Keywords

1 Introduction

The term ‘‘learning styles’’ refers to the concept that individual differs in context of how they process information [1] and students’ preferred ways to learn [2]. There are many learning style models described in literature [3–6], which show that every individual prefers to learn in different learning style. According to above theories, Adaptive e-learning system can also strengthen above concept so that incorporating student learning style definitely enhances the learning process of learner. System that incorporate learning style can provide suggestion to students as well as instructor to optimize students’ learning path. This automatic detection system also overcome the drawbacks of the traditional detection method in e-learning system which is mainly based on questionnaire. In this paper, authors describe how learning process influenced by learning styles and which parameters affect to evaluate personalized learning path according to individual learning style because preferred mode of input varies from individual to individual. Rest of the paper is divided into four sections. Second section describes background and all related work done so far in this domain. Third section explains the concept of proposed model. Fourth section is an experimental section which gives the results and related discussion. Last section concludes the whole work and gives the future aspects.

2 Background and Related Works

2.1 Background

Adaptive personalized e-learning is able to support different learning paths and contents according to learner’s preferences so that it suits and fits into every individual learner’s diverse needs and backgrounds [7, 8]. Many literature surveys showed that many work in domain of e-commerce have been done, but not much work done in e-learning domain. As advancement in technology and development of new tools, the concept of e-learning is highly demanded in view of how to know our students preferences and enhancing their learning processes. It is a challenging task in recent era. In traditional e-learning system, only few experts’ opinions were responsible to provide learning paths and content in adaptive learning system so it was teacher centric. But here we have tried to provide learner centric adaptive system which enable to support learners with more self-control and efficient learning in the given e-learning environment. In context of adaptive e-learning system, Web Usages Mining is the application area of data mining techniques through which it discovers patterns from web data, targeted towards various applications of e-learning system. Figure 1 given below shows the different application of Web Usages Mining.

Fig. 1
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Web usage mining application areas

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Web Usages Mining consists of mainly three components, preprocessing, and pattern discovery and pattern evaluation. This is show in flowchart given in Fig. 2.

Fig. 2
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Web usages mining process

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There are many data mining techniques used in different processes of Web Usages Mining for example statistical analysis, association rule, clustering, classifications, sequential patterns and dependency model are used in pattern discovery.

2.2 Related Works

LS-AEHS is an adaptive e-learning system which incorporates learning style and show the effect of learning achievements of learners after adapting matching learning materials according to their learning style [9]. Increasing the effectiveness and efficiency of learning courses and learners’ satisfaction by adapting to prior knowledge is an important approach [10, 11].

The Web Watcher [12], Site Helper [13], Krishnapuram [14], and clustering work by Mobasher et al. [15] and Yan et al. [16], all they have focused on providing Web Site personalization based on web usage mining. Yan et al. [16] used web server log data. They found and analyzed clusters of users having similar access patterns. Web Usage Mining [17] is tool in the Internet community where data form online web is converted into meaningful knowledgeable utilities. There are many data mining techniques have been used to represent student models, such as rules [18], fuzzy logic [19], and case-based reasoning [20].

3 Proposed Approach for Detecting Learning Style

In the following subsections, about learning style, features of the patterns of navigational access behavioral data relevant to learning style and implementation details are presented.

3.1 Learning Style

Learning Style (LS) of a learner is a way how a learner collects, processes and organizes information [21]. This paper is based on Felder-Silverman learning style model. According to FSLSM, each learner has a preferred mode of learning style measured in four dimensions (active/reflective, sensing/intuitive, visual/verbal, sequential/global) [1, 5, 22]. The concept for providing adaptivity based on learning styles aims to enable LMSs to automatically generate personalized learning path according to the learner’s learning style.

3.2 Relevant Features of Behavior Pattern

There are many parameters or variables values generated by user navigational pattern stored in log file on server. We only analyses those variables which directly or indirectly relevant and correlated with corresponding learning style of FSLS. These variable are described in Table 1.

Table 1 Relevant variables for each corresponding learning style dimension of FSLSM
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The threshold values for all given parameters are set according to the literature [23, 24] and by using some statistical functions. Prefix used before the variables name i.e. t, and freq, are used for time and frequency.

3.3 Implementation

Implementation of this approach basically uses multiple resources because not only one resource has all features, which gives the best result. Moodle is one of the best open source software for providing to create powerful, flexible and engaging online courses and experiences in learning management system [25]. The data gathered by Moodle LMS may require less amount of work in data pre-processing than data collected by other systems because it stored all the relevant and authenticate web usages data in database as well as in log file. Weka [26] is open source software that provides a collection of machine learning and data mining algorithms. Now DDAM model is formed based on Bayesian Network with the help of WEKA tool. Weka supports ARFF file format which gives the additional benefit for using Moodle platform.

A Bayesian Network (BN) is based on Bays theorem which gives the formula of calculating conditional probability and is composed of two components: qualitative for defining structure and quantitative part for quantify the network [27]. This Bayesian network gives the conditional probabilities of all dependent variables which represent the strength of the dependencies among nodes represented by variables used to find the corresponding learning characteristics for a particular learning style. Moodle does not have any visualization tool. Therefore, in this paper Gismo tool [28] used for visualization. Weka, Moodle and Gismos all three have many common features like they support ARFF file format, may be implemented in Java and they can work on same dataset. So, these are the main reasons of using WEKA, Moodle and Gismo tools in combination.

3.4 Dynamic Dependency Adaptive Model (DDAM)

Generally learners were not very attentive and responsive about to fill ILS questionnaire due to many reasons. They may be influenced by others which can lead to wrong information about their learning style [29]. Our proposed approach based on [30] is that the users’ preferences can change due to many reasons. Factors that affects are the type and quality of the learning objects of the course.. Therefore, the dynamic user modeling based on students’ behaviors in a course level or in a session level is strongly recommended.

Here, we only consider four dimensions of FSLS and eliminate organizational dimension because it is proved that induction is the natural human learning style. Experiments have also proved that most engineering students are inductive learners [1]. According to this, we collect all the relevant variables described above in Table 1 from LMS and set its threshold values in form of marginal probability distributions according to literature [23, 24]. By calculating the conditional probabilities of nodes which represent variables corresponding to the characteristics of particular learning style in reference to FSLS learning dimensions, Bayesian network is formed which shows the relationship among random variables. Arch provides the strength of relation among variables. Similarly based on this conditional probability distribution of parameters, we form all Bayesian Networks of all the four dimensions using relevant parameters which is shows in Fig. 3. For example for deciding input dimension, we set typ_Lo, t_t_Lo, no_visit_Lo and its threshold value as deciding factor. For example threshold values for chat, data access pattern and exam are given in Tables 2, 3, and 4.

Fig. 3
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Bayesian network model for learning style

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Table 2 Chat probability threshold value
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Table 3 Data access probability threshold value
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Table 4 Exam result probability threshold value
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4 Experiment and Result

Using our GyanDarshan e-learning Tutorial made-up on Moodle platform, it is shown in Figs. 4 and 5, 40 participants logging data who are registered in a course C++, used for experiment purpose. Now with the help of WEKA software applied with DDAM classifier which analyses the patterns and automatically detects learning. styles. The relevant variables are based on many activities data associated with topics of a course and of a particular session shown in Table 1. This also gives the learner’s navigational access and behavioral data. Gismo provides visualization tool for generating graphical representations that explores various learning aspects of students. It also shows the performance graph neatly.

Fig. 4
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Dashboard admin VIEW

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Fig. 5
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Course content topic wise with various activities

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Pre test and post test results show that if personalized learning path according to learners learning style is provided, they perform well in terms of their grade, time taken to acquire knowledge, understand learning process high precision value and get full satisfaction (Figs. 6, 7, and 8).

Fig. 6
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CPT values of all learning dimension

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Fig. 7
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Population of learners for understanding dimensions

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Fig. 8
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Populations of learners for two dimensions

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5 Conclusion and Future Works

This paper presents an approach to develop Dynamic Dependency Adaptive Model (DDAM) to integrate learning styles into Adaptive e-learning system to assess the effect of adapting educational system individualized to the student’s learning style and learning path. It evaluates its effectiveness in the learning process. This system is a learner centric instead of teacher centric which could increase the students’ autonomy. We are working on to obtain complete learner’s information’s and applying in descriptive data mining algorithms to obtain other hidden information of users so that students will get a personalized environment in near future. This model is based on the Felder Silverman learning theories but in future we will also explore other learning theories which may give other hidden and similar results which increases and strengthens of our research work and future prospects.