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Beyond Cognitive and Affective Issues: Designing Smart Learning Environments for Psychomotor Personalized Learning

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Learning, Design, and Technology

Abstract

Although learning can involve cognitive, affective, and psychomotor aspects, the latter (which refers to motor skills learning) has been hardly considered when providing personalized support within smart learning environments, despite there are many activities that require learning specific motor skills, such as learning to operate (surgery), to speak with sign language, to play a musical instrument, to practice a sport technique, etc. Emerging technologies from the recently coined term of “Internet of Me,” such as wearable devices from the life-logging movement, can enrich the type of data gathered while learning by considering features related to body movements. In turn, the new big data paradigm facilitates a more efficient computing of performance indicators from the combination of the heterogeneous sources of data collected from the wearable devices. If learning environments want to be smart, they need to deploy the appropriate technological infrastructure not only to collect and process information regarding psychomotor learning but also to provide the corresponding personalized support. In this chapter, the tangibREC framework is proposed, which defines the new concept of tangible recommendations aimed to provide physical scaffolding to the recommendation process. These recommendations (identified with TORMES methodology) can be modeled and printed in 3D to physically guide learners on how to perform accurate movements in terms of the learners’ individual physical features and progress in the motor skills acquisition.

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References

  • Abrahamson, D., & Trninic, D. (2015). Bringing forth mathematical concepts: Signifying sensorimotor enactment in fields of promoted action. In D. Reid, L. Brown, A. Coles, & M.-D. Lozano (Eds.), Enactivist methodology in mathematics education research [Special issue]. ZDM–The International Journal on Mathematics Education, 47(2), 295–306.

    Google Scholar 

  • Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

    Article  Google Scholar 

  • Al-Busaidi, A. M. (2012). Development of an educational environment for online control of a biped robot using MATLAB and Arduino. In IEEE Mecatronics-REM 2012, pp. 337–344. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6451030&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F6423127%2F6450975%2F06451030.pdf%3Farnumber%3D6451030

  • Algar, D., & Guldberg, A. (2013). Insole modeling using Kinect 3D sensors (Master thesis in Biomedical Engineering). Chalmers University of Technology.

    Google Scholar 

  • Allotta, B., Conti, R., Governi, L., Meli, E., Ridolfi, A., & Volpe, Y. (2015). Development and experimental testing of a portable hand exoskeleton. In Intelligent robots and systems (IROS), 2015 IEEE/RSJ international conference on, Hamburg, pp. 5339–5344. http://ieeexplore.ieee. org/xpl/login.jsp?tp=&arnumber=7354131&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7354131.

  • Arsenault, D. (2014). A quaternion-based motion tracking and gesture recognition system using wireless inertial sensors (Master of Applied Science in Human-Computer Interaction). Carleton University.

    Google Scholar 

  • Bae, J., Haninger, K., Wai, D., Garcia, X., & Tomizuka, M. (2012). A network-based monitoring system for rehabilitation. In The 2012 IEEE/ASME international conference on advanced intelligent mechatronics, Kachsiung, pp. 232–237. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6265884.

  • Banaee, H., Ahmed, M. U., & Loutfi, A. (2013). Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges. Sensors, 13, 17472–17500.

    Article  Google Scholar 

  • Banzi, M. (2009). Getting started with Arduino. O’Reilly Media, Sebastopol, CA. http://cdn.oreilly.com/oreilly/booksamplers/9780596155513-sampler.pdf.

  • Bartneck, C., Soucy, M., Fleuret, K., & Sandoval, E. B. (2015). The robot engine – Making the unity 3D game engine work for HRI. In Robot and human interactive communication (RO-MAN), 2015 24th IEEE international symposium on, Kobe, pp. 431–443. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7333561&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F7321996%2F7333553%2F07333561.pdf%3Farnumber%3D7333561.

  • Bloom, B. S. (1956). Taxonomy of educational objectives. In Handbook 1: Cognitive domain. New York, NY: David McKay.

    Google Scholar 

  • Bogue, R. (2013). 3D printing: The dawn of a new era in manufacturing? Assembly Automation, 33(4), 307–311.

    Article  Google Scholar 

  • Cincotti, C. C., O’Donnell, S., Zapata, G. E., Rabolli, C. M., & BuSha, B. F. (2015). Strength amplifying hand exoskeleton. In Biomedical engineering conference (NEBEC), 2015 41st annual Northeast, Troy, NY, pp. 1–2. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7117082&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7117082.

  • Cui, L., Phan, A., & Allison, G. (2015). Design and fabrication of a three dimensional printable non-assembly articulated hand exoskeleton for rehabilitation. In Engineering in Medicine and Biology Society (EMBC), 2015 37th annual international conference of the IEEE, Milano, pp. 4627–4630. http://embc.embs.org/2015/.

  • Czech, A. (2015). 3D printed exoskeleton hand. Available from Cults https://cults3d.com/en/gadget/3d-printed-exoskeleton-hand

  • Datta, S. (2014). Forced fingers. Available from Github https://github.com/dattasaurabh82/Forced-Fingers

  • Dave, R. (1970). Psychomotor levels. In R. J. Armstrong (Ed.), Developing and writing behavioral objectives. Tucson, AZ: Educational Innovators Press.

    Google Scholar 

  • DellaGrotte, J., Ridi, R., Landi, M., & Stephens, J. (2008). Postural improvement using core integration to lengthen myofascia. Journal of Bodywork and Movement Therapies, 12, 231–245.

    Article  Google Scholar 

  • Di Rienzo, M., Rizzo, F., Meriggi, P., Castiglioni, P., Mazzoleni, P., Parati, G., … Ferratini, M. (2009). MagIC system. IEEE Engineering in Medicine and Biology Magazine, 28, 35–40.

    Google Scholar 

  • Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of recommender systems to support learning. In Recommender Systems Handbook (2nd ed.). Springer, pp 421–451. http://link.springer.com/chapter/10.1007%2F978-1-4899-7637-6_12 & http://link.springer.com/content/pdf/bfm%3A978-1-4899-7637-6%2F1.pdf

  • Engelmann, F. (2011). FabScan – Affordable 3D laser scanning of physical objects (Bachelor’s thesis at the Media Computing Group) RWTH Aachen University.

    Google Scholar 

  • Fan, W., & Bifet, A. (2013, April). Mining big data: Current status, and forecast to the future. SIGKDD Explorations Newsletter, 14(2), 1–5.

    Google Scholar 

  • Ferris, T. L. J., & Aziz, S. M. (2005). A psychomotor skills extension to Bloom’s taxonomy of education objectives for engineering education. In Conference iCEER (Exploring Innovation in Education and Research). Available at http://slo.sbcc.edu/wp-content/uploads/bloom-psychomotor.pdf

  • Fleury, A., Sugar, M., & Chau, T. (2015). E-textiles in clinical rehabilitation: A scoping review. Electronics, 4, 173–203.

    Article  Google Scholar 

  • Fong, D. T.-P., & Chan, Y.-Y. (2010). The use of wearable inertial motion sensors in human lower limb biomechanics studies: A systematic review. Sensors, 10, 11556–11565.

    Article  Google Scholar 

  • Gagné, R. M., & Briggs, L. J. (1979). Principles of instructional design (2nd ed.). New York, NY: Holt, Rinehart, and Winston.

    Google Scholar 

  • Gioberto, G., & Dunne, L. E. (2013). Overlock-stitched stretch sensors: Characterization and effect of fabric property. Journal of Textiles and Apparel Technology and Management, 8, 1–14.

    Google Scholar 

  • Giorgino, T., Tormene, P., Lorussi, F., de Rossi, D., & Quaglini, S. (2009). Sensor evaluation for wearable strain gauges in neurological rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17, 409–415.

    Article  Google Scholar 

  • Harms, H., Amft, O., & Troester, G. (2010). Estimating posture-recognition performance in sensing garments using geometric wrinkle modeling. IEEE Transactions on Information Technology in Biomedicine, 14, 1436–1445.

    Article  Google Scholar 

  • Harrow, A. J. (1972). A taxonomy of the psychomotor domain. New York, NY: David McKay.

    Google Scholar 

  • Huluta, E., da Silva, R. F., & de Oliveira, T. E. A. (2014). Neural network-based hand posture control of a humanoid robot hand. In IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA), Ottawa, ON, pp. 124–128 http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6841450&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6829977%2F6841424%2F06841450.pdf%3Farnumber%3D6841450.

  • Kagawa, T., Motomura, M., & Nishino, H. (2014). A 3D editing method with hand gesture using sound information. In Eighth international conference on complex, intelligent and software intensive systems, Birmingham pp. 637–642. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6915588&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6915588.

  • Kearney, P. (1994). Affective learning scale. In: R. B. Rubin, P. Palmgreen & H. E. Sypher (Eds.), Communication research measures: A sourcebook (pp. 81–85 and pp. 238–241). New York: The Guilford Press.

    Google Scholar 

  • Kifayat, K., Fergus, P., Cooper, S., & Merabti, M. (2010). Body area networks for movement analysis in physiotherapy treatments. In IEEE 24th international conference on advanced information networking and applications workshops, Perth, WA, pp. 866–872. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5480793&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5480793.

  • Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4-5), 441–504.

    Article  Google Scholar 

  • Kulik, J. A., & Fletcher, J. D. (2015). Effectiveness of intelligent tutoring systems. A meta-analytic review. Review of Educational Research. doi: 10.3102/0034654315581420. http://rer.sagepub.com/content/early/2015/04/17/0034654315581420.abstract.

    Google Scholar 

  • Lane, C. & Santos, O. C. (2016). Embodied learning and artificial intelligence: Expanding the bandwidth of learning technologies. Section: Ideas worth sharing: Smarter digital tools for students and educators. Program independent through leadership, Pearson.com. Available from: https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/ideas-worth-sharing_embodied-learning-and-artificial-intelligence.pdf.

  • Lee, J., & Bae, J. (2015). Design of a hand exoskeleton for biomechanical analysis of the stroke hand. In Rehabilitation robotics (ICORR), 2015 I.E. international conference on, Singapore, pp. 484–489. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7281246.

  • Lee, B. W., Lee, C., Kim, J., & Lee, M. (2011). Optimum conductive fabric sensor sites for evaluating the status of knee joint movements using bio-impedance. Biomedical Engineering Online, 10, 48.

    Article  Google Scholar 

  • Li, L., Vouga, E., Gudym, A., Luo, L., Barron, J. T., & Gusev, G. (2013). 3D self-portraits. ACM Transactions Graphics, 32(6), Article 187.

    Google Scholar 

  • Lieberman, J., & Breazeal, C. (2007). TIKL: Development of a wearable vibrotactile feedback suit for improved human motor learning. IEEE Transactions on Robotics, 23(5), 919–926.

    Article  Google Scholar 

  • Lindgren, R. (2015). Getting into the cue embracing technology-facilitated body movements as a starting point for learning. Learning technologies and the boduy: Integration and implementation. New York, NY: Routledge.

    Google Scholar 

  • Lorussi, F., Galatolo, S., & Bartalesi, R. (2013). Modeling and characterization of extensible wearable textile-based electrogoniometers. IEEE Sensors Journal, 13, 217–228.

    Article  Google Scholar 

  • Mager, R. (1997). Preparing instructional objectives: A critical tool in the development of effective instruction (3rd ed.). Atlanta, GA: Center for Effective Performance.

    Google Scholar 

  • Mannini, A., & Sabatini, A. M. (2010). Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 10, 1154–1175.

    Article  Google Scholar 

  • Marcel, S. (2002). Gestures for multi-modal interfaces: a review (IDIAP research report 02-34).

    Google Scholar 

  • Marchal-Crespo, L., Raai, M., Rauter, G., Wolf, P., & Riener, R. (2013). The effect of haptic guidance and visual feedback on learning a complex tennis task. Experimental Brain Research, 213 (2): 277–291 http://link.springer.com/article/10.1007%2Fs00221-013-3690-2.

  • Nithya, R., Divya Bharathi, S., & Poongavanam, P. (2015). Design of orthotic assistive exoskeleton for human hand. In IEEE International Conference on Engineering and Technology (ICETECH), 2015 I.E. international conference on, Coimbatore, pp. 1–6. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7275044.

  • Noorhidawati, A., Ghalebandi, S. G., & Hajar, R. S. (2015). How do young children engage with mobile apps? Cognitive, psychomotor, and affective perspective. Computers & Education, 87, 385–395.

    Article  Google Scholar 

  • Palmas, G., Bachynskyi, M., Oulasvirta, A., Seidel, H.-P., & Weinkauf, T. (2014). MovExp: A versatile visualization tool for human-computer interaction studies with 3D performance and biomechanical data. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2359–2368.

    Article  Google Scholar 

  • Penney, S. (2011). Psychomotor domain. In B. Hoffman (Ed.), Encyclopedia of educational technology. San Diego, CA: SDSU Department of Educational Technology. Retrieved October 30, 2015, from http://eet.sdsu.edu/eetwiki/index.php/Psychomotor_domain

  • Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., … Strohecker, C. (2004). Affective learning – A manifesto. BT Technology Journal, 22(4), 253–269.

    Google Scholar 

  • Preece, S. J., Kenney, L. P., Major, M. J., Dias, T., Lay, E., & Fernandes, B. T. (2011). Automatic identification of gait events using an instrumented sock. Journal of NeuroEngineering and Rehabilitation, 8, 32.

    Article  Google Scholar 

  • Rapp, A., & Cena, F. (2014). Self-monitoring and technology: Challenges and open issues in personal informatics. Universal access in human-computer interaction. Design for all and accessibility practice. LNCS, 8516, 613–622.

    Google Scholar 

  • Rauter, G., Sigrist, R., Baur, K., Baumgartner, L., Riener, R., & Wolf, P. (2011). A virtual trainer concept for robot-assisted human motor learning in rowing. BIO web of conferences 1, article 00072.

    Google Scholar 

  • Rohwer, W. D. J., & Sloane, K. (1994). Psychological perspectives. In L. W. Anderson & L. A. Sosniak (Eds.), Bloom’s taxonomy a forty-year retrospective, vol. 93, part 2, Yearbook of the national society for the study of education (pp. 41–63). Chicago, IL: The University of Chicago Press.

    Google Scholar 

  • Romiszowski, A. (1999). The development of physical skills: Instruction in the psychomotor domain. In C. Reigeluth (Ed.), Instructional-design theories and models (Vol. II, pp. 457–479). New York, NY: Laurence Erlbaum Associates.

    Google Scholar 

  • Rovai, A. P., Wighting, M. J., Baker, J. D., & Grooms, L. D. (2009). Development of an instrument to measure perceived cognitive, affective, and psychomotor learning in traditional and virtual classroom higher education settings. The Internet and Higher Education, 12(1), 7–13.

    Article  Google Scholar 

  • Santos, O. C. (2015). Education still needs artificial intelligence to support personalized motor skill learning: Aikido as a case study. In: CEUR workshop proceedings, AIED 2015 workshops, Madrid, Vol. 1432, No. 4, pp. 72–81.

    Google Scholar 

  • Santos, O. C. (2016). Emotions and personality in adaptive e-learning systems: an affective computing perspective. In Tkalčič, DeCarolis, de Gemmis, Odić, & Košir (Eds.), Emotions and personality in personalized services. Springer, doi: 10.1007/978-3-319-31413-6_13 http://www.springer.com/in/book/9783319314112.

    Google Scholar 

  • Santos, O. C. (2016). Training the body: The potential of AIED to support personalized motor skills learning. Special Issue “The next 25 Years: How advanced, interactive educational technologies will change the world”. International Journal of Artificial Intelligence in Education June 2016, 26(2), 730–755 doi: 10.1007/s40593-016-0103-2 http://link.springer.com/article/10.1007%2Fs40593-016-0103-2.

  • Santos, O. C., & Boticario, J. G. (2011). Requirements for semantic educational recommender systems in formal e-learning scenarios. Algorithms, 4(2), 131–154.

    Article  Google Scholar 

  • Santos, O. C., & Boticario, J. G. (2015). Practical guidelines for designing and evaluating educationally oriented recommendations. Computer and Education, 81, 354–374.

    Google Scholar 

  • Santos, O. C., Saneiro, M., Boticario, J., & Rodriguez-Sanchez, C. (2016a). Toward interactive context-aware affective educational recommendations in computer assisted language learning. New Review of Hypermedia and Multimedia, 22(1–2), 27–57. http://www.tandfonline.com/toc/tham20/current.

  • Santos, O. C., Uria-Rivas, R., Rodriguez-Sanchez, M. C., & Boticario, J. G. (2016b). An open sensing and acting platform for context-aware affective support in ambient intelligent educational settings. IEEE Sensors Journal, 16(10), 3865–3874. http://ieeexplore.ieee.org/xpl/ogin.jsp?tp=&arnumber=7425146&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F7361%2F7450223%2F07425146.pdf%3Farnumber%3D7425146.

  • Schneider, J., Börner, D., van Rosmalen, P., & Specht, M. (2015). Augmenting the senses: A review on sensor-based learning support. Sensors, 15, 4097–4133.

    Article  Google Scholar 

  • Selinger, M., Sepulveda, A., & Buchan, J. (2013). Education and the internet of everything: How ubiquitous connectedness can help transform pedagogy. White Paper, Cisco, San Jose, Oct 2013. Available: http://www.cisco.com/web/strategy/docs/education/education_internet.pdf

  • Shu, L., Hua, T., Wang, Y., Li, Q., Feng, D. D., & Tao, X. (2010). In-shoe plantar pressure measurement and analysis system based on fabric pressure sensing array. IEEE Transactions on Information Technology in Biomedicine, 14, 767–775.

    Article  Google Scholar 

  • Simpson, E. J. (1972). The classification of educational objectives in the psychomotor domain. Washington, DC: Gryphon House.

    Google Scholar 

  • Soderstrom, N. C., & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176–199.

    Article  Google Scholar 

  • Spicer, A., & Cederström, C. (2015). You’ve heard of the internet of things, now behold the Internet of Me. The Conversation, Jan 2015. Available: http://theconversation.com/youve-heard-of-the-internet-of-things-now-behold-the-internet-of-me-36379

  • Starner, T. (2014). How wearables worked their way into the mainstream. Pervasive Computing, 13(4), 10–15.

    Article  Google Scholar 

  • Thomas, K. (2004). Learning taxonomies in the cognitive, affective, and psychomotor domain. Available online http://www.rockymountainalchemy.com/whitePapers/rma-wp-learning-taxonomies.pdf

  • Tormene, P., Bartolo, M., De Nunzio, A. M., Fecchio, F., Quaglini, S., Tassorelli, C., & Sandrini, G. (2012). Estimation of human trunk movements by wearable strain sensors and improvement of sensor’s placement on intelligent biomedical clothes. Biomedical Engineering Online, 11, 95.

    Article  Google Scholar 

  • Van der Linden, J., Johnson, R., Bird, J., Rogers, Y., & Schoonderwaldt, E. (2011). Buzzing to play: Lessons learned from an in the wild study of real-time vibrotactile feedback. In Proceedings of the conference on human factors in computing systems (CHI’11), Vancouver, 7–12 May 2011, pp. 533–543.

    Google Scholar 

  • Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.

    Article  Google Scholar 

  • Xu, X., Sheng, Q. Z., Zhang, L. J., Fan, Y., & Dustdar, S. (2015). From big data to big service. Computer, 48(7), 80–83.

    Article  Google Scholar 

  • Yamada, T., Hayamizu, Y., Yamamoto, Y., Yomogida, Y., Izadi-Najafabadi, A., & Hata, K. (2011). A stretchable carbon nanotube strain sensor for human-motion detection. Nature Nanotechnology, 6, 296–301.

    Article  Google Scholar 

  • Yang, C.-C., & Hsu, Y.-L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10, 7772–7788.

    Article  Google Scholar 

  • Zysset, C., Kinkeldei, T., Münzenrieder, N., Petti, L., Salvatore, G., & Tröster, G. (2013). Combining electronics on flexible plastic strips with textiles. Textile Research Journal, 83, 1130–1142.

    Google Scholar 

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  1. aDeNu Research Group. Artificial Intelligence Department. Computer Science School, UNED, Calle Juan Del Rosal, 16, 28040, Madrid, Spain

    Olga C. Santos

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  1. Olga C. Santos
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  1. College of Info, Ste G150, Univ of North Texas, Denton, Texas, USA

    Michael J Spector

  2. School of Education, Virginia Tech, Blacksburg, Virginia, USA

    Barbara B Lockee

  3. School of Education, Baker University, Overland Park, Kansas, USA

    Marcus D. Childress

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Santos, O.C. (2016). Beyond Cognitive and Affective Issues: Designing Smart Learning Environments for Psychomotor Personalized Learning. In: Spector, M., Lockee, B., Childress, M. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_8-1

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  • DOI: https://doi.org/10.1007/978-3-319-17727-4_8-1

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