Human Machine Learning Symbiosis.

Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner’s learning state both in terms of knowledge and motivation. This paper describes the benefits of such a system and a proposed design that integrates human learning in both the cognitive and affective domains with machine learning which adapts to both.

[1]  Ryan Shaun Joazeiro de Baker,et al.  Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game , 2015, Comput. Educ..

[2]  R. Bjork,et al.  Learning Concepts and Categories , 2008, Psychological science.

[3]  Sumaiya Iqbal,et al.  Genetic algorithm-based improved sampling for protein structure prediction , 2017, Int. J. Bio Inspired Comput..

[4]  Arthur C. Graesser,et al.  The Relationship between Affective States and Dialog Patterns during Interactions with AutoTutor , 2005 .

[5]  Robert E. Schapire,et al.  A Game-Theoretic Approach to Apprenticeship Learning , 2007, NIPS.

[6]  Ann Majchrzak,et al.  Enabling Customer-Centricity Using Wikis and the Wiki Way , 2006, J. Manag. Inf. Syst..

[7]  Sumaiya Iqbal,et al.  DisPredict: A Predictor of Disordered Protein Using Optimized RBF Kernel , 2015, PloS one.

[8]  Sidney K D'Mello,et al.  The Academic Diligence Task (ADT): Assessing Individual Differences in Effort on Tedious but Important Schoolwork. , 2014, Contemporary educational psychology.

[9]  E. Skinner,et al.  Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. , 1993 .

[10]  Doug Rohrer,et al.  Interleaving Helps Students Distinguish among Similar Concepts , 2012 .

[11]  B. Skinner,et al.  Technology of Teaching , 1967, Nature.

[12]  R. Morgan,et al.  Customer Engagement: Exploring Customer Relationships Beyond Purchase , 2012 .

[13]  Csaba Szepesvári,et al.  Algorithms for Reinforcement Learning , 2010, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[14]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[15]  Katherine N. Lemon,et al.  Customer Engagement Behavior: Theoretical Foundations and Research Directions , 2010 .

[16]  Abdul Sattar,et al.  An Enhanced Genetic Algorithm for Ab Initio Protein Structure Prediction , 2016, IEEE Transactions on Evolutionary Computation.

[17]  Sidney K. D'Mello,et al.  The Affective Experience of Novice Computer Programmers , 2015, International Journal of Artificial Intelligence in Education.

[18]  Matthew M. Chingos,et al.  Interactive Learning Online at Public Universities: Evidence from a Six-Campus Randomized Trial. , 2014 .

[19]  J. Lygeros,et al.  A game theoretic approach to controller design for hybrid systems , 2000, Proceedings of the IEEE.

[20]  Madhu Chetty,et al.  Generalized Schemata Theorem Incorporating Twin Removal for Protein Structure Prediction , 2007, PRIB.

[21]  Gary Natriello,et al.  Problems in the Evaluation of Students and Student Disengagement from Secondary Schools. , 1984 .

[22]  R. Brodie,et al.  Customer Engagement , 2011 .

[23]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[24]  Elaine. Chapman.  Alternative Approaches to Assessing Student Engagement Rates - Practical Assessment, Research & Evaluation , 2016 .

[25]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[26]  Shana K. Carpenter,et al.  Using Spacing to Enhance Diverse Forms of Learning: Review of Recent Research and Implications for Instruction , 2012 .

[27]  Arthur C. Graesser,et al.  Theoretical Perspectives on Affect and Deep Learning , 2011 .

[28]  Sumaiya Iqbal,et al.  hGRGA: A scalable genetic algorithm using homologous gene schema replacement , 2017, Swarm Evol. Comput..

[29]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[30]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..