Data mining in e learning a review hembade and prasad

Unlock Management (Research Journal in Management Sciences), Vol. I, Issue I, Oct. 2009, ISSN: 0975-8038

Data Mining in E-learning a Review 1

Satyawan C. Hembade, 2 Dr. M.S. Prasad.

Bharati Vidyapeeth University Institute of Management and Entrepreneurship Development, Pune

restructure the course contents, change the content sequence and technology can also be changed and modeling the learners behavior. To achieve such needs various data mining methods can be used. These methods classify and cluster the eLearning resources and students into various categories.

1. Abstract E-Learning systems are just traditional based web portals or Intelligent Tutoring Systems (ITS) which takes the advantages from latest technologies such as Artificial Intelligence. During the usage of ITS various data such as navigation sequence, data related to student learning and assessment is generated. This data can be mined to find some hidden patterns and result of mining process is used to redesign the course contents according to student’s need. This article discusses some of data mining methods used in e-learning environment.

3. Classification Learning

Problem

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Classification is used to find the relationship between a set of multivariate data items and a certain set of outcomes for each of them predefined groups and find. There are plenty of methods used for classification; some of them include Nearest Neighbor, Naive Bayes classifier and Neural Network. In the following section we shall see some of the techniques (or families of techniques) have been applied to elearning.

2. Introduction E-learning utilizes a network (LAN, WAN or Internet) for delivery, interaction, or facilitation. E-Learning systems can make use of a wide range of technologies and media; each technology uses different delivery media or interaction tools. It is also important to realize that each user of a system will often learn best with certain technologies. E-learning include various modes of learning such as distributed learning, distance learning (other than pure correspondence), Computer Based Training (CBT) delivered over a network, and Web Based Training (WBT). In order to make sure that target users get most benefited from the system the data generated during the usage of elearning system can be mined using various methods and result is used to

3.1 Fuzzy Logic Methods Data mining works on large data but elearning modules give us less data; data mining techniques are required to apply on this data. The performance of learner depends on usage of e-leaning modules; this usage is not just quantitative but is fuzzy. Good student will make less usage and perform well while normal student might make more usage. This less or more usage are fuzzy terms and might vary from module to module. This fuzzy data can be used to evaluate elearning websites [1]. One of the need of e-leaning or a Leaning Management 1

System (LMS) is to provide functionality to search the data; which leads requirement of a good search module that has to be capable of correcting the user’s mistakes, finding the right and relevant information and present it in an intuitive manner. One such approach based on fuzzy logic algorithm is discussed in [2]. It makes simpler for the teachers and the students to find the relevant information.

fuzzy implementation helps to encode both structured and non-structured knowledge. On the other hand, for learners, the neural network approach has been applied to make personalized curriculum profile based on individual learner requirements in a fuzzy environment.

3.2 Using Artificial Neural Network (ANN)

Fuzzy model is used for the evaluation of students in an Intelligent Tutoring System (ITS) [3] was presented. Fuzzy theory was used to measure and transform the interaction between the student and the ITS into linguistic terms; these fuzzy relation are then used with various algorithms. {which/ why }

ANN is used to classify the e-Learner types and organize learning material.

Based on these e-Learner groups, users can obtain course recommendation from the group's opinions. When groups of related interests have been established, the DM is used to find the best learning path [6].

In [4] fuzzy data is used to form fuzzy set and then used with association rule mining algorithms to fed back to teachers for refining or reorganizing the teaching materials and tests. It uses an inductive learning algorithm of the AQ family: AQR in second phase, to find the concept descriptions indicating the missing concepts during student’s learning. The results of this phase could also be fed back to teachers for refining or reorganizing the learning path.

The Neural Network can be used to examine the ability of student and predicting his and her performance.

Personalization of e-learning system can be done using multi-agent e-learning system based on Item Response Theory (IRT) and Artificial Neural Network (ANN) which presents adaptive tests (based on IRT) and personalized recommendations (based on ANN) [7].

Artificial Neural Networks can be used to realize fuzzy relations operated with the max-min composition [3]. These fuzzy relations represent the estimation made by human tutors of the degree of association between an observed response and a student characteristic.

Neuro-fuzzy approach based on an evolutionary technique is used to obtain an optimal learning path for both instructor and learner [5]. The diagnostic model uses neuro-fuzzy synergy to imitate instructor in diagnosing learners’ characteristics, and equips the intelligent learning environment with reasoning capabilities. These reasoning capabilities can be used to drive pedagogical decisions based on the learning style of the learner. For the instructor the neuro-

3.3 Using Graphs and Trees Graph theory plays a vital role in data mining; Graph Mining is a separate research branch of data mining. Elearning resources are organized in tree / 2

or graph structure and usage of resources also generate the sequence graph. These graphs are mined to personalization of courses, based both on the student’s needs and capabilities and on the teacher’s profile [8]. Personalized learning paths in the courses were modeled using graph theory.

line course activities [12, 13]. Well known Data mining algorithm the Apriori for Association Rules was applied to capture relationships among URL references based on the navigational patterns of students [21].

4. Clustering problem in e-Learning

Data mining is useful for student modeling to personalize education environment [9]. This modeling is based on the students’ learning performance and communication preferences. In [9, 10], Decision Trees (DT) as classification models was applied. A discussion of the implementation of the Distance Learning Algorithm (DLA), which uses Rough Set theory to find general decision rules, was presented in [10]: A DT was used to adequate the original algorithm to distance learning issues. On the basis of the results obtained, the instructor might consider the reorganization of the course materials.

Unlike in classification problems, in data grouping or clustering we are not interested in modeling a relation between a set of multivariate data items and a certain set of outcomes for each of them (being this in the form of class membership labels). Instead, here it is usually aim to discover and model the groups in which the data items are often clustered, according to some item similarity measure.

Data mining is used for on documents serves as a basis for knowledge extraction in e-learning environments.

One such approach is presented in [11]; this approach is based on a radical model of text data that considers phrasal features paramount in documents, and employs graph theory to facilitate phrase representation and efficient matching. In the process of text mining, a grouping (clustering) approach is also employed to identify groups of documents such that each group represents a different topic in the underlying document collection. Document groups are tagged with topic labels through unsupervised key-phrase extraction from the document clusters. The approach serves in solving some of the difficult problems in elearning by automatically organizing documents and articles based on topics, and providing summaries for documents and groups of documents.

3.4 Association Rule Mining Data generated during the usage of elearning can be used to extract useful patterns that might help faculties, content managers, and web masters to evaluate and interpret on-line course contents and activities.

Association Rules for classification are useful in the areas of learning recommendation systems [12, 13], learning material organization [18], student learning assessments [14, 15, 16, 17], course adaptation to the student’s behaviour [19, 20], and evaluation of educational web sites [18]. Inter-session and intrasession frequent pattern mining were applied to extract useful patterns that might help to evaluate and interpret on3

Usability of Artificial Neural Networks in clustering is discussed in details in [22, 23], specifically it uses Kohonen’s Self-Organizing Maps (SOM) for the evaluation of students in a tutorial supervisor (TS) system, as well as the ability of a fuzzy TS to adapt question difficulty in the evaluation process.

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5. Conclusions and Future Work We have reviewed some of data mining methods used in e-learning system. All the methods are need not be applicable to a single e-learning system. We can use these methods to improve learning experience by reorganizing resources, designing student behavioral modeling etc.

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Some future research lines will focus on developing e-learning system to use these data mining techniques and measuring the applicability of the same.

Contribution can also be done by developing and/or modifying data mining techniques which are useful for educator for improve learning process.

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