MULTILEVEL ANALYSIS OF STUDENT’S FEEDBACKUSING MOODLE LOGS IN VIRTUAL CLOUD ENVIRONMENT

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  In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the benefits of the students. In this research work, different data mining classification models are applied to analyse and predict students’ feedback based on their Moodle usage data. The models described in this paper surely assist the educators, decision maker, mentors to early engage with the issues as address by students. In this research, real data from a semester has been experimented and evaluated. To achieve the better classification models, discretization and weight adjustment techniques have also been applied as part of the pre – processing steps. Finally, we conclude that for efficient decision making with the student’s feedback the classifier model must be appropriate in terms of accuracy and other important evaluation measures. Our experiments also shows that by using weight adjustment techniques like information gain and support vector machines improves the performance of classification models.
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  • 1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 5, October 2017 DOI:10.5121/ijcsit.2017.9502 15 MULTILEVEL ANALYSIS OF STUDENT’S FEEDBACKUSING MOODLE LOGS IN VIRTUAL CLOUD ENVIRONMENT Ashok Verma1 , Sumangla Rathore2 , Santosh Vishwakarma3 and Shubham Goswami4 1 Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India 2 Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan,India 3 Department of Computer Science & Engineering,Gyan Ganga Institute of Technology & Sciences, Jabalpur, India 4 Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India ABSTRACT In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the benefits of the students. In this research work, different data mining classification models are applied to analyse and predict students’ feedback based on their Moodle usage data. The models described in this paper surely assist the educators, decision maker, mentors to early engage with the issues as address by students. In this research, real data from a semester has been experimented and evaluated. To achieve the better classification models, discretization and weight adjustment techniques have also been applied as part of the pre – processing steps. Finally, we conclude that for efficient decision making with the student’s feedback the classifier model must be appropriate in terms of accuracy and other important evaluation measures. Our experiments also shows that by using weight adjustment techniques like information gain and support vector machines improves the performance of classification models. KEYWORDS Educational Data, Educational Data Mining,LMS, Moodle, Feedback system, weight adjustment techniques.
  • 2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017 1. INTRODUCTION The use of web-based education systems or e last years, spurred by the fact that neither students nor teachers are bound to any specific location and that this form of computer- platform. In particular, collaborative and communication tools are also educational contexts and as a result. Learning Management Systems (LMSs) are becoming much more common in universities, community colleges, schools, and businesses, and are even used by individual instructors in order to add web te face-to-face courses. LMSs can offer a great variety of channels and workspaces to facilitate information sharing and communication among participants in a course. They let educators distribute information to students, produce content material, prepare assignments and tests, engage in discussions, manage distance classes and enable collaborative learning with forums, chats, file storage areas, news services, etc. Some examples of commercial systems are Blackboard and TopClass while some examples of free systems are Ilias Cloud computing can store a huge amount of educational resources and provide infrastructure, platform, and application services for users instead of letting users sav can also provide unlimited computing power for the completion [1][2]. Nowadays, one of the most commonly used Learning Management System is Modular Object Oriented Dynamic Learning Environment ( the creation of powerful, flexible and engaging figure shows an architecture of Moodle system which is based on a layered approach. Figure 1.Architecture International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017 based education systems or e-learning systems has grown exponentially in the spurred by the fact that neither students nor teachers are bound to any specific location -based education is virtually independent of a specific hardware platform. In particular, collaborative and communication tools are also becoming widely used in educational contexts and as a result. Learning Management Systems (LMSs) are becoming much more common in universities, community colleges, schools, and businesses, and are even used by individual instructors in order to add web technology to their courses and supplement traditional face courses. LMSs can offer a great variety of channels and workspaces to facilitate information sharing and communication among participants in a course. They let educators on to students, produce content material, prepare assignments and tests, engage in discussions, manage distance classes and enable collaborative learning with forums, chats, file storage areas, news services, etc. Some examples of commercial systems are ackboard and TopClass while some examples of free systems are Ilias,Claroline and Moodle. Cloud computing can store a huge amount of educational resources and provide infrastructure, platform, and application services for users instead of letting users save them in their devices. It can also provide unlimited computing power for the completion of various types of Nowadays, one of the most commonly used Learning Management System is Modular Object Oriented Dynamic Learning Environment (Moodle), a free learning management system enabling the creation of powerful, flexible and engaging online courses and experiences. The following figure shows an architecture of Moodle system which is based on a layered approach. Figure 1.Architecture of Moodle System International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017 16 learning systems has grown exponentially in the spurred by the fact that neither students nor teachers are bound to any specific location based education is virtually independent of a specific hardware becoming widely used in educational contexts and as a result. Learning Management Systems (LMSs) are becoming much more common in universities, community colleges, schools, and businesses, and are even used by chnology to their courses and supplement traditional face courses. LMSs can offer a great variety of channels and workspaces to facilitate information sharing and communication among participants in a course. They let educators on to students, produce content material, prepare assignments and tests, engage in discussions, manage distance classes and enable collaborative learning with forums, chats, file storage areas, news services, etc. Some examples of commercial systems are ,Claroline and Moodle. Cloud computing can store a huge amount of educational resources and provide infrastructure, e them in their devices. It f various types of application Nowadays, one of the most commonly used Learning Management System is Modular Object Moodle), a free learning management system enabling The following figure shows an architecture of Moodle system which is based on a layered approach.
  • 3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017 17 Moodle (Modular Object-Oriented Dynamic Learning Environment) is a mainstream open-source learning management system widely welcomed due to its simple and clear operation as well as flexible space expanding capability [24]. The typical working environment is a server with LAMP, short for Linux operation system, Apache server, Mysql database and PHP scripting language. This architecture could optimize Moodle and makes it more stable and safer. A Moodle Virtual Cluster is constructed through virtualization technology in cloud computing to spread excessive load in single server of Moodle and avoid aforesaid shortcomings in physical cluster. Virtual cluster can dynamically allocate resources on demands, thus utilizing resources efficiently and constructing an elastic computing architecture [5][17]. Moodle has benefited many Universities and colleges around the world. Moodle allows teachers to assess hundreds and thousands of students’ performance as it allows the instructors to track students’ grades, personal information, academic results, number of tests taken, and other activities. This data is usually termed as Educational data. The data collected from Students' is huge and manually extracting any useful information from such huge dataset is not an easy task. One such approach which can be used here is Data Mining. Data Mining is a technique of extracting useful and hidden information from large databases. It is also termed as a "Knowledge discovery" process. This knowledge once mined can be used to increase revenues, sales, cut costs or both. [19]. In the past few years, researchers have largely focused on using Data Mining techniques such as classification, clustering, association rule mining to analyse educational data; i.e. to improve the educational system. These methods have been proven immensely useful in analysing students' learning behaviour and performance. Data mining techniques have been successfully applied to educational data; to understand students' learning issues, recommendation system: where new courses are recommended on the basis of their performance, feedback for teachers, etc. Though there are several tools which can be used for analysis; but they have not given much insight into students' behaviour. The educational data can be mined to understand students’ behaviour using data mining techniques; this is termed as Educational Data Mining. 2. RELATED WORK The problem of high computational load in Moodle server has been addressed by Guo et al. [1]. They proposed that the centralized server load must be allocated to several other servers in terms of virtual clusters. They perform concurrent access pressure test to evaluate the virtual cloud clusters performance and found that it improves transaction capability of the system. Chen et al. [4] proposed an efficient resource management system for on-line virtual clusters provision, aiming to provide immediately-available virtual clusters for academic users. Particularly, they investigated two crucial problems: efficient VM image management and intelligent resource mapping, either of them has remarkable impact on the performance of the system. Another important work carried out by [20] about the usage of data mining applied for personalization in web environments. They also develop tools for web based learning environment primarily focused on evaluating learning process of the educational system. A similar work is carried out by [21] which focus on web-based technology that relates affordability of accessing the ubiquitous Web and the simplicity of deploying and maintaining
  • 4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017 18 resources. They discussed some data mining and machine learning techniques that could be used to enhance web-based learning environments for the educator to better evaluate the leaning process, as well as for the learners to help them in their learning endeavour. Another approach as suggested by Alves et al. [16] is to extends a recent comparative study covering four different courses lectured at the Polytechnic of Porto - School of Engineering, in respect to the usage of a particular Learning Management System, i.e .Moodle, and its impact on students' results. A similar approach was proposed by Daraghmi et al. [23]. They suggested that a new method for Moodle a web based interface supporting a wide range of activities including forums, contents, and assignments is provided to assist both the teachers and the students. However, limitations of the file-size acceptable for uploads, weakness in the assessment procedure, complicated task of replacing an existing file, and the lowest visiting rate of the traditional discussion module are some major deficiencies in the traditional Moodle module. They also integrated Moodle with Facebook to increase the visiting rate of the traditional discussion Moodle module, hence, increasing the students' motivation to ask questions and the interaction among the students. The performance and the usability of the new module were evaluated and promising results were obtained. Another seminal work by Nagi&Suesawaluk [18], suggested that virtual learning environment is designed to help educators create online courseware with opportunities for rich interaction. Interactivity is becoming a key facet of eLearning. Moodle logs all activities including views and posts for all learning objects hosted in the system. It provides different statistics to help the content experts to improve the quality of eLearning courseware. They also describes the use of automated, scalable real-time containing data of all activity for four major ICT courses offered as a part of the Master Degree eLearning program at Assumption University of Thailand. Recently Holbl et al. [19] presented experience with learning management system Moodle when used in their educational process. They discussed the use of a feedback form enabling students to asses and comment courses. Additionally, the results of a questionnaire compiled to gain data on student experiences with Moodle with focus on features of the platform and specific privacy concerns are presented. Further, the relation between the experience gained with the course and the questionnaire results is described. Students were asked about Moodle features they use and specific privacy concerns, including visibility of profiles, results and grades. Also students' relation to giving feedback feature of Moodle user’s analysed. Another important work on web usage mining with multilevel analysis and data pre-processing is given by Sael et al. [20]. This research illustrates the potential of Web Usage Mining on e- Learning domain. They used educational data mining techniques to analyse learners' behaviour, to help in learning evaluation and to enhance the structure of a given course. They focused on the pre-processing task, which is considered as the most crucial phase in the whole process. They also presented multidimensional graphics in order to understand users' accesses. These aggregated variables provide teachers and tutors with interesting knowledge about students' learning process according to different levels of content accessed. Recently an important work is carried out by Pong &Rungworawut [21] which focus on the new pattern of teaching evaluation. The research carried out by them also presents analysis model for
  • 5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017 19 teaching evaluation from answering and posting a comment to discussion in form of open-end question obtained from Moodle LMS forum using data mining techniques. The techniques extract classification of attitudes that are defined positive and negative attitude from students to instructor for improvement of learning and teaching. Another important paper is given by Gil et al. [22]. They proposed a new way to verify the identity of users in learning management systems used at Higher Education. The starting point is to study the needs demanded by the new Distance Education model. After the study of possible security risks in the new environment, they conclude that the use of biometrics tools provide a good mechanism to check the user identity. In this article the authors show how to integrate a fingerprint verification system in an open source LMS called Moodle as a reliable method for achieving user authentication. The system was used successfully for a group of Industrial Engineering students at UNED for accessing their Electronic laboratory exams. They also analysed the students' attitudes faced with a new way of control access both from the technical and personal points of view. Based on the extensive literature survey carries out, our proposed work focus on first; the pre- processing step with the Moodle corpus as an important and key aspects before analysing the students feedback pattern and second; the recommendation based as the solution of the problems identified. 3. METHODOLOGY For assessment of our approach, we utilized the information from the Moodle framework. Moodle, like most LMSs, records every one of the understudies' utilization data in log files as well as straightforwardly in a database. There exists large number of interrelated relations in the Moodle database. Be that as it may, this data is not required thus it is additionally important to change over the valuable information to the required arrangement utilized for the mining purpose. Therefore, Moodle information must be pre-processed to change over it to the adoptable format for further processing and mining. Then, various data mining algorithms (classification algorithms in our case) will be executed to find hidden patterns and data inside the info of interest for the instructor. So, this mining method consists of three phases of knowledge discovery process: pre-processing, data processing, and post-processing. Next there is an overview in further detail relating to how these steps have been perform with the Rapidminer tool used in this research. In the pre-processing step, the user created data files from the Moodle database have been imported in excel worksheet.Our mining tool additionally splits the data file into parts, such as training and testing files. Table 1 shows the list of attributes used for processing of the Moodle data. The feedback of students have been assessed with course name as identification of course, N_post as the total number of posts submitted by student in a current semester, N_post sub is the attribute which signify the number of posts related to teaching feedback and N_post teacher specify the post which relates teachers feedback.
  • 6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017 20 Table 1. Attributes used by students in Moodle Logs Attributes used for students Name Description Course Identification number of the course N_post No of posts send to Moodle N_post_sub No of subject related posts send to Moodle N_post_teacer No of teacher related posts send to Moodle As specify in the Figure 2, Moodle database has been integrated and several preprocessing steps have been performed as part of preprocessing. The major steps includes tokenization, stop word removal, stemming and generate n-grams during pre-processing of data. Tokenization [3] refers to process of taking character sequence from defined document unit and breaking it into words, symbols, phrases and numbers called tokens. Stop word removal [4] filter out the words that have no values for retrieval purpose. Stemming [5] perform replacement of all the variations of the words with its root word. The variant words may be plurals, gerund forms, prefixes, suffixes etc. a stem word can represents all of its variants that reduces the size of dictionary containing all words of document collection. In our analysis Porter algorithm found best because it produces maximum number of tokens. Figure 2 Import of Moodle database and preprocessing
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