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In the ELEKTRA game they use unobtrusive knowledge assessment to see the user's level of knowledge. | 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 | <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> | ||
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. | 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. | |||
= References = | = References = | ||
Revision as of 12:37, 18 February 2018
| Student | Student Number |
| Abby Berkers | 0951825 |
| Dennis van den Berg | 0949036 |
| Sophie van den Eerenbeemt | 0954445 |
| Christine Ingwersen | 0952530 |
| Ellen Mans |
Learning with Smart Home for Kids
Problem Statement
Intelligent Quiz Master
Idea. Use a set of arithmic questions (addition, subtraction, fractions) since then it is easy for us to check if it makes sense. Also, since most children have difficulties with arithmic this is actually useful.
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?
Users
- Children
- Parents
- Teachers
User Requirements
Approach
Milestones
Deliverables
Who does what? Planning
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. [1] [2]
Chapter 7 and/or 8, about knowledge and learning paths (mathematically). [3]
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. [4]
"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." [5] 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. [6]
In the ELEKTRA game they use unobtrusive knowledge assessment to see the user's level of knowledge. [7] 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. [8] 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.
References
- ↑ 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
- ↑ Deepa Paranjpe, Clustering Semantically Similar and Related Questions, from https://nlp.stanford.edu/courses/cs224n/2007/fp/paranjpe.pdf
- ↑ Knowledge Spaces, Chapters 7,8, from https://link-springer-com.dianus.libr.tue.nl/content/pdf/10.1007%2F978-3-642-58625-5.pdf
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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
- ↑ 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