PRE2017 3 Groep14: Difference between revisions

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= Learning with Smart Home for Kids =
='''Process'''=
'''S'''mart
[[Process Group 14]]
'''H'''ome
'''E'''ntertaining
'''E'''ducational
'''P'''rogram


= Subject =
='''Report'''=
Enhance knowledge levels of young children through an interactive smart home environment. Teachers and parents will be able to tell the system the desired final knowledge level and the system has to be able to interact with the children and assess his knowledge level to create fitting personal questions. In our project we will focus on the quiz and try to create this.
[[Report group 14]]


='''Peer review and Contributions'''=
==Peer review==
As all of us have contributed a lot to this project, there was good communication and we do not have any negative remarks about each other, we decided that each of us deserves the same result. This is why we decided on the following peer review results:
* Abby: 7,5
* Christine: 7,5
* Dennis: 7,5
* Ellen: 7,5
* Sophie: 7,5
Of course there is always room for improvement and our project could have been better, which is why we think 7,5 is a good representation of what we have done.


= Users =
==Contributions==
* Children from class 3 - 4 in the Netherlands. The system could be changed to fit other age groups that have other knowledge levels, but for this project we focus on this group.
We also had 6 hours of meetings each week, so that makes 7*6 = 42 hours extra for everyone.
* Parents or guardians of these children.
* Teachers that can tell the systems what level of knowledge needs to be reached.


== User Requirements ==
* Abby
'''children:'''
** State of the art - 10 hours
* learning though playing
** Desk research - 5 hours
* competitive, want the highest reward (number of sheep)
** Programming - 40 hours
 
** Wiki - 10 hours
'''parents:'''
* Christine
* want their children to study properly
** State of the art - 10 hours
* want their children to be happy
** Desk research - 30 hours
* want their children to be motivated/interested
** Survey - 15 hours
* want to have more free time as they do not have to tutor anymore
** Wiki - 10 hours
* want to check their children's levels
** Program design - 5 hours
 
* Dennis
'''teachers:'''
** State of the art - 10 hours
* let the children learn effectively
** Presentation - 8 hours
* the system has to match the curriculum
** Programming - 45 hours
* easier to check all students' progress
** Wiki - 10 hours
 
* Ellen
= Objective =
** State of the art - 10 hours
Develop a smart quiz inside a smart home that can learn himself and ask children fitting questions to let him gain knowledge.
** Desk research - 30 hours
 
** Survey - 15 hours
= Approach =
** Wiki - 10 hours
Creating a simulation in Unity and C#.
** Presentation - 5 hours
Creating the smart quiz in C#.
* Sophie
 
** State of the art - 10 hours
== Intelligent Quiz Master ==
** Presentation slides and demo - 5 hours
'''Idea.''' Use a set of arithmic questions (addition, subtraction, fractions) since then it is easy for us to check if it makes sense.
** Programming - 45 hours
Also, since most children have difficulties with arithmic this is actually useful.
** Wiki - 10 hours
 
Given a set of questions, the quiz master will test the knowledge of a child, and help the child improve by asking the right questions at the right time.
We will build an application that selects the next question to ask the child, based on the previous answers the child gave to previous questions.
 
The quiz master has to:
 
* Find out the level of knowledge the child has, and ask questions that are on the 'edge' of a childs knowledge in order to improve their knowledge.
* Optionally invent new questions, similar to the already existing questions.
 
In order to do so, we must:
 
* Define '''distance''' (or '''question similarity''') between questions, which questions are of similar difficulty. So cluster questions based on their difficulty. Note that this will vary per child.
* Simulate the (increasing/decreasing) knowledge of different children. (To be able to train our app.)
* Construct a (large enough) data set to use parts of it for training and validation.
* Find out what the next '''right''' question would be. Our app should do this, based on the question similarity for a certain child. Educational/psychological: what are the best questions to ask?
 
== Milestones ==
* Finishing planning
* Summarizing SotA
* Quiz
** Teacher can enter category and boundary (knowledge goal that has to be reached)
** Smart home/quiz can generate questions inside category
** Smart home/quiz can understand the person's input
** Smart home/quiz uses input to generate personal level questions
** Smart home/quiz gives results to parents/teachers
* Simulation
** Create environment
** Create moving people
** System can react to people and environment
** Use quiz
** Reward system (SHEEP)
** Feedback system
 
== Deliverables ==
Smart quiz program and a simulation/game that shows how it would be implemented in a smart home with actual people.
 
= Who will do what? Planning =
Dennis    -> quiz programming
Sophie    -> simulation
Ellen     -> wiki
Abby      -> quiz programming
Christine -> quiz design
 
'''week 1:'''
* literature search, SotA summary
* make plan
* setup Git
* Unity tutorial
* update wiki
'''week 2:'''
* interaction plan
* script simulation
* quiz plan:
  * how to make AI
  * categories
  * creating questions
* update wiki
'''week 3:'''
* UML of quiz
* teacher program finished
* smart quiz has to generate questions inside the categories
* creating simulation environment and people
* update wiki
'''week 4:'''
* quiz has to read input (parsen?)
* start with letting quiz learn from input
* simulation has to react to environment and people
* update wiki
'''week 5:'''
* quiz has to learn from input
* simulation has to use quiz
* update wiki
'''week 6:'''
* quiz has to learn from input
* feedback system in quiz
* reward system in simulation
* feedback system in simulation
* update wiki
'''week 7:'''
* BUFFER
* start with presentation
'''week 8:'''
* final presentation
 
= State of the Art Literature Study =
 
About similar questions, though used more on Q&A sites to find similar questions. This is not based on difficulty.
<ref> A Topic Clustering Approach to Finding Similar Questions from Large Question and Answer Archives, from http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0071511&type=printable</ref>
<ref> ''Deepa Paranjpe'', Clustering Semantically Similar and Related Questions, from https://nlp.stanford.edu/courses/cs224n/2007/fp/paranjpe.pdf</ref>
 
Chapter 7 and/or 8, about knowledge and learning paths (mathematically).
<ref> Knowledge Spaces, Chapters 7,8, from https://link-springer-com.dianus.libr.tue.nl/content/pdf/10.1007%2F978-3-642-58625-5.pdf</ref>
 
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.
<ref> ''Habgood, M.P.J. and Ainsworth, S.E.'', Motivating children to learn effectively: exploring the value
of intrinsic integration in educational games, from http://shura.shu.ac.uk/3556/1/Habgood_Ainsworth_final.pdf</ref>
 
"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."
<ref> ''Göbel, S., Wendel, V., Ritter, C., and Steinmetz, R.'', Personalized, Adaptive Digital Educational Games using Narrative Game-based Learning Objects, from ftp://dmz02.kom.e-technik.tu-darmstadt.de/papers/GWRS10_507.pdf</ref>
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.
<ref> ''Kickmeier-Rust, M.D., Göbel, S., and Albert, D.'', 80Days: Melding Adaptive Educational Technology and Adaptive and Interactive Storytelling in Digital Educational Games, from http://ceur-ws.org/Vol-386/p02.pdf</ref>
 
In the ELEKTRA game they use unobtrusive knowledge assessment to see the user's level of knowledge.
<ref> ''Kickmeier-Rust, M. D., Hockemeyer, C., Albert, D., & Augustin, T.'', Micro adaptive, non-invassive assessment in educational games, from http://css-kti.tugraz.at/research/cssarchive/publicdocs/publications/kickmeier-rust_adaptivity.pdf</ref>
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.
 
Paper about using smart homes to learn about human behaviour.
<ref> ''Brdiczka, O., Langet, M., Miasonnasse, J., and Crowley, J.L.'', Detecting Human Behavior Models From Multimodal Observation in a Smart Home, from https://pdfs.semanticscholar.org/1fa3/5bbda418228e59ab49bb4aff302701f21583.pdf</ref>
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."
<ref> ''Majdi Rawashdeh, Mohammed GH. Al Zamil, Samer Samarah, M. Shamim Hossain, Ghulam Muhammad'', Micro adaptive, non-invassive assessment in educational games, from https://ac.els-cdn.com/S0167739X17311342/1-s2.0-S0167739X17311342-main.pdf?_tid=bef05bc4-14aa-11e8-abcb-00000aab0f01&acdnat=1518958610_409380ed13eef0bd5bb3b6332b1e9138</ref>
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".
<ref> ''Hong Zhou, Brad Goold'', A Domestic Adaptable Infant Monitoring System Using Wireless Sensor Networks </ref>
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.
 
Lastly, 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."
<ref> ''Zhi-Hong Chen, Chih-Yueh Chou
, Shu-Fen Tseng, Ying-Chu Su'', Feedback of Interface Agents on Student Perception: Level, Dialogue, and
Emotion, from https://pdfs.semanticscholar.org/d7d2/5d3ff5f286951486b5b11956cfc0c65619a1.pdf </ref>
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.
 
Now, we propose some papers related to the way the educational aspectof our smart home is designed and implemented. First, a paper that does not seem completely relevant to the project at first.
<ref> ''E. Oliver'', Gamification as transformative assessment in
higher education, from http://www.scielo.org.za/pdf/hts/v73n3/55.pdf </ref>
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 implementationof 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. 
 
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."
<ref> ''Marja Kankaanranta, merja Koivula, Marja-Leena Laakso, Marleena Mustola'', Ditital Games in Early Childhood: Broadening Definitions of Learning, Literacy, and Play, from https://books.google.nl/books?hl=nl&lr=&id=BsVCDgAAQBAJ&oi=fnd&pg=PA349&ots=c-UyQVsRzx&sig=RIrTs2S-MZTh5cirTbIKerQ9M10#v=onepage&q&f=false </ref>
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.
 
Lastly, a book that could possibly be helpful. It proposes design principles of educational virtual worlds of preschool children.
<ref> ''Jana Pejoska'', Design principles of educational virtual worlds for preschool
children, from https://jyx.jyu.fi/dspace/bitstream/handle/123456789/26862/URN:NBN:fi:jyu-2011050210720.pdf?sequence=1 </ref>
Especially the principles described in chapter 4 could be applied to the designing of our smart home system.
 
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".
<ref> ''Antonio Sanchez and Lisa Burnell'', A Smart Home Lab as a Pedagogical Tool, from https://link.springer.com/chapter/10.1007/978-3-642-30171-1_12 </ref>
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.
 
We reference 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".
<ref> ''Tamie Salter, Francois Michaud, and Dominic Letourneau'', An Exploratory Investigation into the Effects of Adaptation in Child-Robot Interaction, from https://link.springer.com/chapter/10.1007/978-3-642-03986-7_12 </ref>
The conclusions in this article can be useful in describing the effects of using a Smart Home system for learning purposes.
 
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.
<ref> ''Kleopatra Nikolopoulou & Vasilis Gialamas'', Barriers to the integration of computers in early childhood settings: Teachers’ perceptions, from https://link.springer.com/article/10.1007/s10639-013-9281-9 </ref>
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."
<ref> ''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 https://link.springer.com/article/10.1007/s12369-010-0080-9 </ref>
This study can be used to aid the social learning aspect of childhood in the proposed smart home system.
 
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.
<ref> ''Fumihide Tanaka'', Robotics for Supporting Childhood Education, from https://link.springer.com/chapter/10.1007/978-4-431-54159-2_10 </ref>
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.
 
== The tutoring system ==
* 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. <ref> 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 https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9487/9685 </ref>
 
* Combine Artificial intelligence with tutoring has already been used and examined for several years, this is an intelligent tutoring system. When designing such a program you should keep a few things in mind, for instance provide immediate feedback on errors. <ref>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, https://doi.org/10.1016/B978-044481862-1.50103-5</ref>
 
== 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. <ref> 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 https://doi.org/10.1016/j.cmpb.2008.02.001</ref>
 
* It is possible for a smart home to detect speech and react upon it in crowdy and noisy environments <ref> 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 https://hal.archives-ouvertes.fr/hal-00422576/document </ref>
 
* Smart homes can use their social features to interact with persons. <ref> 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, https://doi.org/10.1016/j.maturitas.2009.07.014.</ref>
 
= References =
<references/>


='''Coaching Questions'''=
[[Coaching Questions Group 14]]
[[Coaching Questions Group 14]]

Latest revision as of 14:21, 4 April 2018

Student Student Number
Abby Berkers 0951825
Dennis van den Berg 0949036
Sophie van den Eerenbeemt 0954445
Christine Ingwersen 0952530
Ellen Mans 0956433

Process

Process Group 14

Report

Report group 14

Peer review and Contributions

Peer review

As all of us have contributed a lot to this project, there was good communication and we do not have any negative remarks about each other, we decided that each of us deserves the same result. This is why we decided on the following peer review results:

  • Abby: 7,5
  • Christine: 7,5
  • Dennis: 7,5
  • Ellen: 7,5
  • Sophie: 7,5

Of course there is always room for improvement and our project could have been better, which is why we think 7,5 is a good representation of what we have done.

Contributions

We also had 6 hours of meetings each week, so that makes 7*6 = 42 hours extra for everyone.

  • Abby
    • State of the art - 10 hours
    • Desk research - 5 hours
    • Programming - 40 hours
    • Wiki - 10 hours
  • Christine
    • State of the art - 10 hours
    • Desk research - 30 hours
    • Survey - 15 hours
    • Wiki - 10 hours
    • Program design - 5 hours
  • Dennis
    • State of the art - 10 hours
    • Presentation - 8 hours
    • Programming - 45 hours
    • Wiki - 10 hours
  • Ellen
    • State of the art - 10 hours
    • Desk research - 30 hours
    • Survey - 15 hours
    • Wiki - 10 hours
    • Presentation - 5 hours
  • Sophie
    • State of the art - 10 hours
    • Presentation slides and demo - 5 hours
    • Programming - 45 hours
    • Wiki - 10 hours

Coaching Questions

Coaching Questions Group 14

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