iElectrical and Computer Engineering Department, University of Alabama at ... of Health Informatics and Information Management, Louisiana Tech University,.
A Learning Environment Based on Knowledge Storage and Retrieval Using Concept Maps i
2 i i i Ramaraju Rudraraju , Luai Najim , Varadraj P. Gurupur , Murat M. Tanik Electrical and Computer Engineering Department, University of Alabama at Birmingham
Birmingham, AL, USA 2 Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, LA, USA
Abstract - In today's digital age, a plethora of websites host knowledge related to various professions. Individuals working in different professions in turn consume this knowledge either to advance
their
skills
or
to
accomplish
their
professional
responsibilities. As this knowledge is continuously evolving, both in terms of size and content, the process used for acquiring the
Search Engines
knowledge becomes important. Concept maps are proven to explicitly facilitate meaningful learning for all age groups. In this paper
we
propose
implemented
using
a a
new
knowledge
learning
integration
environment
that
process, facilitates
meaningful learning by enabling the users to make a connection between newly gained and existing knowledge using concept maps. We also discuss an algorithm to construct a meta-map in order to represent collective information related to a topic and
Knowledge Sources
explain the various ways to extract valuable information from it.
a & A Websites Index Terms-Concept maps, bookmarks, social networking, information retrieval, search engines, recommender systems.
I.
Fig. I: Flow of knowledge
INTRODUCTION
T
oday, World Wide Web (WWW) is an integral part of knowledge generation and dissemination. Virtually, knowledge about any topic is available to the public in various online resources such as blogs, question and answer websites (Q & A websites), articles, wikis, and online documentations. These online resources are referred to as knowledge sources henceforth. These knowledge sources host content that is useful for professionals having different experience levels. Due to diversity in the functional behavior of these knowledge sources and constant changing nature of the content, dissemination of knowledge through these knowledge sources is not organized. Further, knowledge gained by professionals by referring to the knowledge sources is for the most part not shared with others. The flow of information from knowledge sources can be interpreted as an iterative process (Fig. 1) that begins with knowledge being deposited in their websites. Users explore these knowledge sources by utilizing search engines to find information. Search engine results return links to knowledge sources and users study them to find answers to their questions. New knowledge gained by the user, which in some cases can be a direct result of consuming the existing knowledge (in knowledge sources) is again deposited back into these knowledge sources. 978-1-4799-6585-4/14/$31.00 ©2014 IEEE
Users from various professions and backgrounds explore these knowledge sources primarily using search engines like Google, Bing, etc. The knowledge gained by the user after a search exploration is often recorded in bookmarks (online or offline), files (on computer), or is just memorized. In many cases, after learning about a topic from these knowledge sources, users do not make a connection between the newly obtained knowledge with their existing knowledge about the topic. This leads to rote learning [1], mainly because the artifacts (bookmarks, files) that are used to record the knowledge gained do not explicitly enable users to relate their prior knowledge to the newly obtained knowledge. Our learning environment addresses the above mentioned problems by facilitating meaningful learning using concept maps and integrating the knowledge of users to discover relationships between the web pages in order to provide valuable information. A. Concept Maps
A concept map is a tool used for organization, representation and sharing of knowledge. Joseph Novak invented concept maps while conducting a longitudinal study aimed at understanding the learning patterns in children during their schooling years. Concept maps were used in this study for consistently representing and analyzing students' knowledge about a topic. They are based on underlying
2 principles of constructIvIst epistemology and cognitive psychology [3] and are strongly influenced by Ausubel's assimilation theory. A key principle of this theory on which concept maps are theoretically grounded comes from the epigraph of a book written by Ausubel in 1968 [2], "If I had to reduce all of educational psychology to just one principle, I would say this: The most important single factor influencing learning is what the learner already knows. Ascertain this and teach him accordingly." A concept map is represented as a collection of propositions. Each proposition contains two or more concepts connected using linking words or phrases [4] (Fig. 2). Concepts are enclosed in circles and related concepts are connected with connectors labeled with linking words. Concepts in the map are hierarchically organized, with more general concepts positioned at the top and more specific concepts towards the bottom.
A.
Web Application
Initial data from users is gathered using a web application. Professional biographies (experience, years at profession, specialty, etc.) of users are recorded. Then, users are able to import their existing bookmarks from bookmarking services such as Diigo, Delicious, Google Bookmarks, and from browser bookmarks of major browsers such as Chrome, Firefox, Internet Explorer, and Safari into the web application. Collected user Bookmarks are automatically converted into an initial concept map (Fig. 3) in which bookmark categories are represented by concepts and websites URLs' are organized as leaves under the concepts. The initial concept map can then be re-organized by the user. This concept map visually represents the knowledge of the user stored previously as bookmarks. More concepts are appended to this knowledge model through the web application or browser plug-in as the user gains more knowledge.
B. Meaningful Learning with Concept Maps
When a learner goes through meaningful learning, they integrate new concepts and propositions with existing ideas in their cognitive structure. Moreover the learner feels in control of the knowledge acquired and is capable of using it for creative thinking and problem solving [1]. Since concept maps explicitly support making connections between new and existing knowledge, using them leads to meaningful learning [3, 5, 6, 8, and 9]. In an interesting study Carnot, et al. [7] determined that use of concept maps to represent content on a web page instead of plain text, resulted in visitors (both trained and untrained in using concept maps) gaining more meaningful and accurate information from the page. II.
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LEARNING ENVIRONMENT
Our process provides a learning environment that facilitates meaningful learning as a combination of web application and browser plugin. Our proposed Web application has a full range of features needed to gather and maintain knowledge models of the users, and the browser plugin provides direct access to features of the web application right from the browser window.
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IV.
Browser Plug-in
The browser plugin-in provides an easy to use interface for the users reading through a page, to build a concept map summarizing contents of the page being read while browsing (Fig. 4). The plug-in application also checks if the page being viewed already has a stored concept map--put together by one of the visitors of the web page. If the page has a pre-stored concept map then it will be displayed in a side bar. Users can also suggest additions or modifications to existing concept maps of pages and also add them to their knowledge model. III.
KNOWLEDGE INTEGRATION PROCESS
Knowledge integration process utilizes information gathered using the web application and the browser plug-in. Web application enables users to have their bookmarks organized as a visual knowledge model. Using the browser plug-in, users represent content of a web page in form of a concept map (Fig. 4). To maintain a single source of information, while encouraging collaboration among users, we enforce that there should be only one concept map associated to each topic on a web page. This ensures that all users visiting a web page are collaboratively making necessary additions or modifications to one single concept map (associated with a topic on a web page). Users are allowed to suggest additions or modifications to the concept map and these suggestions are peer reviewed by professionals working in the same discipline. The determination of the reviewers is made using the profession and years of experience collected using the web application. A concept map of a web page represents the knowledge and understanding of all previous visitors of the page. Future visitors can benefit from the combined effort and knowledge of the previous visitors. The page's knowledge model will help the visitors in easily understanding the content. This process fosters meaningful learning by explicitly enabling the visitors to add newly acquired knowledge to their existing knowledge models and also takes advantage of the combined effort of visitors of the page.
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INDEXING CONCEPT MAPS WITH A META-MAP
A server-side database will be used to store all the concept maps generated by the previous process. The volume of the concept maps collection will require an algorithmic mechanism to index and retrieve them based on a keyword search. An optimal architecture for indexing concept maps can be realized by using meta-maps. The meta-map structure provides a logical separation-which can be physical if needed between concept maps and the meta-data related to them. The structure of the meta-map will contain a primary index to every key concept and a list of secondary indexes for every concept map related to the main concept. For example, the concept "Stack" will have a primary index in the meta-map structure and every concept map about stack will be listed in the secondary list of indexes related to "Stack". To provide an optimum indexing for the primary keys, a context will be stored with it as shown in Fig. 5. The context will ensure a better focus for searching the concept maps database. Also, with each secondary index, additional data can be stored about the concept maps such as: related web pages, confidence, activity metrics, and type of content. V.
META-MAP INFORMATION
The meta-map can hold several context-limiting factors such as context, related web page suggestions, confidence, metrics, and information summary of a topic. An example of such meta-map is shown in Fig. 6. A.
Context (primary)
Internet search engines provide the means of searching content based on keyword matching. Refinement of such search is done with adding more relevant keywords in the hope of providing a better match. The process as it stands does not guarantee a result that will satisfy the search criteria. For example, searching for the word "stack" will provide results from many disciplines that employ the word. A search for the word "stack" with the Google search engine resulted in websites that have the word "stack" in them such as stackoverflow.com, a dictionary definition of the word, a restaurant with such name, in addition to many computer science websites the define the stack concept. A concept map as it stands provides a context to the keyword being searched. The concept map shown in Fig 2, cannot be mistaken for a restaurant name for example. To optimize the process of searching concept maps, a meta-map must be created. The meta-map contains information about the specified keywords so as to provide better means for locating the best matches for the search requested. The process of search is modified in that context is first set so as to limit the relevant concept maps. Then, the resulting set will be searched for the requested keywords. B.
Related Web Page Suggestions
When a user is looking at a web page, we use the tags attached to each node in the meta-map of the current topic and offer web pages that have additional tags (that are not attached to the current page) as suggestions to the user.
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Fig. 6: Sample Meta-map
VI.
META-MAP ALGORITHM
C. Confidence
A.
Confidence is computed as a percentage to represent the number of results that match the search criteria. For a given web page, it is calculated using the number of professionals that have the current page as part of their knowledge model.
We build a meta-map for each concept added as the main topic on a web page (e.g. Stack in Fig. 2 will have a meta map). Meta-map acts as an index pointing to relevant concept maps that are built by the users for various web pages. Every time a new concept map is added on a web page, the meta map of that topic is updated by adding a link to the concept map and additional meta-information about the concept.
D. Activity Metrics
Metrics like the number of times a concept map was viewed, number of edits, and average user rating are pre calculated for each web page. E. Information Summary of a Topic
Meta-map and tags attached to the main topic can be used to formulate a summary about how the topic is explained using content on several web pages.
978-1-4799-6585-4/14/$31.00 ©2014 IEEE
B.
Meta-Map
Algorithm for Building a Meta-Map
Maintain a list of main topics that consist a list of beginning concepts 2. Start the meta-map with topic name added on a web page as the beginning concept 3. Attach the following meta information to the beginning concept a. List of tags given by the user
1.
5 b.
List of concepts consisting the path under which the web page was categorized in the knowledge model of the user c. Total number of users who has this concept added to their knowledge model d. Average user rating of the web pages that are being pointed by the beginning concept 4. Every time a new concept is added on a web page run the following steps a. Search the list of beginning tags to check if it is existing b. If existing, check if the path under which this concept was categorized partially matches to the list attached to the meta-tag. c. If the path partially matches, recalculate meta information in step 3 and add the concept under the main topic d. If the topic is new or the list does not match the list attached to any of the main topics, run steps 2 and 3 to create a new main topic. VII.
RELATED WORK
Our learning environment consists of two sub-systems, 1) knowledge storage system which enables users to store their knowledge as concept maps and 2) knowledge retrieval system which uses the collected information to provide valuable recommendations to the users. In this section we review related work relevant to both of these sub-systems. A.
Work Related to Knowledge Storage
Applications discussed below, IHMC Cmap Tool and Diigo, provide a mechanism to store knowledge obtained while browsing the web. These applications also facilitate collaboration and sharing among the users. A. l. lHMC Cmap Tool
Cmap Tool is a concept-mapping tool developed by the Institute for Human and Machine Cognition (IHMC) [3]. It provides an interface to develop concept maps, both individually and collaboratively. Their repository of concept maps can be searched to find a concept maps related to a topic. The key differences between Cmap tools and our application are 1) maintaining a single concept map related to a topic on a web page for all users in order to provide a single point of information and 2) providing direct access to the concept maps related to a web page through the browser. A. 2. Diigo
Diigo is a leading online bookmarking system that allows users to store bookmarks, highlight specific text, and tag web pages. It provides browser plugin to access the information stored (highlights, notes) about the page directly while browsing. As the bookmarks increase in numbers, it becomes difficult to keep track of the knowledge using tags and because bookmarks are not well suited for knowledge modeling, it is challenging to make connections between new and existing knowledge. B.
Work Related to Knowledge Retrieval
Since knowledge retrieval systems provide suggestions 978-1-4799-6585-4/14/$31.00 ©2014 IEEE
based on collective information stored by users, they essentially act as recommender systems. Research on recommender systems based on collaborative filtering has been undertaken since early-to-mid 1990's to help users address the problem of information overloading. Early work on recommender systems was focused on specialized areas (e.g. GroupLens [10] and Tapestry [11] mail system) and digital media domains (e.g. Ringo system [12]). GroupLens system [10], was one of the earliest recommender systems built for the Usenet news articles to provide predictions (recommendations) of what a user would like based on the ratings of like-minded readers. The Tapestry [11] mail system was developed at Xerox Palo Alto Research Center based on the belief that information filtering can be more effective when humans are involved in the filtering process. It was designed to filter E-mail messages by participants collaboratively adding annotations. Recommender systems quickly gained ground both in research and commercial practice. By late 1990's several companies were marketing recommender engines. From the earliest adoption of recommender systems in commercial applications, businesses recognized the need for recommender systems to move away from being purely recommender algorithms to hybrid recommenders that also consider input from the user. Collaborative Web Search in a corporate environment [13] and SuggestBot [14] are examples of such systems. In Collaborative Web Search in a corporate environment [13], a field study was conducted on 50 employees of a software company to study the use of a collaborative web search tool. Recommendations were added to the search engine results based on earlier selection by the employees. This resulted in improved percentage of successful search sessions and better position ordering of relevant results on the search page. SuggestBot [14] was implemented in Wikipedia, to help authors fmd work. It performed intelligent task routing based on author's previous work to reduce cost and increase value of contributions. The tool performed four times better than random task assignments. The recommendations provided by our system come under the criteria of user centric recommendations similar to [13] and [14]. VIII.
CONCLUSION AND FUTURE WORK
The novelty of our approach is in achieving two important goals while the users are doing one exercise. Users get the advantage of being able to use concept maps to get a visual representation of knowledge that will help them recall information contents of a web page and leads to meaningful learning. While doing this exercise using concept maps they are also contributing towards building a visual knowledge model about different topics on the web for the benefit of everyone. We use this combined model to extrapolate useful information in order to provide suggestions. Connecting this knowledge model with profession also adds extra confidence to the suggestions being provided.
6 Our next step is to implement a prototype of our learning environment and provide it to a group of users working as software developers. We expect to receive initial feedback and analyze the usefulness of knowledge models gathered from the users. Considering the high volumes of data that can be collected by the application as we expand the application to larger users groups and other domains, we are planning to use quantum search algorithms to improve the efficiency of data storage and retrieval. We are also working on developing algorithms to analyze the contents of concept maps in order to fmd relations between information content of web pages consisting content about similar topics. REFERENCES
[I]
Joseph D. Novak, "Learning, Creating, and Using Knowledge: Concept maps as facilitative tools in schools and corporations". Journal of e Learning and Knowledge Society Vol. 6, n. 3, pp. 21 - 30, September
[2]
Alberto 1. Canas, Priit Reiska and Joseph D. Novak, "Concept Mapping in e-Learning", E-Learning, Chap 22,pp. 122 -127,2010. Joseph D. Novak, 'The Origins Of The Concept Mapping Tool And The Continuing Evolution of the Tool", Information Visualization Journal, pp. 175 -184,2006. Joseph D. Novak, Alberto J. Canas, 'The Theory Underlying Concept Maps and How to Construct and Use Them" Technical Report IHMC CmapTools 2006-01 Rev 01-2008, Florida Institute for Human and Machine Cognition, 2008. Susan L. Miertschin, Cheryl L. Willis, "Concept Maps To Navigate Complex Learning Environments", ACM STOlTE, 2007. Yuka Egusa, Hitomi Saito, Masao Takaku, Hitoshi Terai, M kik Miwa, Noriko Kando, "Using Concept Maps To Evaluate Exploratory Search", CRES, Japan, 2010. Mary Jo Carnot, Bruce Dunn, Alberto J. Canas, "Concept Maps Vs. Web Pages For Information Searching And Browsing", IHMC, 2003. Kate Sanders, Jonas Boustedt, Anna Eckerdal, Robert McCartney, Jan Erik Mostrom, Lynda Thomas, Carol Zander, "Student Understanding Of Object Oriented Programming As Expressed Tn Concept Maps", ACM SIGCSE, 2008. Melius Weideman, Wouter Kritzinger, "Concept Mapping - A Proposed Theoretical Model For Implementation As A Knowledge Repository",
2010.
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[9]
ICT in Higher Education, 2003.
[10] Paul Resnick, Neophytos Jacovou, Mitesh Suchak, Peter Bergstrom, John Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews", ACM, 1994. [II] David Goldberg, David Nichols, Brian M. Oki and Douglas Terry, "Using collaborative filtering to weave an information Tapestry", ACM,
1992. [12] Shardanand U, Maes P, "Social information filtering: algorithms for automating word of mouth". In: Katz, I.R, Mack R, Marks L, Rosson M.B, Nielsen 1., ACM STGCHT, 1995. [13] Barry Smyth, Evelyn Balfe, Oisin Boydell, Keith Bradley, Peter Briggs, Maurice Coyle, Jill Freyne, "A Live-User Evaluation of Collaborative Web Search", pp. 1419 -1424,IJCAT, 2005. [14] Dan Cosley, Dan Frankowski, Loren Terveen, John Riedl, "SuggestBot: Using Intelligent Task Routing to Help People Find Work in Wikipedia", ACM, 2007.
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