13 Smart Learning Using Big and Small Data for Mobile and IOT e-Health
635
70. Y. Cheung, P.-Y. Hsueh, I. Ensari, J. Willey, K. Diaz, Quantile coarsening analysis of highvolume wearable activity data in a longitudinal observational study. Sensors 18(9), 3056
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71. M. Behrends, T. Kupka, R. Schmeer, I. Meyenburg-Altwarg, M. Marschollek, Knowledge
transfer in health care through digitally collecting learning experiences – results of Witra
care. Stud. Health Technol. Inform. 225, 287–291 (2016)
72. D. Schaeffer, D. Vogt, E-M. Berens, K. Hurrelmann. Gesundheitskompetenz der Bevölkerung
in Deutschland: Ergebnisbericht, Universität Bielefeld, Fakultät für Gesundheitswissenschaften (2016)
73. I. Kickbusch, Health Literacy. The Solid Facts (World Health Organization, Geneva, 2013)
74. G. Irving, A.L. Neves, H. Dambha-Miller, A. Oishi, H. Tagashira, A. Verho, J. Holden,
International variations in primary care physician consultation time: A systematic review of
67 countries. BMJ Open 7, e017902 (2017)
75. D.H. Schunk, Learning Theories: An Educational Perspective (Macmillan, New York, 1991)
76. T.M. Duffy, D. Jonassen, Constructivism: New implications for instructional technology?
Educ. Technol. 31(5), 3–12 (1991)
77. D.J. Cunningham, Assessing constructions and constructing assessments: A dialogue. Educ.
Technol. 31(5), 13–17 (1991)
78. P.A. Ertmer, T.J. Newby, Behaviorism, cognitivism, constructivism: Comparing critical
features from an instructional design perspective. Perform. Improv. Q 26, 43–71 (2013)
79. A.T. Corbett, J.R. Anderson, LISP intelligent tutoring system research in skill acquisition,
in Computer Assisted Instruction and Intelligent Tutoring Systems: Shared Goals and
Complementary Approaches, ed. by J. Larkin, R. Chabay, (Prentice-Hall Inc., Englewood
Cliffs, New Jersey, 1992), pp. 73–110
80. J. Anderson, Skill acquisition and the LISP tutor. Cogn. Sci. 13, 467–505 (1989)
81. J. Anderson, A.T. Corbett, K.R. Koedinger, R. Pelletier, Cognitive tutors: Lessons learned. J.
Learn. Sci. 4, 167–207 (1995)
82. R. Nkambou, J. Bourdeau, R. Mizoguchi (eds.), Advances in Intelligent Tutoring Systems
(Springer, Berlin, 2010), p. 4
83. I. Goldstein, S. Papert, Artificial intelligence, language, and the study of knowledge. Cogn.
Sci 1, 84–123 (1977)
84. M. Minsky, Logical versus analogical or symbolic versus connectionist or neat versus scruffy.
AI Mag. 12, 34–51 (1991)
85. L. Faria, A. Silva, Z. Vale, A. Marques, Training control centers’ operators in incident
diagnosis and power restoration using intelligent tutoring systems. IEEE Trans. Learn.
Technol 2, 135–147 (2009)
86. Z.A. Vale, A. Machado, M. Fernanda Fernandes, C. Ramos, Sparse: An intelligent alarm
processor and operator assistant. IEEE Expert 12, 86–93 (1997)
87. G. Acampora, J.M. Cadenas, V. Loia, E.M. Ballester, A multi-agent memetic system for
human-based knowledge selection. IEEE Trans. Syst. Man Cybern. A 41, 946–960 (2011)
88. Z. Yu, Y. Nakamura, D. Zhang, S. Kajita, K. Mase, Content provisioning for ubiquitous
learning. IEEE Pervasive Comput. 7, 62–70 (2008)
89. F. Colace, M. de Santo, Ontology for E-learning: A Bayesian approach. IEEE Trans. Educ.
53, 223–233 (2010)
90. P. Verma, S.K. Sood, S. Kalra, Student career path recommendation in engineering stream
based on three-dimensional model. Comput. Appl. Eng. Educ. 25, 578–593 (2017)
91. G. Tsaganou, M. Grigoriadou, T. Cavoura, D. Koutra, Evaluating an intelligent diagnosis
system of historical text comprehension. Expert Syst. Appl. 25, 493–502 (2003)
92. O. Taylan, B. Karagözo˘ glu, An adaptive neuro-fuzzy model for prediction of student’s
academic performance. Comput. Ind. Eng. 57, 732–741 (2009)
93. K. Chrysafiadi, M. Virvou, Fuzzy logic for adaptive instruction in an E-learning environment
for computer programming. IEEE Trans. Fuzzy Syst. 23, 164–177 (2015)
635
70. Y. Cheung, P.-Y. Hsueh, I. Ensari, J. Willey, K. Diaz, Quantile coarsening analysis of highvolume wearable activity data in a longitudinal observational study. Sensors 18(9), 3056
(2018)
71. M. Behrends, T. Kupka, R. Schmeer, I. Meyenburg-Altwarg, M. Marschollek, Knowledge
transfer in health care through digitally collecting learning experiences – results of Witra
care. Stud. Health Technol. Inform. 225, 287–291 (2016)
72. D. Schaeffer, D. Vogt, E-M. Berens, K. Hurrelmann. Gesundheitskompetenz der Bevölkerung
in Deutschland: Ergebnisbericht, Universität Bielefeld, Fakultät für Gesundheitswissenschaften (2016)
73. I. Kickbusch, Health Literacy. The Solid Facts (World Health Organization, Geneva, 2013)
74. G. Irving, A.L. Neves, H. Dambha-Miller, A. Oishi, H. Tagashira, A. Verho, J. Holden,
International variations in primary care physician consultation time: A systematic review of
67 countries. BMJ Open 7, e017902 (2017)
75. D.H. Schunk, Learning Theories: An Educational Perspective (Macmillan, New York, 1991)
76. T.M. Duffy, D. Jonassen, Constructivism: New implications for instructional technology?
Educ. Technol. 31(5), 3–12 (1991)
77. D.J. Cunningham, Assessing constructions and constructing assessments: A dialogue. Educ.
Technol. 31(5), 13–17 (1991)
78. P.A. Ertmer, T.J. Newby, Behaviorism, cognitivism, constructivism: Comparing critical
features from an instructional design perspective. Perform. Improv. Q 26, 43–71 (2013)
79. A.T. Corbett, J.R. Anderson, LISP intelligent tutoring system research in skill acquisition,
in Computer Assisted Instruction and Intelligent Tutoring Systems: Shared Goals and
Complementary Approaches, ed. by J. Larkin, R. Chabay, (Prentice-Hall Inc., Englewood
Cliffs, New Jersey, 1992), pp. 73–110
80. J. Anderson, Skill acquisition and the LISP tutor. Cogn. Sci. 13, 467–505 (1989)
81. J. Anderson, A.T. Corbett, K.R. Koedinger, R. Pelletier, Cognitive tutors: Lessons learned. J.
Learn. Sci. 4, 167–207 (1995)
82. R. Nkambou, J. Bourdeau, R. Mizoguchi (eds.), Advances in Intelligent Tutoring Systems
(Springer, Berlin, 2010), p. 4
83. I. Goldstein, S. Papert, Artificial intelligence, language, and the study of knowledge. Cogn.
Sci 1, 84–123 (1977)
84. M. Minsky, Logical versus analogical or symbolic versus connectionist or neat versus scruffy.
AI Mag. 12, 34–51 (1991)
85. L. Faria, A. Silva, Z. Vale, A. Marques, Training control centers’ operators in incident
diagnosis and power restoration using intelligent tutoring systems. IEEE Trans. Learn.
Technol 2, 135–147 (2009)
86. Z.A. Vale, A. Machado, M. Fernanda Fernandes, C. Ramos, Sparse: An intelligent alarm
processor and operator assistant. IEEE Expert 12, 86–93 (1997)
87. G. Acampora, J.M. Cadenas, V. Loia, E.M. Ballester, A multi-agent memetic system for
human-based knowledge selection. IEEE Trans. Syst. Man Cybern. A 41, 946–960 (2011)
88. Z. Yu, Y. Nakamura, D. Zhang, S. Kajita, K. Mase, Content provisioning for ubiquitous
learning. IEEE Pervasive Comput. 7, 62–70 (2008)
89. F. Colace, M. de Santo, Ontology for E-learning: A Bayesian approach. IEEE Trans. Educ.
53, 223–233 (2010)
90. P. Verma, S.K. Sood, S. Kalra, Student career path recommendation in engineering stream
based on three-dimensional model. Comput. Appl. Eng. Educ. 25, 578–593 (2017)
91. G. Tsaganou, M. Grigoriadou, T. Cavoura, D. Koutra, Evaluating an intelligent diagnosis
system of historical text comprehension. Expert Syst. Appl. 25, 493–502 (2003)
92. O. Taylan, B. Karagözo˘ glu, An adaptive neuro-fuzzy model for prediction of student’s
academic performance. Comput. Ind. Eng. 57, 732–741 (2009)
93. K. Chrysafiadi, M. Virvou, Fuzzy logic for adaptive instruction in an E-learning environment
for computer programming. IEEE Trans. Fuzzy Syst. 23, 164–177 (2015)