
Format: Thesis
Language:eng
eng
Published: 2019
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Online Access:https://etd.uum.edu.my/8160/1/s900386_01.pdf
https://etd.uum.edu.my/8160/2/s900386_02.pdf
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institution Universiti Utara Malaysia
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language eng
eng
advisor Ab Aziz, Azizi
Ahmad, Faudziah
topic PN Literature (General)
T Technology (General)
spellingShingle PN Literature (General)
T Technology (General)
Ali, Hayder Mohammed Ali
An ambient agent model for reading companion robot
description Reading is essentially a problem-solving task. Based on what is read, like problem solving, it requires effort, planning, self-monitoring, strategy selection, and reflection. Also, as readers are trying to solve difficult problems, reading materials become more complex, thus demands more effort and challenges cognition. To address this issue, companion robots can be deployed to assist readers in solving difficult reading tasks by making reading process more enjoyable and meaningful. These robots require an ambient agent model, monitoring of a reader’s cognitive demand as it could consist of more complex tasks and dynamic interactions between human and environment. Current cognitive load models are not developed in a form to have reasoning qualities and not integrated into companion robots. Thus, this study has been conducted to develop an ambient agent model of cognitive load and reading performance to be integrated into a reading companion robot. The research activities were based on Design Science Research Process, Agent-Based Modelling, and Ambient Agent Framework. The proposed model was evaluated through a series of verification and validation approaches. The verification process includes equilibria evaluation and automated trace analysis approaches to ensure the model exhibits realistic behaviours and in accordance to related empirical data and literature. On the other hand, validation process that involved human experiment proved that a reading companion robot was able to reduce cognitive load during demanding reading tasks. Moreover, experiments results indicated that the integration of an ambient agent model into a reading companion robot enabled the robot to be perceived as a social, intelligent, useful, and motivational digital side-kick. The study contribution makes it feasible for new endeavours that aim at designing ambient applications based on human’s physical and cognitive process as an ambient agent model of cognitive load and reading performance was developed. Furthermore, it also helps in designing more realistic reading companion robots in the future.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Ali, Hayder Mohammed Ali
author_facet Ali, Hayder Mohammed Ali
author_sort Ali, Hayder Mohammed Ali
title An ambient agent model for reading companion robot
title_short An ambient agent model for reading companion robot
title_full An ambient agent model for reading companion robot
title_fullStr An ambient agent model for reading companion robot
title_full_unstemmed An ambient agent model for reading companion robot
title_sort ambient agent model for reading companion robot
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2019
url https://etd.uum.edu.my/8160/1/s900386_01.pdf
https://etd.uum.edu.my/8160/2/s900386_02.pdf
_version_ 1747828339168509952
spelling my-uum-etd.81602022-02-16T01:45:57Z An ambient agent model for reading companion robot 2019 Ali, Hayder Mohammed Ali Ab Aziz, Azizi Ahmad, Faudziah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences PN Literature (General) T Technology (General) Reading is essentially a problem-solving task. Based on what is read, like problem solving, it requires effort, planning, self-monitoring, strategy selection, and reflection. Also, as readers are trying to solve difficult problems, reading materials become more complex, thus demands more effort and challenges cognition. To address this issue, companion robots can be deployed to assist readers in solving difficult reading tasks by making reading process more enjoyable and meaningful. These robots require an ambient agent model, monitoring of a reader’s cognitive demand as it could consist of more complex tasks and dynamic interactions between human and environment. Current cognitive load models are not developed in a form to have reasoning qualities and not integrated into companion robots. Thus, this study has been conducted to develop an ambient agent model of cognitive load and reading performance to be integrated into a reading companion robot. The research activities were based on Design Science Research Process, Agent-Based Modelling, and Ambient Agent Framework. The proposed model was evaluated through a series of verification and validation approaches. The verification process includes equilibria evaluation and automated trace analysis approaches to ensure the model exhibits realistic behaviours and in accordance to related empirical data and literature. On the other hand, validation process that involved human experiment proved that a reading companion robot was able to reduce cognitive load during demanding reading tasks. Moreover, experiments results indicated that the integration of an ambient agent model into a reading companion robot enabled the robot to be perceived as a social, intelligent, useful, and motivational digital side-kick. The study contribution makes it feasible for new endeavours that aim at designing ambient applications based on human’s physical and cognitive process as an ambient agent model of cognitive load and reading performance was developed. Furthermore, it also helps in designing more realistic reading companion robots in the future. 2019 Thesis https://etd.uum.edu.my/8160/ https://etd.uum.edu.my/8160/1/s900386_01.pdf text eng public https://etd.uum.edu.my/8160/2/s900386_02.pdf text eng public phd doctoral Universiti Utara Malaysia Aarts, E., & De Ruyter, B. (2009). New research perspectives on Ambient Intelligence. Journal of Ambient Intelligence and Smart Environments, 1(1), 5–14. Aziz, A. A., Ahmad, F., ChePa, N., & Yusof, S. A. M. (2013). Verification of an Agent Model for Chronic Fatigue Syndrome. International Journal of Digital Content Technology and Its Applications, 7(14), 25. Aziz, A. A., Ahmad, F., & Hintaya, H. M. (2012). An agent model for temporal dynamics analysis of a person with chronic fatigue syndrome. In Brain Informatics (pp. 107– 118). Springer. Aziz, A. A., Shabli, A. H. M., & Ghanimi, H. M. A. (2017). Formal Specifications and Analysis of an Agent-Based Model for Cognitive Aspects of Fear of Crime BT - Multi-disciplinary Trends in Artificial Intelligence: 11th International Workshop, MIWAI 2017, Gadong, Brunei, November 20-22, 2017, Proceedings. In S. Phon- Amnuaisuk, S.-P. Ang, & S.-Y. Lee (Eds.) (pp. 331–345). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-69456-6_28. Abar, S., Theodoropoulos, G. K., Lemarinier, P., & O’Hare, G. M. P. (2017). Agent Based Modelling and Simulation tools: A review of the state-of-art software. Computer Science Review, 24, 13–33. Abro, A. H., Klein, M. A. C. A., Manzoor, A. R., Tabatabaei, S. A., & Treur, J. (2014). A Computational Model of the Relation between Regulation of Negative Emotions and Mood. In C. Loo, K. Yap, K. Wong, A. Teoh, & K. Huang (Eds.), Neural Information Processing SE - 8 (Vol. 8834, pp. 59–68). Springer International Publishing. https://doi.org/10.1007/978-3-319-12637-1_8 Adam, C., & Gaudou, B. (2016). BDI agents in social simulations: a survey. The Knowledge Engineering Review, 31(3), 207–238. Adegoke, O., Aziz, A. A., Yusof, Y. (2015). Designing a BDI Agent Reactant Model Of Behavioural Change Intervention. Jurnal Teknologi, 78(2–2), 83–93. Aigner, P., & McCarragher, B. (1999). Shared control framework applied to a robotic aid for the blind. Control Systems, IEEE, 19(2), 40–46. Al-Shaqi, R., Mourshed, M., & Rezgui, Y. (2016). Progress in ambient assisted systems for independent living by the elderly. SpringerPlus, 5(1), 624. https://doi.org/10.1186/s40064-016-2272-8 Al Hazzouri, A. Z., Haan, M. N., Deng, Y., Neuhaus, J., & Yaffe, K. (2014). Reduced heart rate variability is associated with worse cognitive performance in elderly Mexican Americans. Hypertension, 63(1), 181–187. Al Husaini, Z. A. E. (2013). Knowledge, attitude and practice of reading habit among female medical students, Taibah University. Journal of Taibah University Medical Sciences, 8(3), 192–198. https://doi.org/http://dx.doi.org/10.1016/j.jtumed.2013.09.004. Alexander, P. A., & Laboratory, T. D. R. and L. R. (2012). Reading Into the Future: Competence for the 21st Century. Educational Psychologist, 47(4), 259–280. https://doi.org/10.1080/00461520.2012.722511 Alidoust, M., & Rouhani, M. (2015). A Computational Behavior Model for Life-Like Intelligent Agents. In J. Romportl, E. Zackova, & J. Kelemen (Eds.), Beyond Artificial Intelligence SE - 12 (Vol. 9, pp. 159–175). Springer International Publishing. https://doi.org/10.1007/978-3-319-09668-1_12 Alsheikh, N. O., & Mokhtari, K. (2011). An examination of the metacognitive reading strategies used by native speakers of Arabic when reading in English and Arabic. English Language Teaching, 4(2), 151. An, G., Mi, Q., Dutta-Moscato, J., & Vodovotz, Y. (2009). Agent-based models in translational systems biology. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 1(2), 159–171. Applegate, A. J., Applegate, M. D., Mercantini, M. A., McGeehan, C. M., Cobb, J. B., DeBoy, J. R., … Lewinski, K. E. (2014). The Peter effect revisited: Reading habits and attitudes of college students. Literacy Research and Instruction, 53(3), 188–204. Aryania, A., Daniel, B., Thomessen, T., & Sziebig, G. (2012). New trends in industrial robot controller user interfaces. 3rd IEEE International Conference on Cognitive Infocommunications, 365–369. https://doi.org/10.1109/CogInfoCom.2012.6422007 Augello, A., Cipolla, E., Infantino, I., Manfré, A., Pilato, G., & Vella, F. (2018). Social signs processing in a cognitive architecture for an humanoid robot. Procedia Computer Science, 123, 63–68. https://doi.org/https://doi.org/10.1016/j.procs.2018.01.011. Augusto, J. C., & McCullagh, P. (2007). Ambient intelligence: Concepts and applications. Computer Science and Information Systems, 4(1), 1–27. Ayasun, S., Fischl, R., Vallieu, S., Braun, J., & Cadırlı, D. (2007). Modeling and stability analysis of a simulation–stimulation interface for hardware-in-the-loop applications. Simulation Modelling Practice and Theory, 15(6), 734–746. Aziz, A. A. (2012). Exploring Computational Models for Intelligent Support of Persons with Depression (doctoral’s thesis). VU University Amsterdam, Netherland. Aziz, A. A. (2016). Knowing When to Support: A Human-Aware Agent Model in a Psychological Domain. Human Factors and Ergonomics Malaysia, Vol 1(No 1), 45– 54. Aziz, A. A., & Klein, M. C. A. (2011). Computational Modeling of Therapies related to Cognitive Vulnerability and Coping. In BICA (pp. 16–25). Aziz, A. A., Klein, M. C. A., & Treur, J. (2010). An integrative ambient agent model for unipolar depression relapse prevention. Journal of Ambient Intelligence and Smart Environments, 2(1), 5–20. Baars, M., Wijnia, L., & Paas, F. (2017). The association between motivation, affect, and self-regulated learning when solving problems. Frontiers in Psychology, 8, 1346. Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559. Bainbridge, W. W. a., Hart, J., Kim, E. S. E. S., & Scassellati, B. (2008). The effect of presence on human-robot interaction. In RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication (pp. 701–706). IEEE. https://doi.org/10.1109/ROMAN.2008.4600749. Bakar, J. A. A., Mat, R. C., Aziz, A. A., Jasri, N. A. N., & Yusoff, M. F. (2016). Designing agent-based modeling in dynamic crowd simulation for stressful environment. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(10), 151–156. Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the system usability scale. Intl. Journal of Human–Computer Interaction, 24(6), 574–594. Bartneck, C. (2003). Interacting with an embodied emotional character. In Proceedings of the 2003 international conference on Designing pleasurable products and interfaces (pp. 55–60). New York,USA: ACM. Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71–81. Batula, A. M., Kim, Y. E., & Ayaz, H. (2017). Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface. BioMed Research International, 2017. Bazghandi, A. (2012). Techniques, advantages and problems of agent based modeling for traffic simulation. Int J Comput Sci, 9(1), 115–119. Bear, G. G., Slaughter, J. C., Mantz, L. S., & Farley-Ripple, E. (2017). Rewards, praise, and punitive consequences: Relations with intrinsic and extrinsic motivation. Teaching and Teacher Education, 65(Supplement C), 10–20. https://doi.org/https://doi.org/10.1016/j.tate.2017.03.001. Behroozi, M., Lui, A., Moore, I., Ford, D., & Parnin, C. (2018). Dazed: measuring the cognitive load of solving technical interview problems at the whiteboard. In Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results (pp. 93–96). ACM. Bellotto, N., Fernandez-Carmona, M., & Cosar, S. (2017). Enrichme integration of ambient intelligence and robotics for AAL. AAAI. Berland, M., & Wilensky, U. (2015). Comparing Virtual and Physical Robotics Environments for Supporting Complex Systems and Computational Thinking. Journal of Science Education and Technology, 1–20. https://doi.org/10.1007/s10956-015-9552-x. Berns, K., & Hirth, J. (2006). Control of facial expressions of the humanoid robot head ROMAN. In International Conference on Intelligent Robots and Systems (pp. 3119– 3124). IEEE. Bicho, E., Louro, L., & Erlhagen, W. (2010). Integrating verbal and nonverbal communication in a dynamic neural field architecture for human–robot interaction. Frontiers in Neurorobotics, 4. Bickmore, T. W., Caruso, L., Clough-Gorr, K., & Heeren, T. (2005). ‘It’s just like you talk to a friend’relational agents for older adults. Interacting with Computers, 17(6), 711–735. Bjorklund, D. F. (2013). Children’s strategies: Contemporary views of cognitive development. Psychology Press. Blehm, C., Vishnu, S., Khattak, A., Mitra, S., & Yee, R. W. (2005). Computer Vision Syndrome: A Review. Survey of Ophthalmology, 50(3), 253–262. https://doi.org/http://dx.doi.org/10.1016/j.survophthal.2005.02.008 Block, R. A., Hancock, P. A., & Zakay, D. (2010). How cognitive load affects duration judgments: A meta-analytic review. Acta Psychologica, 134(3), 330–343. Boaler, J., Dieckmann, J. A., Perez Núñez, G., Liu Sun, K., & Williams, C. (2018). Changing Students Minds & Achievement in Mathematics: The Impact of a Free Online Student Course. In Frontiers in Education (Vol. 3, p. 26). Frontiers. Bogue, R. (2017). Domestic robots: Has their time finally come? Industrial Robot: An International Journal, 44(2), 129–136. Bohn, J., Coroamă, V., Langheinrich, M., Mattern, F., & Rohs, M. (2005). Social, economic, and ethical implications of ambient intelligence and ubiquitous computing. In Ambient intelligence (pp. 5–29). Springer. Boksem, M. A. S., & Tops, M. (2008). Mental fatigue: costs and benefits. Brain Research Reviews, 59(1), 125–139. Bongiorno, C., Miccichè, S., & Mantegna, R. N. (2017). An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in air traffic management. PLoS One, 12(4), e0175036. Boomgaard, G., Lavitt, F., & Treur, J. (2018). Computational Analysis of Social Contagion and Homophily Based on an Adaptive Social Network Model. In 10th International Conference on Social Informatics, SocInfo’18. Saint Petersburg, Russia. Bosse, T. (2005). Analysis of the Dynamics of Cognitive Processes (doctoral’s thesis). VU University Amsterdam, Netherland., 410. Bosse, T., Both, F., Duell, R., Hoogendoorn, M., Klein, M. C. A., Van Lambalgen, R., Treur, J. (2013). An ambient agent system assisting humans in complex tasks by analysis of a human’s state and performance. International Journal of Intelligent Information and Database Systems, 7(1), 3–33. Bosse, T., Both, F., Gerritsen, C., Hoogendoorn, M., & Treur, J. (2007). Model-based reasoning methods within an ambient intelligent agent model. In European Conference on Ambient Intelligence (pp. 352–370). Springer. Bosse, T., Both, F., Gerritsen, C., Hoogendoorn, M., & Treur, J. (2012). Methods for model-based reasoning within agent-based Ambient Intelligence applications. Knowledge-Based Systems, 27, 190–210. Bosse, T., Both, F., Van Lambalgen, R., & Treur, J. (2008). An Agent Model for a Human’s Functional State and Performance. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 02 (pp. 302–307). IEEE Computer Society. Bosse, T., Callaghan, V., & Lukowicz, P. (2010). On computational modeling of human- oriented knowledge in Ambient Intelligence. JAISE, 2(1), 3–4. Bosse, T., Duell, R., Memon, Z. A., Treur, J., & van der Wal, C. N. (2017). Computational model-based design of leadership support based on situational leadership theory. Simulation, 605–617. https://doi.org/10.1177/0037549717693324. Bosse, T., Duell, R., Memon, Z., Treur, J., & van der Wal, C. N. (2015). Agent-Based Modeling of Emotion Contagion in Groups. Cognitive Computation, 7(1), 111–136. https://doi.org/10.1007/s12559-014-9277-9 Bosse, T., Hoogendoorn, M., Klein, M. C. A., & Treur, J. (2011). An ambient agent model for monitoring and analysing dynamics of complex human behaviour. Journal of Ambient Intelligence and Smart Environments, 3(4), 283–303. Bosse, T., Hoogendoorn, M., Klein, M. C. A., van Lambalgen, R., van Maanen, P.-P., & Treur, J. (2011). Incorporating human aspects in ambient intelligence and smart environments. Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives (Pp. 128-164), IGI Global . Bosse, T., Jonker, C. M., Meij, L, van Der, & Treur, J. (2006). A Temporal Trace Language for the Formal Analysis of Dynamic Properties, 1–15. Bosse, T., Jonker, C. M., van der Meij, L., Sharpanskykh, A., & Treur, J. (2009). Specification and verification of dynamics in agent models. International Journal of Cooperative Information Systems, 18(01), 167–193. Bosse, T., Jonker, C. M., van Der Meij, L., & Treur, J. (2005). LEADSTO: A Language and Environment for Analysis of Dynamics by SimulaTiOn. International Journal on Artificial Intelligence Tools, 3533(03), 363–366. https://doi.org/10.1007/11550648_15 Bosse, T., Memon, Z. a., & Treur, J. (2011). a Recursive BDI Agent Model for Theory of Mind and Its Applications. Applied Artificial Intelligence, 25, 1–44. https://doi.org/10.1080/08839514.2010.529259 Bosse, T., Pontier, M., & Treur, J. (2010). A computational model based on Gross’ emotion regulation theory. Cognitive Systems Research, 11(3), 211–230. Bosse, T., & Provoost, S. (2015). Integrating conversation trees and cognitive models within an eca for aggression de-escalation training. In 18 International Conference on Principles and Practice of Multi-Agent Systems (pp. 650–659). Lecture Notes in Artificial Intelligence, Springer Verlag. Bosse, T., & Sharpanskykh, A. (2010). A framework for modeling and analysis of ambient agent systems: application to an emergency case. In Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence (ISAmI 2010) (pp. 121– 129). Springer. Both, F., Hoogendoorn, M., Klein, M. C. A., & Treur, J. (2009). Design and Analysis of an Ambient Intelligent System Supporting Depression Therapy. In HEALTHINF (pp. 142–148). Both, F., Hoogendoorn, M., Klein, M. C. A., & Treur, J. (2015). A generic computational model of mood regulation and its use to model therapeutical interventions. Biologically Inspired Cognitive Architectures, 13, 17–34. Both, F., Hoogendoorn, M., van der Mee, A., Treur, J., & de Vos, M. (2012). An intelligent agent model with awareness of workflow progress. Applied Intelligence, 36(2), 498– 510. Bouzeghoub, M., & Kedad, Z. (2000). A Logical Model for Data Warehouse Design and Evolution. In Y. Kambayashi, M. Mohania, & A. M. Tjoa (Eds.), Data Warehousing and Knowledge Discovery SE - 18 (Vol. 1874, pp. 178–188). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-44466-1_18 Bovbel, P., & Nejat, G. (2014). Casper: An Assistive Kitchen Robot to Promote Aging in Place. Journal of Medical Devices, 8(3), 30945. Bradáč, V., & Kostolányová, K. (2017). Intelligent Tutoring Systems. In E-Learning, E- Education, and Online Training: Third International Conference, eLEOT 2016, Dublin, Ireland, August 31–September 2, 2016, Revised Selected Papers (pp. 71–78). Springer. Braselton, S., & Decker, B. C. (1994). Using graphic organizers to improve the reading of mathematics. The Reading Teacher, 276–281. Bratman, M. E. (1990). What is intention. Intentions in Communication, 15–32. Brazier, F. M. T., Jonker, C. M., & Treur, J. (2000). Compositional design and reuse of a generic agent model. Applied Artificial Intelligence, 14(5), 491–538. Breazeal, C. (2003). Toward sociable robots. Robotics and Autonomous Systems, 42(3), 167–175. Breazeal, C. (2017). Social Robots: From Research to Commercialization. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (p. 1). ACM. Breazeal, C., Dautenhahn, K., & Kanda, T. (2016). Social Robotics. In B. Siciliano & O. Khatib (Eds.), In book: Springer Handbook of Robotics, Edition: 2nd (pp. 1935– 1937). Springer International Publishing. https://doi.org/10.1007/978-3-319-32552- 1_72 Breazeal, C., Kidd, C. D., Thomaz, A. L., Hoffman, G., & Berlin, M. (2005). Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. (pp. 708–713). IEEE. Breazeal, C. L. (2004). Designing sociable robots. MIT press. Briggs, T. W., & Kennedy, W. G. (2016). Active shooter: an agent-based model of unarmed resistance. In Proceedings of the 2016 Winter Simulation Conference (pp. 3521–3531). IEEE Press. Broadbent, E., Feerst, D. A., Lee, S. H., Robinson, H., Albo-Canals, J., Ahn, H. S., & MacDonald, B. A. (2018). How Could Companion Robots Be Useful in Rural Schools? International Journal of Social Robotics. https://doi.org/10.1007/s12369- 017-0460-5 Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability Evaluation in Industry, 189(194), 4–7. Brookhart, S. M. (2017). How to give effective feedback to your students. ASCD. Brunken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38(1), 53–61. Brünken, R., Steinbacher, S., Plass, J. L., & Leutner, D. (2002). Assessment of cognitive load in multimedia learning using dual-task methodology. Experimental Psychology, 49(2), 109. Burgar, C. G., Lum, P. S., Shor, P. C., & van der Loos, H. F. M. (2000). Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. Journal of Rehabilitation Research and Development, 37(6), 663–674. Burgoyne, K., Baxter, B., & Buckley, S. (2013). Developing the reading skills of children with Down syndrome. Educating Learners with Down Syndrome: Research, Theory and Practice with Children and Adolescents: Research, Theory, and Practice with Children and Adolescents, 195. Buscher, G., Dengel, A., Biedert, R., & Elst, L. V. (2012). Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond. ACM Transactions on Interactive Intelligent Systems (TiiS), 1(2), 9. Calderwood, C., Ackerman, P. L., & Conklin, E. M. (2014). What else do college students “do” while studying? An investigation of multitasking. Computers & Education, 75, 19–29. Cao, T., Wan, F., Wong, C. M., da Cruz, J. N., & Hu, Y. (2014). Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain- computer interfaces. Biomedical Engineering Online, 13(1), 28. Carney, L. G., & Hill, R. M. (1982). The nature of normal blinking patterns. Acta Ophthalmologica, 60(3), 427–433. Cech, P. (2016). Smart Classroom Study Design for Analysing the Effect of Environmental Conditions on Students ’ Comfort. In 5th International Workshop on Smart Offices and Other Workplaces, Intelligent Environemnt 2016 (pp. 14–23). https://doi.org/10.3233/978-1-61499-690-3-14. Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8(4), 293–332. Chang, A., & Breazeal, C. (2011). TinkRBook: shared reading interfaces for storytelling. In Proceedings of the 10th International Conference on Interaction Design and Children (pp. 145–148). ACM. https://doi.org/10.1145/1999030.1999047 Chang, K., Nelson, J., Pant, U., & Mostow, J. (2013). Toward Exploiting EEG Input in a Reading Tutor. International Journal of Artificial Intelligence in Education, 22(1), 19–38. Chang, W., & Šabanović, S. (2015). Interaction Expands Function: Social Shaping of the Therapeutic Robot PARO in a Nursing Home. Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, 343–350. https://doi.org/10.1145/2696454.2696472. Chen, F., Ruiz, N., Choi, E., Epps, J., Khawaja, M. A., Taib, R., … Wang, Y. (2012). Multimodal behavior and interaction as indicators of cognitive load. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(4), 22. Chen, I.-J., & Chang, C.-C. (2009). Cognitive Load Theory: An Empirical Study of Anxiety and Task Performance in Language Learning. Electronic Journal of Research in Educational Psychology, 7(2). Chen, S., & Epps, J. (2013). Automatic classification of eye activity for cognitive load measurement with emotion interference. Computer Methods and Programs in Biomedicine, 110(2), 111–124. Chen, S., Epps, J., Ruiz, N., & Chen, F. (2011). Eye activity as a measure of human mental effort in HCI. In Proceedings of the 16th international conference on Intelligent user interfaces (pp. 315–318). ACM. Chesney, T., Gold, S., & Trautrims, A. (2017). Agent based modelling as a decision support system for shadow accounting. Decision Support Systems, 95, 110–116. Chin, D. (2007). Information Filtering, Expertise and Cognitive Load. In D. Schmorrow & L. Reeves (Eds.), Foundations of Augmented Cognition SE - 9 (Vol. 4565, pp. 75–83). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-73216-7_9 Chin, K. O., Gan, K. S., Alfred Rayner, Anthon Ypatricia, & Lukose, D. (2014). Agent Architecture: An Overview. Transactions On Science And Technology, 1(1), 18–35. Choi, H.-H., van Merriënboer, J. J. G., & Paas, F. (2014). Effects of the physical environment on cognitive load and learning: towards a new model of cognitive load. Educational Psychology Review, 26(2), 225–244. Chong, N.-Y., & Mastrogiovanni, F. (2011). Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspective. Information Science Reference. Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? Computers in Human Behavior, 25(2), 315–324. https://doi.org/http://dx.doi.org/10.1016/j.chb.2008.12.020 Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3), 149–210. Cobanoglu, M. C., Kindiroglu, A. A., & Balcisoy, S. (2009). Comparison of mobile device navigation information display alternatives from the cognitive load perspective. In Engineering Psychology and Cognitive Ergonomics (pp. 149–157). Springer. Cominelli, L., Mazzei, D., & De Rossi, D. E. (2018). SEAI: Social Emotional Artificial Intelligence based on Damasio’s Theory of Mind. Frontiers in Robotics and AI, 5, 6. Conway, D., Dick, I., Li, Z., Wang, Y., & Chen, F. (2013). The Effect of Stress on Cognitive Load Measurement. In P. Kotzé, G. Marsden, G. Lindgaard, J. Wesson, & M. Winckler (Eds.), Human-Computer Interaction – INTERACT 2013 SE - 58 (Vol. 8120, pp. 659–666). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642- 40498-6_58 Cormen, T. H. (2009). Introduction to algorithms. MIT press. Cornelius, C. V. M., Lynch, C. J., & Gore, R. (2017). Aging out of crime: exploring the relationship between age and crime with agent based modeling. In Proceedings of the Agent-Directed Simulation Symposium (p. 3). Society for Computer Simulation International. Corno, F., De Russis, L., & Sáenz, J. P. (2017). Pain Points for Novice Programmers of Ambient Intelligence Systems: an Exploratory Study. In Computer Software and Applications Conference (COMPSAC), 2017 IEEE 41st Annual (Vol. 1, pp. 250– 255). IEEE. Costa, A., Novais, P., & Julian, V. (2018). A Survey of Cognitive Assistants BT - Personal Assistants: Emerging Computational Technologies. In A. Costa, V. Julian, & P. Novais (Eds.) (pp. 3–16). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-62530-0_1 Crooks, A. T., & Hailegiorgis, A. B. (2014). An agent-based modeling approach applied to the spread of cholera. Environmental Modelling & Software, 62, 164–177. Csathó, Á., Van Der Linden, D., Hernádi, I., Buzás, P., & Kalmar, G. (2012). Effects of mental fatigue on the capacity limits of visual attention. Journal of Cognitive Psychology, 24(5), 511–524. D’Mello, S. K., Lehman, B., & Person, N. K. (2010). Expert Tutors Feedback Is Immediate, Direct, and Discriminating. In FLAIRS Conference. Daitkar, A. R. (2017). Effect of Achievement Motivation on Personality Traits of Students. The International Journal of Indian Psychology, 4(2), 25–29. Das, R., Kamruzzaman, J., & Karmakar, G. (2018). Modelling majority and expert influences on opinion formation in online social networks. World Wide Web, 21(3), 663–685. Dautenhahn, K. (1995). Getting to know each other—artificial social intelligence for autonomous robots. Robotics and Autonomous Systems, 16(2–4), 333–356. Dautenhahn, K. (1997). I could be you: The phenomenological dimension of social understanding. Cybernetics & Systems, 28(5), 417–453. Dautenhahn, K., Nehaniv, C. L., Walters, M. L., Robins, B., Kose-Bagci, H., Mirza, N. A., & Blow, M. (2009). KASPAR–a minimally expressive humanoid robot for human–robot interaction research. Applied Bionics and Biomechanics, 6(3–4), 369– 397. David, N. (2013). Validating simulations. In Simulating Social Complexity (pp. 135–171). Springer. de Jong, M., Zhang, K., Roth, A. M., Rhodes, T., Schmucker, R., Zhou, C., … Veloso, M. (2018). Towards a Robust Interactive and Learning Social Robot. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (pp. 883–891). International Foundation for Autonomous Agents and Multiagent Systems. De Jong, T. (2010). Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science, 38(2), 105–134. DeStefano, D., & LeFevre, J.-A. (2007). Cognitive load in hypertext reading: A review. Computers in Human Behavior, 23(3), 1616–1641. https://doi.org/http://dx.doi.org/10.1016/j.chb.2005.08.012 Detje, F., Dorner, D., & Schaub, H. (2003). The Logic of Cognitive Systems: Proceedings of the Fifth International Conference on Cognitive Modeling, ICCM’03. Universitäts-Verlag Bamberg. Dey, N., & Ashour, A. S. (2017). Ambient Intelligence in Healthcare: A State-of-the-Art. Global Journal of Computer Science and Technology. Dilshad, M., Adnan, A., & Akram, A. (2013). Gender Differences in Reading Habits of University Students: An Evidence from Pakistan. Pakistan Journal of Social Sciences (PJSS), 33(2), 311–320. Drogoul, A., Vanbergue, D., & Meurisse, T. (2003). Multi-agent Based Simulation: Where Are the Agents? In J. Simão Sichman, F. Bousquet, & P. Davidsson (Eds.), Multi-Agent-Based Simulation II SE - 1 (Vol. 2581, pp. 1–15). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-36483-8_1 Dubowsky, S., Genot, F., Godding, S., Kozono, H., Skwersky, A., Yu, H., & Yu, L. S. (2000). PAMM-a robotic aid to the elderly for mobility assistance and monitoring: a “helping-hand” for the elderly. In In Proceedings of IEEE International Conference on Robotics and Automation (Vol. 1, pp. 570–576). IEEE. Duell, R. (2016). Making Up Your Mind: An Exploration into Analysis and Support in Individual and Social Contexts (doctoral’s thesis). Duell, R., & Treur, J. (2012). A Computational Analysis of Joint Decision Making Processes. In K. Aberer, A. Flache, W. Jager, L. Liu, J. Tang, & C. Guéret (Eds.), Social Informatics SE - 22 (Vol. 7710, pp. 292–308). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-35386-4_22. Durães, D., Castro, D., Bajo, J., & Novais, P. (2017). Modelling an Intelligent Interaction System for Increasing the Level of Attention BT - Ambient Intelligence– Software and Applications – 8th International Symposium on Ambient Intelligence (ISAmI 2017). In J. F. De Paz, V. Julián, G. Villarrubia, G. Marreiros, & P. Novais (Eds.) (pp. 210–217). Cham: Springer International Publishing. Durantin, G., Gagnon, J.-F., Tremblay, S., & Dehais, F. (2014). Using near infrared spectroscopy and heart rate variability to detect mental overload. Behavioural Brain Research, 259, 16–23. Dutta-Moscato, J., Solovyev, A., Mi, Q., Nishikawa, T., Soto-Gutierrez, A., Fox, I. J., & Vodovotz, Y. (2014). A multiscale agent-based in silico model of liver fibrosis progression. Frontiers in Bioengineering and Biotechnology, 2, 18. Eason, S. H., Goldberg, L. F., Young, K. M., Geist, M. C., & Cutting, L. E. (2012). Reader–text interactions: How differential text and question types influence cognitive skills needed for reading comprehension. Journal of Educational Psychology, 104(3), 515. Eguchi, A., & Okada, H. (2017). Social Robots: How Becoming an Active User Impacts Students’ Perceptions. In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 111–112). ACM. Eguchi, A., & Okada, H. (2018). If You Give Students a Social Robot?-World Robot Summit Pilot Study. In Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 103–104). ACM. Elbaum, B., Vaughn, S., Tejero Hughes, M., & Watson Moody, S. (2000). How effective are one-to-one tutoring programs in reading for elementary students at risk for reading failure? A meta-analysis of the intervention research. Journal of Educational Psychology, 92(4), 605. Eldabi, T. T., & Young, T. (2007). Towards a framework for healthcare simulation. In Simulation Conference, 2007 Winter (pp. 1454–1460). IEEE. Elliott, S. N., Kurz, A., Beddow, P., & Frey, J. (2009). Cognitive load theory: Instruction- based research with applications for designing tests. In Proceedings of the National Association of School Psychologists’ Annual Convention, Boston, MA, February (Vol. 24). Ellis, A. W. (2014). Reading, writing and dyslexia: A cognitive analysis. Psychology Press. Ellner, S. P., & Guckenheimer, J. (2006). Dynamic Models in Biology. Princeton University press. Endres, C. (2012). Real-time Assessment of Driver Cognitive Load as a prerequisite for the situation-aware Presentation Toolkit PresTK. In Adjunct Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2012), Portsmouth, New Hampshire, USA (pp. 76–79). Esposito, F., Otto, T., Zijlstra, F. R. H., & Goebel, R. (2014). Spatially distributed effects of mental exhaustion on resting-state FMRI networks. PloS One, 9(4). Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19(5), 329–335. Fasola, J., & Mataric, M. (2011). Comparing physical and virtual embodiment in a socially assistive robot exercise coach for the elderly. Center for Robotics and Embedded Systems, Los …. Retrieved from http://cres.usc.edu/Research/files/Fasola_11_003.pdf Feil-Seifer, D., & Mataric, M. J. (2011). Socially assistive robotics. Robotics & Automation Magazine, IEEE, 18(1), 465–468. https://doi.org/10.1109/ICORR.2005.1501143. Foasberg, N. M. (2011). Adoption of e-book readers among college students: A survey. Information Technology and Libraries, 30(3). Foasberg, N. M. (2014). Student reading practices in print and electronic media. College & Research Libraries, 75(5), 705–723. Fong, S. F., Lily, L. P. L., & Por, F. P. (2012). Reducing cognitive overload among students of different anxiety levels using segmented animation. Procedia-Social and Behavioral Sciences, 47, 1448–1456. Ford, M. Lou. (2014). Active Learning Strategies and First Grade Reading Achievement Using the TAKE 10! Program and Istation Assessment Tool: A Correlation Study. JONES INTERNATIONAL UNIVERSITY. Formolo, D., Van Ments, L., & Treur, J. (2017). A computational model to simulate development and recovery of traumatised patients. Biologically Inspired Cognitive Architectures. https://doi.org/https://doi.org/10.1016/j.bica.2017.07.002 Fox, E., & Alexander, P. A. (2011). Learning to read. Handbook of Research on Learning and Instruction, 7–31. Freedman, L. S., Midthune, D., Dodd, K. W., Carroll, R. J., & Kipnis, V. (2015). A statistical model for measurement error that incorporates variation over time in the target measure, with application to nutritional epidemiology. Statistics in Medicine. Fritz, C., Ellis, A. M., Demsky, C. A., Lin, B. C., & Guros, F. (2013). Embracing work breaks. Organizational Dynamics, 42, 274–280. Gagliardi, F. (2007). Some Issues About Cognitive Modelling and Functionalism. In AI* IA 2007: Artificial Intelligence and Human-Oriented Computing (pp. 60–71). Springer. Galy, E., Cariou, M., & Mélan, C. (2012). What is the relationship between mental workload factors and cognitive load types? International Journal of Psychophysiology, 83(3), 269–275. Gbenga, A. J. (2012). Mathematical modeling and analysis of HIV/AIDS control measures. University of the Western Cape. Giachetti, R. E., Marcelli, V., Cifuentes, J., & Rojas, J. A. (2013). An agent-based simulation model of human-robot team performance in military environments. Systems Engineering, 16(1), 15–28. Gillmor, S. C., Poggio, J., & Embretson, S. (2015). Effects of reducing the cognitive load of mathematics test items on student performance. Numeracy, 8(1), 4. Giménez, A., Balaguer, C., Sabatini, A. M., & Genovese, V. (2003). The MATS robotic system to assist disabled people in their home environments. In In Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (Vol. 3, pp. 2612–2617). IEEE. Glover, J. (2003). A robotically-augmented walker for older adults. Technical Report,Computer Science Department, Carnegie Mellon University. Goedschalk, L., Treur, J., & Verwolf, R. (2018). A Network-Oriented Modeling Approach to Voting Behavior During the 2016 US Presidential Election. In F. la Prieta, Z. Vale, L. Antunes, T. Pinto, A. T. Campbell, V. Julián, … M. N. Moreno (Eds.), Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017 (pp. 3–15). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-61578-3_1 Gog, T., Kirschner, F., Kester, L., & Paas, F. (2012). Timing and frequency of mental effort measurement: Evidence in favour of repeated measures. Applied Cognitive Psychology, 26(6), 833–839. Gordon, G., & Breazeal, C. (2015). Bayesian Active Learning-based Robot Tutor for Children’s Word-Reading Skills. Retrieved from http://robotshelpingkids.yale.edu/sites/default/files/files/GordonBreazeal_AAAI_20 15_final.pdf Gordon, M., & Breazeal, C. (2015). Designing a virtual assistant for in-car child entertainment. In Proceedings of the 14th International Conference on Interaction Design and Children (pp. 359–362). ACM. Griffin, P., Burns, M. S., & Snow, C. E. (1998). Preventing reading difficulties in young children. National Academies Press. Groccia, J. E. (2018). What Is Student Engagement? New Directions for Teaching and Learning, 2018(154), 11–20. Grzeschik, K., Kruppa, Y., Marti, D., & Donner, P. (2011). Reading in 2110-reading behavior and reading devices: a case study. The Electronic Library, 29(3), 288–302. Hackel, M., Schwope, S., Fritsch, J., Wrede, B., & Sagerer, G. (2005). Humanoid robot platform suitable for studying embodied interaction. In International Conference on Intelligent Robots and Systems,2005 IEEE/RSJ (pp. 2443–2448). IEEE. Haer, T., Botzen, W. J. W., & Aerts, J. C. J. H. (2016). The effectiveness of flood risk communication strategies and the influence of social networks—Insights from an agent-based model. Environmental Science & Policy, 60, 44–52. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Canonical correlation: A supplement to multivariate data analysis. Multivariate Data Analysis: A Global Perspective. 7th Edn. Pearson Prentice Hall Publishing, Upper Saddle River. Hanken, K., Eling, P., & Hildebrandt, H. (2015). Is there a cognitive signature for MS- related fatigue? Multiple Sclerosis Journal, 21(4), 376–381. Hannon, B., & Ruth, M. (2014). Modeling Dynamic Biological Systems. In B. Hannon & M. Ruth (Eds.) (pp. 3–28). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-05615-9_1. Hans, M., Graf, B., & Schraft, R. D. (2002). Robotic home assistant care-o-bot: Past- present-future. In In Proceedings 11th IEEE International Workshop on Robot and Human Interactive Communication (pp. 380–385). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1045652. Harbluk, J. L., Noy, Y. I., Trbovich, P. L., & Eizenman, M. (2007). An on-road assessment of cognitive distraction: Impacts on drivers’ visual behavior and braking performance. Accident Analysis & Prevention, 39(2), 372–379. Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in Psychology, 52, 139–183. Hashim, S. F. S. M., Fikry, A., Ismail, Z., Musa, R., Hashim, R., Ahmad, S. S., … Samat, N. (2014). Humanoids in Autism Therapy: The Child Perspective. Medical and Rehabilitation Robotics and Instrumentation (MRRI2013),Procedia Computer Science, 42(0), 351–356. https://doi.org/http://dx.doi.org/10.1016/j.procs.2014.11.073. Heerink, M., Krose, B., Evers, V., & Wielinga, B. (2009). Measuring acceptance of an assistive social robot: a suggested toolkit. In The 18th IEEE International Symposium on Robot and Human Interactive Communication (pp. 528–533). Toyama, Japan: IEEE. Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Assessing Acceptance of Assistive Social Agent Technology by Older Adults: the Almere Model. International Journal of Social Robotics, 2(4), 361–375. https://doi.org/10.1007/s12369-010-0068-5. Henning, R. A., Jacques, P., Kissel, G. V, Sullivan, A. B., & Alteras-webb, S. M. (1997). Frequent short rest breaks from computer work: effects on productivity and well- being at two field sites. Ergonomics, 40(1), 78–91. https://doi.org/10.1080/001401397188396. Heras, S., Palanca, J., & Chesñevar, C. I. (2018). Argumentation-Based Personal Assistants for Ambient Assisted Living. In Personal Assistants: Emerging Computational Technologies (pp. 19–36). Springer. Herbers, J. E., Cutuli, J. J., Supkoff, L. M., Heistad, D., Chan, C.-K., Hinz, E., & Masten, A. S. (2012). Early reading skills and academic achievement trajectories of students facing poverty, homelessness, and high residential mobility. Educational Researcher, 41(9), 366–374. Ho, C., & Spence, C. (2005). Assessing the effectiveness of various auditory cues in capturing a driver’s visual attention. Journal of Experimental Psychology: Applied, 11(3), 157. Hockey, G. R. J. (1997). Compensatory control in the regulation of human performance under stress and high workload: A cognitive-energetical framework. Biological Psychology, 45(1), 73–93. Hone, K., Akhtar, F., & Saffu, M. (2003). Affective agents to reduce user frustration: the role of agent embodiment. In Proceedings of Human-Computer Interaction (HCI2003), Bath, UK. Hoogendoorn, M., Jaffry, S. W., van Maanen, P.-P., & Treur, J. (2014). Design and validation of a relative trust model. Knowledge-Based Systems, 57(0), 81–94. https://doi.org/http://dx.doi.org/10.1016/j.knosys.2013.12.012 Hoogendoorn, M., Klein, M. C. A., Memon, Z. A., & Treur, J. (2013). Formal specification and analysis of intelligent agents for model-based medicine usage management. Computers in Biology and Medicine, 43(5), 444–457. Hoogendoorn, M., Merk, R.-J., & Treur, J. (2010). An agent model for decision making based upon experiences applied in the domain of fighter pilots. In 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 101–108). IEEE. Hoonakker, P., Carayon, P., Gurses, A. P., Brown, R., Khunlertkit, A., McGuire, K., & Walker, J. M. (2011). Measuring workload of ICU nurses with a questionnaire survey: the NASA task load index (TLX). IIE Transactions on Healthcare Systems Engineering, 1(2), 131–143. Hsu, S.-C., Weng, K.-W., Cui, Q., & Rand, W. (2016). Understanding the complexity of project team member selection through agent-based modeling. International Journal of Project Management, 34(1), 82–93. Hulme, C., & Mackenzie, S. (2014). Working Memory and Severe Learning Difficulties (PLE: Memory). Psychology Press. Hümeyra, G., & Gülözer, K. (2013). The effect of cognitive load associated with instructional formats and types of presentation on second language reading comprehension performance. The Turkish Online Journal of Educational Technology, 12(4). Hunt, L. C. (1970). The effect of self-selection, interest, and motivation upon independent, instructional, and frustational levels. The Reading Teacher, 146–158. Hunter, E. M., & Wu, C. (2016). Give me a better break: Choosing workday break activities to maximize resource recovery. Journal of Applied Psychology, 101(2), 302. Hussain, M. S., Calvo, R. A., & Chen, F. (2013). Automatic cognitive load detection from face, physiology, task performance and fusion during affective interference. Interacting with Computers, 26(3), iwt032. Hussain, S., Chen, S., Calvo, R. A., & Chen, F. (2011). Classification of Cognitive Load from Task Performance & Multichannel Physiology during Affective Changes. In MMCogEmS: Inferring Cognitive and Emotional States from Multimodal Measures, ICMI 2011 Workshop, Alicante, Spain. Huttunen, K., Keränen, H., Väyrynen, E., Pääkkönen, R., & Leino, T. (2011). Effect of cognitive load on speech prosody in aviation: Evidence from military simulator flights. Applied Ergonomics, 42(2), 348–357. Inoue, K., Wada, K., & Uehara, R. (2012). How Effective Is Robot Therapy?: PARO and People with Dementia. In Á. Jobbágy (Ed.), 5th European Conference of the International Federation for Medical and Biological Engineering SE - 204 (Vol. 37, pp. 784–787). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642- 23508-5_204 Jaber, M. Y., Givi, Z. S., & Neumann, W. P. (2013). Incorporating human fatigue and recovery into the learning–forgetting process. Applied Mathematical Modelling, 37(12), 7287–7299. Jalani, N. H., & Sern, L. C. (2015). The Example-Problem-Based Learning Model: Applying Cognitive Load Theory. Procedia-Social and Behavioral Sciences, 195, 872–880. Jayawardena, C., Kuo, I.-H., Broadbent, E., & MacDonald, B. A. (2016). Socially assistive robot healthbot: Design, implementation, and field trials. IEEE Systems Journal, 10(3), 1056–1067. Johal, W. (2015). Robots Interacting with Style. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (pp. 191–192). ACM. John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of Personality: Theory and Research, 2(1999), 102–138. Jonker, C. M., Treur, J., & Wijngaards, W. C. A. (2003). A temporal modelling environment for internally grounded beliefs, desires and intentions. Cognitive Systems Research, 4(3), 191–210. Joseph, S. (2013). Measuring cognitive load: A comparison of self-report and physiological methods (doctoral’s thesis). Arizona State University Tempe, AZ. Jung, Y., & Lee, K. M. (2004). Effects of physical embodiment on social presence of social robots. Proceedings of PRESENCE, 80–87. Kalyuga, S. (2011a). Cognitive Load in Adaptive Multimedia Learning. In R. A. Calvo & S. K. D’Mello (Eds.), New Perspectives on Affect and Learning Technologies SE - 15 (Vol. 3, pp. 203–215). Springer New York. https://doi.org/10.1007/978-1-4419- 9625-1_15 Kalyuga, S. (2011b). Cognitive Load Theory: How Many Types of Load Does It Really Need? Educational Psychology Review, 23(1), 1–19. https://doi.org/10.1007/s10648-010-9150-7 Kalyuga, S. (2011c). Cognitive Load Theory: Implications for Affective Computing. In In proceeding of the Twenty-fourth International Florida Artificial Intelligence Research society Conference. Kalyuga, S. (2012). Instructional benefits of spoken words: A review of cognitive load factors. Educational Research Review, 7(2), 145–159. https://doi.org/http://dx.doi.org/10.1016/j.edurev.2011.12.002 Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31. Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13(4), 351–371. Kalyuga, S., & Renkl, A. (2010). Expertise reversal effect and its instructional implications: Introduction to the special issue. Instructional Science, 38(3), 209–215. Kanda, T., Hirano, T., Eaton, D., & Ishiguro, H. (2003). Person identification and interaction of social robots by using wireless tags. In In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (Vol. 2, pp. 1657–1664). IEEE. Kawaguchi, I., Kodama, Y., Kuzuoka, H., Otsuki, M., & Suzuki, Y. (2016). Effect of Embodiment Presentation by Humanoid Robot on Social Telepresence. In Proceedings of the Fourth International Conference on Human Agent Interaction (pp. 253–256). ACM. Kawamura, K., Bagchi, S., Iskarous, M., & Bishay, M. (1995). Intelligent robotic systems in service of the disabled. IEEE Transactions on Rehabilitation Engineering, 3(1), 14–21. Khan, F. A., Graf, S., Weippl, E. R., & Tjoa, A. M. (2010). Identifying and Incorporating Affective States and Learning Styles in Web-based Learning Management Systems. IxD&A, 9, 85–103. Khawaja, M. A., Chen, F., Owen, C., & Hickey, G. (2009). Cognitive load measurement from user’s linguistic speech features for adaptive interaction design. In Human- Computer Interaction–INTERACT 2009 (pp. 485–489). Springer. Kidd, C. D., & Breazeal, C. (2004). Effect of a robot on user perceptions. In Proceedings International Conference on Intelligent Robots and Systems . (Vol. 4, pp. 3559– 3564). IEEE. Kidd, C. D., & Breazeal, C. (2005). Sociable robot systems for real-world problems. In International Workshop on Robot and Human Interactive Communication. (pp. 353– 358). IEEE. Kidd, C. D., & Breazeal, C. (2008). Robots at home: Understanding long-term human- robot interaction. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3230–3235). IEEE. Kirou, A., Ruszczycki, B., Walser, M., & Johnson, N. (2008). Computational Modeling of Collective Human Behavior: The Example of Financial Markets. In M. Bubak, G. van Albada, J. Dongarra, & P. A. Sloot (Eds.), Computational Science – ICCS 2008 SE - 8 (Vol. 5101, pp. 33–41). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-69384-0_8 Kirschner, P. A. (2002). Cognitive load theory: implications of cognitive load theory on the design of learning. Learning and Instruction, 12(1), 1–10. https://doi.org/https://doi.org/10.1016/S0959-4752(01)00014-7. Klein, M. C. A., Manzoor, A., Middelweerd, A., Mollee, J. S., & te Velde, S. J. (2015). Encouraging Physical Activity via a Personalized Mobile System. Internet Computing, IEEE. https://doi.org/10.1109/MIC.2015.51. Klein, M. C. A., Manzoor, A., & Mollee, J. S. (2017). Active2Gether: A Personalized m-Health Intervention to Encourage Physical Activity. Sensors, 17(6), 1436. Kleinberger, T., Jedlitschka, A., Storf, H., Steinbach-Nordmann, S., & Prueckner, S. (2009). An approach to and evaluations of assisted living systems using ambient intelligence for emergency monitoring and prevention. In International Conference on Universal Access in Human-Computer Interaction (pp. 199–208). Springer. Klingner, J., Boelé, A., Linan-Thompson, S., & Rodriguez, D. (2014). Essential Components of Special Education for English Language Learners With Learning Disabilities. Position Statement of the Division for Learning Disabilities of the Council for Exceptional Children. Arlington, VA: Council for Exceptional Children Division for Learning Disabilities. Knoll, A., Wang, Y., Chen, F., Xu, J., Ruiz, N., Epps, J., & Zarjam, P. (2011). Measuring cognitive workload with low-cost electroencephalograph. In Human-Computer Interaction–INTERACT 2011 (pp. 568–571). Springer. Koedinger, K. R., & Aleven, V. (2007). Exploring the Assistance Dilemma in Experiments with Cognitive Tutors. Educational Psychology Review, 19(3), 239– 264. https://doi.org/10.1007/s10648-007-9049-0 Kolfschoten, G. L. (2011). Cognitive Load in Collaboration-Brainstorming. In 44th Hawaii International Conference on System Sciences (HICSS) (pp. 1–9). IEEE. Korn, G. A., & Korn, T. M. (2000). Mathematical handbook for scientists and engineers: definitions, theorems, and formulas for reference and review. Courier Corporation. Korzun, D. G. (2017). Internet of Things Meets Mobile Health Systems in Smart Spaces: An Overview. In Internet of Things and Big Data Technologies for Next Generation Healthcare (pp. 111–129). Springer. Kruzikas, D. T., Higashi, M. K., Edgar, M., Macal, C. M., Graziano, D. J., North, M. J., & Collier, N. T. (2014). Using agent-based modeling to inform regional health care system investment and planning. In International Conference onComputational Science and Computational Intelligence (CSCI), (Vol. 2, pp. 211–214). IEEE. Kühnel, J., Zacher, H., de Bloom, J., & Bledow, R. (2017). Take a break! Benefits of sleep and short breaks for daily work engagement. European Journal of Work and Organizational Psychology, 26(4), 481–491. https://doi.org/10.1080/1359432X.2016.1269750 Kumar, A., Prakash, J., & Dutt, V. (2014). Understanding Human Driving Behavior through Computational Cognitive Modeling. In R.-H. Hsu & S. Wang (Eds.), Internet of Vehicles – Technologies and Services SE - 6 (Vol. 8662, pp. 56–65). Springer International Publishing. https://doi.org/10.1007/978-3-319-11167-4_6 Kumar, P., Verma, P., Singh, R., & Patel, R. K. (2017). A Novel Design of Inexpensive, Heavy Payload and High Mobility ORQ Robot. In Proceeding of International Conference on Intelligent Communication, Control and Devices (pp. 979–989). Springer. Kwakkel, G., Kollen, B. J., & Krebs, H. I. (2007). Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabilitation and Neural Repair. Lee, J. D. (2014). Dynamics of driver distraction: The process of engaging and disengaging. Annals of Advances in Automotive Medicine, 58, 24. Lee, Y.-C., Lee, J. D., & Boyle, L. N. (2007). Visual attention in driving: The effects of cognitive load and visual disruption. Human Factors: The Journal of the Human Factors and Ergonomics Society, 49(4), 721–733. Lehmann, H., Roncone, A., Pattacini, U., & Metta, G. (2016). Physiologically Inspired Blinking Behavior for a Humanoid Robot. In International Conference on Social Robotics (pp. 83–93). Springer. Leppink, J. (2014). Managing the load on a reader’s mind. Perspectives on Medical Education, 3(5), 327–328. Lewis, J. J. R., & Sauro, J. (2017). Revisiting the Factor Structure of the System Usability Scale. Journal of Usability Studies, 12(4). Leyzberg, D., Spaulding, S., & Scassellati, B. (2014). Personalizing robot tutors to individuals’ learning differences. In Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction (pp. 423–430). ACM. Liu, Y., Raker, J. R., & Lewis, J. E. (2018). Evaluating student motivation in organic chemistry courses: moving from a lecture-based to a flipped approach with peer-led team learning. Chemistry Education Research and Practice. Lo, A. C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., … Volpe, B. T. (2010). Robot-assisted therapy for long-term upper-limb impairment after stroke. New England Journal of Medicine, 362(19), 1772–1783. Lopez, B., & Andres, I. (2014). Powerpoint design based on cognitive load theory and cognitive theory of multimedia learning for introduction to statistics. University of Southern California. Lorist, M. M., Klein, M., Nieuwenhuis, S., De Jong, R., Mulder, G., & Meijman, T. F. (2000). Mental fatigue and task control: planning and preparation. Psychophysiology, 37(5), 614–625. Lytridis, C., Vrochidou, E., Chatzistamatis, S., & Kaburlasos, V. (2018). Social Engagement Interaction Games Between Children with Autism and Humanoid Robot NAO. In The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications (pp. 562–570). Springer. Macindoe, O., & Maher, M. (2005). Intrinsically Motivated Intelligent Rooms. In T. Enokido, L. Yan, B. Xiao, D. Kim, Y. Dai, & L. Yang (Eds.), Embedded and Ubiquitous Computing – EUC 2005 Workshops SE - 20 (Vol. 3823, pp. 189–197). Springer Berlin Heidelberg. https://doi.org/10.1007/11596042_20 Mahoney, R. M., van Der Loos, H. F., Lum, P. S., & Burgar, C. (2003). Robotic stroke therapy assistant. Robotica, 21(01), 33–44. Makonin, S., Bartram, L., & Popowich, F. (2013). A smarter smart home: Case studies of ambient intelligence. IEEE Pervasive Computing, 12(1), 58–66. Mangen, A., Walgermo, B. R., & Brønnick, K. (2013). Reading linear texts on paper versus computer screen: Effects on reading comprehension. International Journal of Educational Research, 58, 61–68. Mann, J. A., MacDonald, B. A., Kuo, I.-H., Li, X., & Broadbent, E. (2015). People respond better to robots than computer tablets delivering healthcare instructions. Computers in Human Behavior, 43, 112–117. Marcora, S. M., Staiano, W., & Manning, V. (2009). Mental fatigue impairs physical performance in humans. Journal of Applied Physiology, 106(3), 857–864. Marschark, M., Sarchet, T., Convertino, C. M., Borgna, G., Morrison, C., & Remelt, S. (2012). Print exposure, reading habits, and reading achievement among deaf and hearing college students. Journal of Deaf Studies and Deaf Education, 17(1), 61–74. Marti, P., Giusti, L., & Bacigalupo, M. (2008). Dialogues beyond words. Interaction Studies. Martín, D., Alcarria, R., Sánchez-Picot, Á., & Robles, T. (2015). An ambient intelligence framework for end-user service provisioning in a hospital pharmacy: a case study. Journal of Medical Systems, 39(10), 116. Martin, S. (2014). Measuring cognitive load and cognition: metrics for technology-enhanced learning. Educational Research and Evaluation, 20(7–8), 592–621. Martinez-Martin, E., & del Pobil, A. P. (2018). Personal Robot Assistants for Elderly Care: An Overview BT - Personal Assistants: Emerging Computational Technologies. In A. Costa, V. Julian, & P. Novais (Eds.) (pp. 77–91). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-62530-0_5 Mast, M., Burmester, M., Graf, B., Weisshardt, F., Arbeiter, G., Španěl, M., … Kronreif, G. (2015). Design of the human-robot interaction for a semi-autonomous service robot to assist elderly people. In Ambient Assisted Living (pp. 15–29). Springer. Matarić, M. (2014). Socially assistive robotics: human-robot interaction methods for creating robots that care. In Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction (p. 333). ACM. Mataric, M. J. (2005). The role of embodiment in assistive interactive robotics for the elderly. In AAAI fall symposium on caring machines: AI for the elderly, Arlington, VA. Matarić, M. J., & Scassellati, B. (2016). Socially assistive robotics. In Springer Handbook of Robotics (pp. 1973–1994). Springer. Mayer, R. E. (1996). Learning strategies for making sense out of expository text: The SOI model for guiding three cognitive processes in knowledge construction. Educational Psychology Review, 8(4), 357–371. Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions? Educational Psychologist, 32(1), 1–19. https://doi.org/10.1207/s15326985ep3201_1 Mayer, R. E. (2002). Multimedia learning. Psychology of Learning and Motivation, 41, 85–139. Mayer, R. E. (2005). The Cambridge handbook of multimedia learning. Cambridge University Press. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. Mc Auley, M., & Mooney, K. (2018). Chapter 7 - Using Computational Models to Study Aging. In J. L. Ram & P. M. B. T.-C. H. of M. for H. A. (Second E. Conn (Eds.) (pp. 79–91). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-811353- 0.00007-5 McCarthy, R., & Achenie, L. E. K. (2017). Agent-based modeling–Proof of concept application to membrane separation and hydrogen storage in a MOF. Computers & Chemical Engineering, 107, 151–157. McMullan, M. (2018). Evaluation of a medication calculation mobile app using a cognitive load instructional design. International Journal of Medical Informatics. Medeiros, L., & Bosse, T. (2017). An Empathic Agent that Alleviates Stress by Providing Support via Social Media. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (pp. 1634–1636). International Foundation for Autonomous Agents and Multiagent Systems. Medeiros, L., & van der Wal, C. N. (2017). An Agent-Based Model Predicting Group Emotion and Misbehaviours in Stranded Passengers. In Portuguese Conference on Artificial Intelligence (pp. 28–40). Springer. Michalowski, M. P., Sabanovic, S., & Kozima, H. (2007). A dancing robot for rhythmic social interaction. In 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), (pp. 89–96). IEEE. Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 81. Mills, C., Bosch, N., Graesser, A., & D’Mello, S. (2014). To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns. In Intelligent Tutoring Systems (pp. 19–28). Springer. Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A. M., & D’Mello, S. K. (2017). Put your thinking cap on: detecting cognitive load using EEG during learning. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 80–89). ACM. Mizuno, K., Tanaka, M., Yamaguti, K., Kajimoto, O., Kuratsune, H., & Watanabe, Y. (2011). Mental fatigue caused by prolonged cognitive load associated with sympathetic hyperactivity. Behavioral and Brain Functions, 7(1), 1–7. https://doi.org/10.1186/1744-9081-7-17 Möckel, T., Beste, C., & Wascher, E. (2015). The effects of time on task in response selection-an erp study of mental fatigue. Scientific Reports, 5, 10113. Mohammed, H., Aziz, A. A., & Ahmad, R. (2015). Exploring the need of an assistive robot to support reading process: A pilot study. In 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR) (pp. 35–40). Kuala Lumpur: IEEE. Mollee, J. S., & Klein, M. C. A. (2017a). Empirical Validation of a Computational Model of Influences on Physical Activity Behavior. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 353–363). Springer. Mollee, J. S., & Klein, M. C. A. (2017b). Empirical Validation of a Computational Model of Influences on Physical Activity Behavior BT - Advances in Artificial Intelligence: From Theory to Practice. In S. Benferhat, K. Tabia, & M. Ali (Eds.) (pp. 353–363). Cham: Springer International Publishing. Mollee, J., & van der Wal, C. N. (2013). A Computational Agent Model of Influences on Physical Activity Based on the Social Cognitive Theory. In G. Boella, E. Elkind, B. Savarimuthu, F. Dignum, & M. Purvis (Eds.), PRIMA 2013: Principles and Practice of Multi-Agent Systems SE - 37 (Vol. 8291, pp. 478–485). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-44927-7_37 Moody, D. L. (2004). Cognitive load effects on end user understanding of conceptual models: An experimental analysis. In Advances in Databases and Information Systems (pp. 129–143). Springer. Moradmand, N., Datta, A., & Oakley, G. (2014). An Interactive Multimedia Development Life Cycle Model Based on a Cognitive Theory of Multimedia Learning. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (Vol. 2014, pp. 746–761). Mori, M. (1970). Bukimi no tani [the Uncanny Valley]. Energy, 7(4), 33–35. Mostafa, S. A., Ahmad, M. S., Mustapha, A., & Mohammed, M. A. (2017). A Concise Overview of Software Agent Research, Modeling, and Development. Software Engineering, 5, 8–25. Mui, L., Mohtashemi, M., & Halberstadt, A. (2002). A Computational Model of Trust and Reputation. In Proceedings of the 35th Hawaii International Conference on System Sciences, 00(c), 1–9. https://doi.org/10.1109/HICSS.2002.994181 Mumm, J., & Mutlu, B. (2011). Designing motivational agents: The role of praise, social comparison, and embodiment in computer feedback. Computers in Human Behavior, 27(5), 1643–1650. Mustapha, R., Yousif, Y., & Aziz, A. aziz. (2017). A Computational Agent Model of Automaticity for Driver’s Training. IOP Conference Series: Materials Science and Engineering, 226(1), 12083. Retrieved from http://stacks.iop.org/1757- 899X/226/i=1/a=012083 Nadolski, R. J., Kirschner, P. A., van Merriënboer, J. J. G., & Wöretshofer, J. (2005). Development of an instrument for measuring the complexity of learning tasks. Educational Research and Evaluation, 11(1), 1–27. Nakashima, H., Hirata, K., & Ochiai, J. (2017). Realization of Mobility as a Service in View of Ambient Intelligence. In Serviceology for Smart Service System (pp. 111– 116). Springer. Naze, S., & Treur, J. (2012). A computational model for development of post-traumatic stress disorders by hebbian learning. In T. Huang, Z. Zeng, C. Li, & C. Leung (Eds.), Neural Information Processing SE - 18 (Vol. 7664, pp. 141–151). Springer. https://doi.org/10.1007/978-3-642-34481-7_18 Neerincx, M. A., Harbers, M., Lim, D., & van der Tas, V. (2014). Automatic feedback on cognitive load and emotional state of traffic controllers. In Engineering Psychology and Cognitive Ergonomics (pp. 42–49). Springer. Neerincx, M. A., Veltman, J. A., Grootjen, M., & Veenendaal, J. (2003). A model for cognitive task load prediction: Validation and application. In Proceedings of the 15th Triennial Congress of the the International Ergonomics Association. Seoul, Korea. Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126. Nicholls, S., Amelung, B., & Student, J. (2017). Agent-based modeling: A powerful tool for tourism researchers. Journal of Travel Research, 56(1), 3–15. Nicholson, B., & O’Hare, D. (2014). The effects of individual differences, prior experience and cognitive load on the transfer of dynamic decision-making performance. Ergonomics, (August 2015), 1–13. https://doi.org/10.1080/00140139.2014.933884 Niculescu, A., Cao, Y., & Nijholt, A. (2010). Manipulating stress and cognitive load in conversational interactions with a multimodal system for crisis management support. In Development of Multimodal Interfaces: Active Listening and Synchrony (pp. 134– 147). Springer. Nikolic, I., & Ghorbani, A. (2011). A method for developing agent-based models of socio- technical systems. In 2011 ieee international conference on Networking, sensing and control (icnsc), (pp. 44–49). IEEE. Noels, K. A., Clément, R., & Pelletier, L. G. (1999). Perceptions of teachers’ communicative style and students’ intrinsic and extrinsic motivation. The Modern Language Journal, 83(1), 23–34. Norling, E. (2004). Folk psychology for human modelling: Extending the BDI paradigm. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 1 (pp. 202–209). IEEE Computer Society. Norling, E. J. (2009). Modelling human behaviour with BDI agents, PhD thesis. University of Melbourne. Retrieved from http://hdl.handle.net/11343/37081 Nourbakhsh, N., Wang, Y., & Chen, F. (2013). GSR and blink features for cognitive load classification. In Human-Computer Interaction–INTERACT 2013 (pp. 159–166). Springer. Nourbakhsh, N., Wang, Y., Chen, F., & Calvo, R. A. (2012). Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks. In Proceedings of the 24th Australian Computer-Human Interaction Conference (pp. 420–423). ACM. Orzechowski, J. (2010). Working Memory Capacity and Individual Differences in Higher- Level Cognition. In A. Gruszka, G. Matthews, & B. Szymura (Eds.), Handbook of Individual Differences in Cognition SE - 21 (pp. 353–368). Springer New York. https://doi.org/10.1007/978-1-4419-1210-7_21 Oviatt, S. (2006). Human-centered design meets cognitive load theory: designing interfaces that help people think. In Proceedings of the 14th annual ACM international conference on Multimedia (pp. 871–880). ACM. Paas, F., Camp, G., & Rikers, R. (2001). Instructional compensation for age-related cognitive declines: Effects of goal specificity in maze learning. Journal of Educational Psychology, 93(1), 181. Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429. Paas, F. G. W. C., van Merriënboer, J. J. G., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and Motor Skills, 79(1), 419– 430. Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24(1), 27–45. Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71. Paas, F., Tuovinen, J., van Merriënboer, J. G., & Aubteen Darabi, A. (2005). A motivational perspective on the rel