State of the Art Literature Study

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The tutoring system


  • These two articles propose methods to find similar questions among a database of questions. The similarity of questions in this article is based on how useful the question to one answer could be to answer a different given question. The methods used here might be useful when we will try to define the distance between questions. [1] [2]
  • There are a lot of approaches to construct a intelligent tutoring system. This paper shows a few key elements to take into account when constructing such a system. The focus is put on making trade-offs, for example productivity vs learnability. [3]
  • On how we can add probabilities to someones response to an answer representing their knowledge. This can be useful when training and testing our intelligent quiz, as we can use this to simulate the responses of a child. For example, a child who thinks they know the answer might give the wrong answer because they are too quick, but in fact they do know the answer. Or a child who does not know the answer might take a correct guess. [4]
  • This chapter continues on the previous. It describes how to model a learning path of a human being. This can also be useful when simulating the responses of the children, as children in real life progress while answering questions. [5]
  • This paper shows that by using a different take on machine learning, it is possible to train children as we are training computers. This is called machine teaching. The goal is to find the optimal training set, the smallest training set, to achieve the desired effect, which is a group of children whom all know the course material given by the teacher.[6]
  • Combining Artificial intelligence with tutoring has already been used and researched for several years and is called an intelligent tutoring system. Therefore designing such a program requires you to keep in mind all of the things that have already been done, which can be found in the article. For instance the article gives 8 designing principles such as: provide immediate feedback on errors, and it shows a possible architecture. [7]
  • "Based on the gathered information the next scene presented to the player should be determined in a way such that the learner is neither unchallenged nor overwhelmed by the complexity of the contained tasks. It should rather be ensured that the learner’s knowledge is steadily increased up to full knowledge mastery." [8] This line perfectly captures what we want to achieve. This paper is about how we can make systems adaptive in such a way that they can adapt to the needs and level of its user.
  • 80Days is a game that is comparable to what we want to achieve. However this is a full on computer game while we want an interactive home system. The adaptation principle is relatable which is why it could be nice to base our system on this game. [9]
  • In the ELEKTRA game they use unobtrusive knowledge assessment to see the user's level of knowledge. [10] They do this by observing the user's behaviour in the game. If we can do something similar but in real life, as a smart home will be able to notice the residents behaviour, we can use this technology to assess the childs level and what he needs from the educational system such that he will get personalized assignments.

User interface

  • A learning system with a specific interface has been devised based on the learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. This system notices preferences like global vs sequential or visual vs auditory. [11]
  • Shows that nowadays the educational aspects in interactive systems is often too separate ("chocolate-covered broccoli") and extrinsic. To create learning systems that work it is important to make them intrinsically motivating. You have to keep in mind that people have to actually use the system, so make it in such a way that they want to. Remember, the primary users will be children. [12]
  • A paper that "focuses on feedback of interface agents on student perception. (...) This study focuses on three types of feedback from interface agents to clarify student perception of single feedback and multiple feedback types." [13] The results of this study should be taken into account when modeling the way in which our system interacts with users, in order to make it's feedback as effective as possible.
  • Next, a paper that analyses "the role of digital games in early childhood, especially from the perspectives of learning, literacy and play. (...) The analysis of young children's learning while they are engaged in digital games in informal contexts furthers our understanding of the potential of game-based learning in formal early childhood education settings." [14] The results presented in this chapter can be used in our project to properly design the way in which infants interact with the system, what digital games the system should present, and how.
  • A book that could possibly be helpful. It proposes design principles of educational virtual worlds of preschool children. [15] Especially the principles described in chapter 4 could be applied to the designing of our smart home system.
  • An interesting paper described a study which "investigated teachers’ perceptions of barriers to using - integrating computers in early childhood settings." Unlike other papers, the viewpoint of this article shifts the view from the developer of the system or the child user to the teachers involved in the process. [16] The findings in this study can be used to descibe the effects on teachers when applying a smart home system to enhance child learning.
  • A paper released in 2010 presents a "device to systematically assess children's orienting behavior in social situations." An set of relevant tests was carried out with "12-36 months old typically developing infants [which] prove usability of the proposed technology." [17] This study can be used to aid the social learning aspect of childhood in the proposed smart home system.
  • This article focusses on whether intelligent tutoring systems are better for learning than conventional classroom teaching. [18] This way we can determine an approach for our intelligent quiz, and maybe decide it would be better to make this a classroom program instead of a personal one.
  • Paper about what is needed for a proper functioning intelligent tutoring system. [19] They are trying to create a personal system in order to continue learning throughout your lifetime, but we can use the requirements that are described.

Smart home

  • Current smart homes have already integrated a lot of features, for instance sensors in the floor to accurate locate persons or cameras with image analysis. However these systems are far from perfect. They have to improve their computing infrastructure more. [20]
  • It is possible for a smart home to detect speech and react upon it in crowdy and noisy environments [21]
  • Smart homes can use their social features to interact with persons. [22]
  • Paper about using smart homes to learn about human behaviour. [23] This could be used for multiple things in our project like the knowledge assessment mentioned above, or checking whether or not the user (child) has some free time that he can use interacting with the system and thus learning. There is a need for pattern recognition in this behaviour.
  • We propose some papers related to the way our smart home system should be designed and implemented. First, a paper that: "proposes a model to improve Activity Recognition in smart homes. The proposed technique is based on defining a profile ofr each activity from training datasets. The profile will be used to induce extra features and will help in distinguishing residents' activities." [24] This technique can be used in our project to develop the way our smart home recognizes activities of the users of the system.
  • Related to this, a paper that proposes a way to implement an "Adaptable Infant Monitoring System (...) with minimal cost, knowledge and time to set up". [25] This technique can be used in the implementation of our smart home, in order to monitor infants using the system, and so prevent accidents, and keep track of the location of the infant. This could (possibly) be combined with the proposed technique above, to also monitor whát the infant is doing.
  • Now, we propose a paper related to the way the educational aspect of our smart home is designed and implemented. First, a paper that does not seem completely relevant to the project at first. [26] However, it discusses "the application of gamification within the environment of education, and more specifically with an emphasis on assessment." This paper "opens the way for the implementation of gamification as a transformative online assessment tool ...", which can be used in our project to determine the way in which our smart home educates and assesses children in a playful manner.
  • We reference a paper that conducted a study amongst students in a "Smart Home Lab". It describes their findings and argues that, in order to successfully use a Smart Home as a Pedagogical Tool, one must shift from virtual reality simulations to the physical environment, which they call "real virtuality". [27] These results can be used to guide the development of a Smart Home as a learning (i.e. pedagogical) tool. Note that, even though the study is focussed on individuals that are outside of our research scope in terms of age, some results might still apply.
  • A paper that "describes an exploratory investigation into the potential effects of a robot exhibiting an adaptive behaviour in reaction to a child’s interaction". [28] The conclusions in this article can be useful in describing the effects of using a Smart Home system for learning purposes.
  • The following paper describes the trend of using robotics technology to support childhood education. It goes into the ethics involved, as well as social acceptance for such technologies. [29] This chapter from the book "Cybernics" can be used to assess the social acceptance and ethics evolved in creating and deploying the proposed smart home system.


  1. A Topic Clustering Approach to Finding Similar Questions from Large Question and Answer Archives, from
  2. Deepa Paranjpe, Clustering Semantically Similar and Related Questions, from
  3. Tom Murray, Authoring Intelligent Tutoring Systems: An analysis of the state of the art, International Journal of Artificial Intelligence in Education, 1999, pp.98-129, from
  4. Knowledge Spaces, Chapter 7 (p. 142 - 173), from
  5. Knowledge Spaces, Chapter 8 (p. 175 - 205), from
  6. Xiaojin Zhu, Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education, Department of Computer Sciences, University of Wisconsin–Madison, from
  7. Albert T. Corbett, Kenneth R. Koedinger and John R. Anderson, Chapter 37 - Intelligent Tutoring Systems, In Handbook of Human-Computer Interaction (Second Edition), edited by Marting G. Helander, Thomas K. Landauer and Prasad V. Prabhu, North-Holland, Amsterdam, 1997, Pages 849-874, ISBN 9780444818621,
  8. Göbel, S., Wendel, V., Ritter, C., and Steinmetz, R., Personalized, Adaptive Digital Educational Games using Narrative Game-based Learning Objects, from
  9. Kickmeier-Rust, M.D., Göbel, S., and Albert, D., 80Days: Melding Adaptive Educational Technology and Adaptive and Interactive Storytelling in Digital Educational Games, from
  10. Kickmeier-Rust, M. D., Hockemeyer, C., Albert, D., & Augustin, T., Micro adaptive, non-invassive assessment in educational games, from
  11. Hyun Jin ChaYong Se KimSeon Hee ParkTae Bok YoonYoung Mo JungJee-Hyong Lee, Learning Styles Diagnosis based on User Interface Behaviors for the Customization of Learning Interfaces in an Intelligent Tutoring System, Creative Design & Intelligent Tutoring Systems Research Center12345, School of Information & Communication Engineering, Sungkyunkwan University, Suwon, Korea, pp 513-524, 2006, from
  12. Habgood, M.P.J. and Ainsworth, S.E., Motivating children to learn effectively: exploring the value of intrinsic integration in educational games, from
  13. Zhi-Hong Chen, Chih-Yueh Chou , Shu-Fen Tseng, Ying-Chu Su, Feedback of Interface Agents on Student Perception: Level, Dialogue, and Emotion, from
  14. Marja Kankaanranta, merja Koivula, Marja-Leena Laakso, Marleena Mustola, Ditital Games in Early Childhood: Broadening Definitions of Learning, Literacy, and Play, from
  15. Jana Pejoska, Design principles of educational virtual worlds for preschool children, from
  16. Kleopatra Nikolopoulou & Vasilis Gialamas, Barriers to the integration of computers in early childhood settings: Teachers’ perceptions, from
  17. Giuseppina Schiavone & Domenico Formica & Fabrizio Taffoni & Domenico Campolo & Eugenio Guglielmelli & Flavio Keller, Multimodal Ecological Technology: From Child’s Social Behavior Assessment to Child-Robot Interaction Improvement, from
  18. Boulay, B. du, Can We Learn from ITSs?, from,%205%20conf.,%20ITS%202000(LNCS1839,%20Springer,%202000)(ISBN%203540676554)(695s)_CsLn_.pdf#page=27
  19. Sharples, M., The design of personal mobile technologies for lifelong learning, from
  20. Marie Chan, Daniel Estève, Christophe Escriba, Eric Campo, A review of smart homes—Present state and future challenges, Volume 91, Issue 1, Pages 55-81, ISSN 0169-2607, 2008, from
  21. Michel Vacher, Anthony Fleury, François Portet, Jean-François Serignat, Norbert Noury, Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living, Domenico Campolo, New Developments in Biomedical Engineering, In-Tech, pp. 645 – 673, 2010, from
  22. Marie Chan, Eric Campo, Daniel Estève, Jean-Yves Fourniols, Smart homes — Current features and future perspectives, Maturitas, Volume 64, Issue 2, Pages 90-97, ISSN 0378-5122, 2009,
  23. Brdiczka, O., Langet, M., Miasonnasse, J., and Crowley, J.L., Detecting Human Behavior Models From Multimodal Observation in a Smart Home, from
  24. Majdi Rawashdeh, Mohammed GH. Al Zamil, Samer Samarah, M. Shamim Hossain, Ghulam Muhammad, Micro adaptive, non-invassive assessment in educational games, from
  25. Hong Zhou, Brad Goold, A Domestic Adaptable Infant Monitoring System Using Wireless Sensor Networks
  26. E. Oliver, Gamification as transformative assessment in higher education, from
  27. Antonio Sanchez and Lisa Burnell, A Smart Home Lab as a Pedagogical Tool, from
  28. Tamie Salter, Francois Michaud, and Dominic Letourneau, An Exploratory Investigation into the Effects of Adaptation in Child-Robot Interaction, from
  29. Fumihide Tanaka, Robotics for Supporting Childhood Education, from