Full Project – Intelligent recommender system for online quiz game
From e-commerce to social networking sites, recommender systemsare gaining more and more interest.They provide connections, news, resources,or products of interest.Nowadays, new technologies and the fast growth of the Internet have made access to information easier for all kinds of people, raising new challenges to education when using Internet as a medium. Information overload results from the availability of a large number of information on any topic in this jet age. To avoid information overload, a recommender system is important. Recommender systems form decision support systems for individuals as well as whole organisations by customising and recommending information or actions.
Game based learning has been studied and seen as an important alternative or supplement to traditional teaching. Existing web based platforms that utilize the power of the Internet in order to provide efficient access to information regarding computer learning games aim to create online communities.Game-Based Learning is an issue that concerns game developers, educators and trainees.
However, electronic educational games can be highly entertaining, butstudies have shown that they do not always trigger learning. Toenhance the effectiveness of educational games, we proposed an intelligent recommender agent for online quiz game. The recommendation systems havebeen tried in e-commerce to entice purchasing of goods, social networking sites and in e-learning environment to recommend courses to students but as yet to be implemented in online quiz game.
Therefore, an intelligent recommendation system for online quiz game is implemented. The motivation behind this project is to make quiz-based game full of fun, enjoyable, interactive and educative with an intelligent recommender agent as the catalyst. The recommendations will be based on the user profile, preferences and the performance of the user during the game play. The techniques used are content filtering and a randomization algorithm.In conclusion, this system will be useful and vital to promoting learning amongst students in schools and increasing the learning habit of students.
Keywords: Recommender Agent, Game-based learning, E-learning, Quiz game
Nowadays, new technologies and the fast growth of the Internet have made access to information easier for all kinds of people, raising new challenges to education when using Internet as a medium. With the development of computer technology, learning is no longer limited toclassrooms. Technology-Enhanced Learning (TEL) aims at designing, developing andtesting socio-technical innovations to support learning practices at both the individualand the organizational level (Manouselis et al., 2011). It helps the learners to learn anywhere and anytime.More and more online platforms such as Symbaloo and Graasp, enable users toaccess, share, and organize learning resources of all kinds, and manage their ownPersonal Learning Environments (PLEs). PLEs give students the opportunity to shapetheir own learning environments. Students can create, organize, repurpose andpackage their learning content and tools to learn more effectively and efficiently (Dabbagh and Kitsantas, 2012).
Recommender systems, for advising or recommending subject matter, are emerging as a growing application and research field (Schmidt-Thieme et al., 2007). These systems help users deal with information overload (Schmidt-Thieme et al., 2007). Information overload results from the availability of a large number of information on any topic in this age. Recommender systems form decision support systems for individuals as well as whole organisations by customising and recommending information. Recommender systems use computational intelligence and can be trained to find patterns between different users and subject matter. In this paper the problem of recommending questions to users in a quiz-based game is tackled.
Game-Based Learning is an issue that concerns game developers, educators and trainees. The teaching methods based on educational games are expected to be extremely attractive to either University students or people who are concerned about Lifelong Learning. In addition, the social and educational aspect of this type of communities (Bouras et al., 2003) is becoming increasingly interesting both from a technological and social perspective. Because electronic games are highly engaging, researchers have started to investigate whether they could be used to assist learning, especially for those children who have lost interest in math or other science courses (Klawe, 1998). However, there is little empirical evidence that electronic educational games can promote learning (Randel et al., 1992), unless the interaction is led by teachers and integrated with other instructional activities (Klawe,1998). One of the main reasons for this limitation of educational games is that learning how to play the game does not necessarily imply learning the target instructional domain. Learning happens only when students actively build the connections between game moves and underlying knowledge. Whether students can build these connections usually depends upon individual differences in knowledge and in the meta-cognitive skills relevant to learn from autonomous exploration (e.g., self-explanation and self monitoring) (Shute, 1993). However, this proposed online quiz-based game will be interactive, effective to use, easy to learn with little or no guide as well as providing an enjoyable user experience. It is worthy of note that most application or program developers do not always take into cognizance usability experience as every application, program or software is meant to be used by someone.
The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the particular characteristics of the data available. For example, movie watchers on Netflix frequently provide ratings on a scale of 1 (disliked) to 5 (liked). Such a data source records the quality of interactions between users and items. Additionally, the system may have access to user-specific and item-specific profile attributes such as demographics and product descriptions respectively. Recommender systems differ in the way they analyze these data sources to develop notions of affinity between users and items which can be used to identify well-matched pairs. Collaborative Filtering systems analyze historical interactions alone, while Content-based Filtering systems are based on profile attributes; and Hybrid techniques attempt to combine both of these designs. The architecture of recommender systems and their evaluation on real-world problems is an active area of research.
The term “collaborative filtering” was introduced in the context of the first commercial recommender system, called Tapestry (Goldberg et al., 1992), which was designed to recommend documents drawn from newsgroups to a collection of users. The motivation was to leverage social collaboration in order to prevent users from getting inundated by a large volume of streaming documents. Collaborative filtering, which analyzes usage data across users to find well matched user-item pairs, has since been juxtaposed against the older methodology of content filtering which had its original roots in information retrieval. In content filtering, recommendations are not “collaborative” in the sense that suggestions made to a user do not explicitly utilize information across the entire user-base. Some early successes of collaborative filtering on related domains included the GroupLens system (Resnick et al., 1994).
Further research was spurred by the public availability of datasets on the web, and the interest generated due to direct relevance to e-commerce. Netflix, an online streaming video and DVD rental service, released a large-scale dataset containing100 million ratings given by about half-a-million users to thousands of movie titles, and announced an open competition for the best collaborative filtering algorithm in this domain. Matrix Factorization (Bell et al., 2009) techniques rooted in numerical linear algebra and statistical matrix analysis emerged as a state of the art technique.
Currently, Recommender Systems remain an active area of research, with a dedicated ACM conference, intersecting several sub-disciplines of statistics, machine learning, data mining and information retrievals. Applications have been pursued in diverse domains ranging from recommending web pages to music, books, movies and other consumer products.
An intelligent recommendation system in a game context is a software agent that tries to “intelligently” recommend actions to a learner based on the actions of previous learners or the present users. This recommendation could be an on-line activity such as doing an exercise, reading posted messages on a conferencing system, or running an on-line simulation, or could be simply a web resource. Recommender systems use computational intelligence to achieve their objectives. Recommendation systems have been used successfully in e-commence, social networking sites – LinkedIn, Facebook, twitter, Google+, etc.
Recommendation systems have been used successfully in e-commerce. However, to date, intelligent recommendation system is not prominent in on-line learning environments and most importantly in a quiz-based game. The research question however is:
How can we integrate recommender systems into an on-line quiz-based game to improve its performance and ‘intelligence’?
The aim of this work is to improve the performance (intelligence) of quiz-based games by developing an intelligent recommender system for on-line quiz-based game.
The specific objectives are to:
- create a model which recommends questions to users in a quiz-based game based on their user profile
- continuously recommend/give questions (either easy or difficult) to users/learners based on his/her performance in the game play and awards points for each correctly answered question.
- adaptively test the performance of the learners in the game play and ascertain if the learners are learning
The research study provides a platform for researchers in an e-learning game-based recommendation systemsthereby opening a vista of research in recommendation systems.It also enhances the on-line learning experience, fully engaging, challenging, motivating and creates fun in the game play
The proposed system is limited in its operations and functionalities. Its search and data mining strength is limited to the system as it cannot go outside the system to search/mine for information. In essence, it cannot go to World Wide Web to mine for information with the keywords which the recommender agent extracts from the user profile.
The scope of the project however covers only the field of computer science for the quiz context. As a result, users’ interests are restricted to only questions from the field of computer science.
Recommender Agent:It is an agent that intelligently recommends actions to user based on the actions of previous users or the present user’s preferences.
Educational Game:They are games that are designed to teach people about certain subjects, expand concepts, reinforce development, understand an historical event or culture, or assist them in learning a skill as they play.
Pedagogical Agent:It is a software agent which helps learners in computer-based education
E-learning:It is the use of electronic media and Information Technologies (IT) in education.
Web-Based Quiz Game:It is a quiz game that can be played online.
Recommendation Systems:They are a type of information filtering systems that recommend products available in e-shops, entertainment items (books music, videos, Video on Demand, books, news, images, events etc.) or people (e.g. social networking sites) that are likely to be of interest to the user.
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Full Project – Intelligent recommender system for online quiz game