A Framework for Interactive Pattern Based Adaptive Recommender Agent Using Concept Map for Personalized e-learning: IPBARA S. Bhattacharya Computer Science and Engineering Department Techno India college of Technology Rajarhat, Kolkata - 156, India suman93
[email protected] P. Basu Computer Science Department Future Institute of Engineering and Management Sonarpur, Kolkata - 150, India
[email protected]
Abstract—This paper addresses the issue of providing personalized user interface to e-learner under web based elearning environment. A frame work, based on concept map trees, is proposed that intelligently tracks the learning pattern of an e-learner and helps the learner to attain his learning objective through the recommender agent. The system is expected to take certain period of adaptive training session to identify the various learning patterns prevailing in a webbased e-Learning environment. Keywords-Personalized e-learning, concept map, Interactive Pattern, Adaptive, Recommender Agent
I. I NTRODUCTION Along with the revolutionary development in the arena of Information and Communication Technology (ICT) elearning is undergoing a rapid change. This development requires massive amount of e-Learning resources generation. However most of the traditional systems do not have provision to appreciate personal learning. They provide the same content to students to learn or to let students opt content from the huge resources in the e-environment without giving any advice or assistance. This often leads learning process a difficult activity and makes e-learner uncomfortable. Therefore personalization of a learners educational activity enhances his total learning experience. The personalization of an educational activity is a process of combining learning resources in such a way that the learner is presented with only the appropriate material [1]. The appropriateness is considered on the basis of various aspects of the learners requirements: material should be limited to the knowledge which is actually to be acquired, and it should be well suited for personal way of learning. PLE (Personal Learning Environment) is suggested as the next-generation e-learning system [2, 3].The personalization can also be a static or dynamic process, depending on when
A.Chakraborty Computer Science and Engineering Department Techno India college of Technology Rajarhat, Kolkata - 156, India
[email protected] S. Roy Computer Science and Technology Department NITTR, Block FC, Sector-III, Saltlake, Kolkata, India
[email protected]
the selection and presentation of material takes place: if the material is decided once in advance, we say that the course is configured; when the material is stated at run-time, that is during the course delivery, the course is said to be adaptive. This paper proposes an adaptive recommender agent that recommends a personalized learning by analyzing interaction pattern of e-learner for concept map based learning materials. Learning material can be presented to the learners in various ways. Usage of concept map to represent learning material is not a new concept [concept map and objects]. A Concept Map constitutes a schematic summary of what has been learned; it is hierarchically organized and represents all abstraction levels. The proposed system takes into account the specific pattern of navigation. The signature pattern of a specific user can be the thumb rule for the customization of learning environment for that particular user and as well as adaptation of the change in signature pattern of a particular user makes this system more dynamic and reliable with the duration of time. Rest of the paper is organized in the following way: Section II discusses theoretical background and related work. In section III overview of the proposed framework has been described. Theoretical aspects of the framework are proposed in Section IV and finally Section V concludes the paper. II. BACKGROUND With the rapid development of the network technique and the prevalence of the Internet, e-learning has become the major trend of the development of international education since 1980’s. However, because of the discrepancy resulting from the essential difference among different students and the simplex pattern of current e-learning systems, students
cant learn according to their needs actively and are subsequently led to poor a learning result which leads to poor learning results [4]. Personalized Learning Environment is envisaged as a solution to the problem. In a next generation e-learning system it should be made possible to personalize the user interface. PLE should be able to present an online learning environment which covers the heterogeneous needs of a student group. It is necessary that the system be able to present personalized views / interfaces. So a new intelligent algorithm could be applied in personalized E-learning system to support personalized E-learning better [5]. However there are lots of differences between the learning styles of different e-learners. Providing corresponding teaching resources according to learners characteristics to implement personalized learning is very difficult [6] [7]. So an agent can be used to determine the learning style. In this paper [8] the author discussed on this type agent and classified them into different sub category. In this paper we introduce an agent which will recommend a learner for a customized e-learning environment based on the learning pattern of the learner
A. LMS and LCMS In essence, an LMS is a high-level, strategic solution for planning, delivering, and managing most of the learning events within an organization, including online, virtual classroom, and instructor led courses. For example, an LMS can simplify global certification efforts, enable entities to align learning initiatives with strategic goals, and provide a viable means of enterprise-level skills management. The focus of an LMS is on managing the learners by keeping track of their progress and performance across all types of learning activities. In addition to managing the administrative functions of online learning, some systems also provide tools to deliver and manage instructor led synchronous and asynchronous online training based on learning object methodology. These systems are called Learning Content Management Systems or LCMSs. An LCMS provides tools for authoring and reusing or repurposing content (Mutated Learning Objects) of MLO as well as virtual spaces for learner interaction (such as discussion forums and live chat rooms). Despite this distinction, the terms LMS is often used to refer to both an LMS and an LCMS, although the LCMS is a further development of the LMS. The above diagram represents the components that comprise a typical LCMS. The content is prepared and deposited in a repository which is accessed by the LMS and distributed to the learners. The individual learners data is also managed by the system and is available to the individual user. So one learner begins to comprehend the integration of content, managing the content for distribution, and managing learner is data.
Figure 1.
Architecture of LCMS
B. Concept Map With the rapid development of information technology, the traditional teaching methods have taken great changes. Concept maps constitute one of the tools frequently used in learning management as they offer the possibility to personalize learning, share knowledge and reinforce learning to learn skills. At the same time, many initiatives or standards are being developed rapidly to make the contents in different learning management systems and learning environments compatible. A concept map is a schematic resource to represent and organize a set of meanings in a propositional structure. In a learning process, the practice of making and remaking concept maps might be considered as an effort to find out concepts and their meanings, giving rise to the knowledge in an explicit way (Novak and Gowin, 1984). In general, a concept map springs up from early scratches and much iteration is required to improve it. Basically, a concept map is a node-link diagram to represent a concept by a node and a relationship (for example, is-a, related-to, or part of) by a link. Because the technique of concept mapping is founded to significantly improve the level of meaningful learning and communication. Concept maps are widely used for summarizing, brainstorming, communicating complex ideas, and improving language ability and so on [9, 10]. Our proposed system introduces learning material using concept map. There are several concept mapping software which can generates learning material by integrating text, drawing, table, web reference, animation, etc. This type of learning material also includes auto layout, visual effects, slides shows, etc. Fig 2 depicts an example of learning material on Object-Oriented Analysis using concept map. A learner using concept map based learning material unconsciously follows an unguided learning path which will generate a tree type data structure. Agent that used in our proposed system fetched that tree and saved it to a database as a log table. Using this log table it will generate a behavioral pattern of learner.
Figure 2. map
Learning material on Object-Oriented Analysis using Concept
passes this information along with the specific signature pattern to the Concept Map Manager and Concept Map Manager reorient the previously stored Concept Map Tree and passes this to the Parser component. Parser component will regenerate the learning content according to the newly oriented Concept Map Tree and submit to the LCMS application server. Server will show this customized learning material to the user. If users method of learning is drastically changed from the previous fashion this change is also taken account by the adaptive nature of the Pattern Recognizer component so that with time user can get better support from the agent. IV. M ETHODOLOGY
III. S YSTEM OVERVIEW The proposed system is a navigation specific Learning management system framework based on user behavior analysis that makes use of information regarding the users navigational patterns within the application environment.
Figure 3.
This section presents further details of the proposed the Interactive Pattern Based Adaptive Recommender Agent (IPBARA). The major components of this IPBARA are: The Concept Map Manager, Pattern Recognizer and the Recommender. These components are briefly described below.
High level architecture of proposed system
The system comprises of three main components; Concept Map Manager, Pattern Recognizer and Recommender Base. The learning module is first submitted to the LCMS application Server and Concept Map Manager. Concept Map Manager extracts the Concept Map Tree and stores in a temporary buffer known as Concept Tree Buffer. For every successful session of the user Application Server will generate the navigational log sequences for that specific user and stores in the Navigational Sequence Buffer. From the Navigational Sequence Buffer Pattern Recognizer reads those sequences and Recognizer generates the signature patterns for that user and and stores these signature patterns into Signature pattern base. Recommender will suggest the user about the modulation of the learning content according to the signature pattern generated by Pattern Recognizer. If user gives affirmative acknowledgement then Recommender
Figure 4.
Structure of IPBARA
A. Concept Map Manager This component builds the concept map tree based on learning content and signature pattern. The tree is finally delivered to the parser for populating learning material to the application server for generating log that in turn, is conveyed to the Pattern Recognizer through the Navigational Sequence Buffer. The component consists of two sub components: Map Extractor component and Map Regenerator component. Along with these two components
it maintains a buffer called Concept Map Tree buffer to temporarily hold the current concept map tree. Map extractor component generates node link map, where node represents learning materials and link for their relations and sequences. However Map Regenerator module regenerates the concept map tree by using the signature pattern coming from the Recommender component. Whether the current tree will be delivered or regenerated tree will be delivered to the parser that depends on the acknowledgement of the learner. The acknowledgement is coming to the Concept Map Manager via the Recommender along with the signature pattern. B. Pattern Recognizer As shown in Fig 4, in every new navigation, the navigational patterns (of n symbols) are logged in the growable Navigation Sequence Storage. In every successful session, server generates the activity log sequence which is called as N avigationalSequence through out the paper. The Pattern Recognizer module checks the whole Navigation Sequence Storage and identifies the repetitive sequence (s) of navigational patterns which is (are) designated as Signature Pattern of the user as per Adaptive Algorithm (Gen Signature Pattern) presented in the next section After a user logs in to the application, the proposed LMS framework stores the users navigational patterns up to n symbol length as Navigation Sequence S t for tth navigation. Let us suppose that up to tth navigation, t numbers of Navigation Sequence have been stored and designated as Set A where A = {S 0 , S 1 · · · S t }. Next, the Pattern Recognizer module uses Adaptive Algorithm (GenSignatureP attern) to identify k number of l length Signature Pattern out of A, where the length l is tuned adaptively in between two range values (discussed in next section) and k ≥ 1. All the k number of l length Signature Patterns thus identified, form the Set designated as B. In the light of previous sections, following discussion introduce the theoretical aspects primarily comprising an adaptive algorithm to find k number of 1 symbol length signature patterns and complexity analysis of the proposed framework. K1 and K2 [11] are two boundary integer values which will tune the length of the signature pattern l. Initially K1 will be assigned by 1 at t=0, otherwise for t > 0, K1 will be equal to the value of the length of last signature pattern. The value ofK2 will be up to the total length of the navigation sequence i.e.; n. Here x is an integer variable with a value within the range of K1 and K2 . The value of x will govern the identification of signature pattern at any specific session. It is evident that, while computing most repetitive sequence for a specific session, many pairs of (r, x) will emerge out wherer is the number of most repetitive sequence identified for a given length x. Each (r, x) provides an idea of lengthwise variation through an index termed as Tuning Factor and designated as λ where λ = r/x.
So the minimum value of λ i.e.; λmin will dictate the least variation in signature pattern lengthwise hence it is the best choice for signature pattern for the given pool of Navigation Sequence. A λmin value identified at a given session t holds the information of lengthwise variation found up to session t. Hence while computing signature pattern for session t+1, it will be sufficient to start examination of most repetitive sequence from x ´t where´ xt is the value of x for which λmin was identified at session t. Algorithm: Gen SignatureP attern Input: (i) SessionSequence S t for tth session, along with SessionSequence storage containing S 0 , S 1 · · · S t−1 and Ps0 , Ps1 · · · Pst−1 (ii) K1 = 1 (if t = 0) k x ´t−1 (otherwise) (iii) K2 = n where n = length of SessionSequences Step 0: for all x (where K1 ≤ x ≤ K2 ), do Step 1 toStep 2 Step 1: call Gen Repetitive Seq subroutine (described next) to have repetitive sequence P and number of repetitive sequence r. Step 2: compute λ = r/x and if λ ≤ λmin , λmin ← λ Step 3: designate x for which λ = λmin as x ´t t Step 4: Ps ← P Step 5: Stop. Output: SignatureP attern Pst for tth session sequence Above algorithm imparts adaptiveness in the identification of SignatureP attern from the knowledge derived from SessionSequence storage. In spite of the fact that the repetitive sequence search or the most subsequent occurrence search (algo Gen Repetitive Seq, presented below) is driven by deterministic approach, the change in K1 boundary value in every session, enables the next session to be adaptive with the knowledge derived and translated via x ´t−1 . The proper adaptive behavior of x ´t−1 ensures the diminishing difference between the values of K1 and K2 and this results in reduced search for SignatureP atttern with the progress of time. A search for repetitive sequence within SessionSequence storage is described next. This search could be optimized ( with respect to time) by employing any smarter deterministic algorithm. In its very basic form, Gen Repetitive Seq will take up (r(n ∗ p)2 ) order of time to search for the repetitive sequence of any specific length from the SessionSequence storage. Hence in the above algorithm, repetitive calling of this sub-routine will take up ((K2 − K1 )r(n ∗ p)2 ) time to search for intended SignatureP attern. Hence SignatureP attern generation can be done in polynomial run time. V. R ECOMMENDER C OMPONENT Recommender component generates recommendation of adapted learning module for the learners by regenerating
concept maps. However depending upon the acknowledgement coming from the learner, signature pattern is supplied to the concept map manager to regenerate the concept map. The signature pattern along with the acknowledgement is provided by the component to the concept map manager. For employ this, the component makes use of signature pattern supplied by the growable signature base. The recommendation will be more appropriate as the growable signature base matures by the more use of learning materials by the learners. VI. C ONCLUSION In this paper we have proposed a system which would offer a personalized e-learning environment according to the various access patterns of the e-learners. A recommender agent is incorporated in the system which could recognize the learning pattern of a learner. The agent suggests different concept maps of diversely behaved learners. It is expected that after a certain period of adaptive training session the system will provide customized e-learning environment as per requirement of an e-learner. R EFERENCES [1] Franzoni. A. L. and Assar. S. (2009).Student Learning Styles Adaptation Method Based on Teaching Strategies and ElectronicMedia Educational Technology and Society, 12 (4) pages 15-29. [2] Johnson. M, Liber. O,Wilson. S,Sharples. P,Milligan. C, Beauvoir. P.(2006)Mapping the future: The personal learning environment reference model and emerging technology ALTC 2006 The next generation Research proceedings., ISBN 09545870-5-7. [3] Kolas. L.and Staupe. A,(2006)A requirement specification of a next generation e-learning system [4] Xiaogan, Hubei,Research on Personalized E- Learning Model Xuemin Zhang Department of Computer and Science, Xiaogan University, China
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