Adaptive course sequencing for personalization of learning path using neural network

Advancements in technology have led to a paradigm shift fromtraditional to personalized learning methods with varied implementationstrategies. Presenting an optimal personalized learning path in aneducational hypermedia system is one of the strategies that is important inorder to increase the effectiveness of a learning session for each student.However, this task requires much effort and cost particularly in definingrules for the adaptation of learning materials. This research focuses onthe adaptive course sequencing method that uses soft computingtechniques as an alternative to a rule-based adaptation for an adaptivelearning system. The ability of soft computing technique in handlinguncertainty and incompleteness of a problem is exploited in the study. Inthis paper we present recent work concerning concept-based classificationof learning object using artificial neural network (ANN). Self OrganizingMap (SOM) and Back Propagation (BP) algorithm were employed todiscover the connection between the domain concepts contained in thelearning object and the learner’s learning need. The experiment resultshows that this approach is assuring in determining a suitable learningobject for a particular student in an adaptive and dynamic learning environment

Comparative classification of student's academic failure through Social Network Mining and Hierarchical Clustering

Student academic failure are caused by several factors such as: family relationship, study time, absence, parent education, travel time and etc. This study observe several factors which are related to student academic failure by calculating the centrality degree between students to find the correlation between failure factors for each students. Furthermore, each student will be measured by measuring the geodesic distance for each factors for hierarchical clustering. The flow betwenness measure and hierarchical clustering show the promising result, where students who has similar factors value are tends to be grouped together in the same cluster. The student with high value of flow betwenness is considered as broker of network and play vital character inside network. The result of study is believed can bring important and useful information toward the student performance analysis for future better education

An approach to restrict viewing of media

Nowadays, the issue of copyright infringement of media contents becomes more vital since the media content is published on the internet for easy access to users.Due to that, the authors’ work affect adversely if their work can be copied or downloaded, modified or shared illegally by unauthorized users.In this paper, we proposed a method to restrict the viewing of images to control the media content accessibility.The method is using basic encryption scheme in order to prevent copyright violations, means, user who does not have specific player and/or with correct password can still view the grey-scale part of the media work in order to accomplish the marketing purposes.The encryption scheme integrates with certain properties such frequency and/or date time which will be used as a salt parameter for Password Based Key Derivation Function 2 (PBKDF2). The method also uses Color Lookup Table (CLUT) as an input to encrypt color table which the decryption process relies on the properties and the correct password

Java Programming Assessment Tool for Assignment Module in Moodle E-learning System

AbstractThis paper proposes an intermediate system called JAssess which is developed to provide a handy way to manage submission of students’ Java programming exercises from MoodleTM, as well as grading them semi-automatically. Details about the proposed system and the algorithm that lay behind it is explain. It presents the major methods used while evaluating the Java programming assignment and how to overcome the different environment used in JAssess and MoodleTM. A few test samples are included. Results show that the proposed model is able to display the suggested mark along with the output for every successful compilation, and will display the error along with the suggested mark for every failed compilation. Some limitations of the system and suggestions for future works section was conclude in this paper

The Analysis of Student Colla borative Work Inside Social Learning Network Analysis Based on Degree and Eigenvector Centrality

Social learning network analysis is a potential approach to analyze the behaviour of students in collaborative work. However, most of the previous works focus on asynchronous discussion forum as the learning activity.  Very few of them are trying to analyze the students' collaborative work while using wiki e-learning. This paper proposes the degree centrality and eigenvector method for identifying the collaborative work of students while in wiki e-learning. The log data of the Moodle e-learning system is observed that records the students' activities and actions while using wiki.  The result shows that there is a close similarity between the degree centrality and the eigenvector. The result also reveals the students who obtain high outdegree values.  Furthermore, Agent_1 and Agent_12 represent the students who obtained high outdegree values, which mean these two nodes are acting as source providers that able to supply information and knowledge through the network. This result also strengthened by value of closeness and betweenness where Agent_1 and Agent_12 leading on this measurement. The high closeness value of Agent_1 and Agent_12 will lead into fast spreading information since they have fastest route and has the most direct route to the other node inside the network, thus collaborative work is easy to be initialized by these Agents. This work has successfully identified collaborative work of student. This finding is believed to bring enormous benefit on the e-learning system improvement in the future

A Concise Fuzzy Rule Base to Reason Student Performance Based on Rough-Fuzzy Approach

A fuzzy inference system employing fuzzy if then rules able to model the qualitative aspects of human expertise and reasoning processes without employing precise quantitative analyses. This is due to the fact that the problem in acquiring knowledge from human experts is that much of the information is uncertain, inconsistent, vague and incomplete (Khoo and Zhai, 2001; Tsaganou et al., 2002; San Pedro and Burstein, 2003; Yang et al., 2005). The drawbacks of FIS are that a lot of trial and error effort need to be taken into account in order to define the best fitted membership functions (Taylan and Karagözoglu, 2009) and no standard methods exist for transforming human knowledge or experience into the rule base (Jang, 1993)

Analysis and Identification of Data Heterogeneity on Learning Environment Using Ontology Knowledge

Heterogeneity on learning environment is about different data and applications to support a learning process in education institutions. Distributed and various systems on learning environment is the current issues to produce big and heterogeneity data problem. A lot of relationships are formed between elements on learning environment. The element on learning environment consists of learning data, learning applications, data sources, learning concept, and data heterogeneity aspect on learning environment. These elements are interrelated and produce complex relationship between each other. A complex relationship problem between elements on learning environment makes a process of analysis and identification difficult to be done. Existing method to drawing this heterogeneity problem make confuse and misunderstanding readers. To solved this problem, researcher using ontology knowledge to describe and draw a semantic relationship that represent the complexity of data relationship on learning environment. The result of this analysis is to develop ontology knowledge to solve complexity relationship on learning environment, and also to help reader’s better understanding the complex relationship between elements on learning environment

A Quantitative Analysis of Malaysian Secondary School Technology Leadership

Effective school administrators are keys to large-scale, sustainable education reform. Rapid changes in technology have led to new possible ways for managing and leading schools. Leadership within the context of these changes becomes a crucial agenda among school leaders all over the world. Technology Leadership is seen as the relationship between leadership and technology, whereupon the administrators must play a more proactive role in implementing technology, and more specifically strive to interface the human and information technology components. Many point to the problem of overemphasis on the technological aspect at the exclusion of the human resources function. The use of a Model of Technology Leadership, which is based on the standard set by National Educational Technology Standard for Administrators (NETS-A, 2002), is proposed. NETS-A, 2002, was initiated by International Society for Technology in Education. This paper discusses both the model and standards mentioned. It also explores the concept of Technology Leadership against the backdrop of current structure and processes in the education institution. It also reports on the findings of a survey on Administrators as Technology Leaders among 63 administrators of Secondary Schools in Negeri Sembilan. The findings explored show the existence of Technology Leadership elements in school; but school administrators scored average on the Leadership and Vision and Teaching and Learning variables and below average on the Productivity and Professional Practice variable. The t-test scores revealed that neither school location nor administrators’ gender significantly influence the level of technology leadership.Keywords: Technology Leadership; Model of Technology Leadership; Administrators as Technology Leader

Student classification in adaptive hypermedia learning system using neural network

Conventional hypermedia learning system can pose disorientation and lost in hyperspace problem that will cause learning objectives hard to achieve. Adaptive hypermedia learning system is the solution to overcome this problem by personalizing the learning module presented to the student based on the student knowledge acquisition.This research aims to use neural network to classify the student whether he is advanced, intermediate and beginner student.The classification process is important in adaptive hypermedia learning system in order to provide suitable learning module to each individual student by taking consideration of the studentsí knowledge level, his learning style and his performance as he learn through the system. Data about the student will be collected using implicit and explicit extraction technique. Implicit extraction technique gathers and analyses the studentís behavior captured in the server log while they navigate through the system. Explicit extraction technique on the other hand collects studentís basic information from user registration data. Three classifiers were identified in determining the studentís category.The first classifier determines the student initial status based on data collected from explicit data extraction technique.The second classifier identifies studentís status from implicit data extraction technique by monitoring his behavior while using the system.The third classifier, meanwhile will be executed if the student has finished studying and finished doing the exercises provided in the system. Further, the data collected using both techniques will be integrated to form a user profile that will be used for classification using simple back propagation neural network

Data mapping process to handle semantic data problem on student grading system

Many applications are developed on education domain. Information and data for each application are stored in distributed locations with different data representations on each database. This situation leads to heterogeneity at the level of integration data. Heterogeneity data may cause many problems. One major issue is about the semantic relationships data among applications on education domain, in which the learning data may have the same name but with a different meaning, or learning data that has a different name with same meaning. This paper discusses on semantic data mapping process to handle semantic relationships problem on education domain. There are two main parts in the semantic data mapping process. The first part is the semantic data mapping engine to produce data mapping language with turtle (.ttl) file format as a standard XML file schema, that can be used for Local Java Application using Jena Library and Triple Store. The Turtle file contains detail information about data schema of every application inside the database system. The second part is to provide D2R Server that can be accessed from outside environment using HTTP Protocol. This can be done using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. To implement the semantic data process, this paper focuses on the student grading system in the learning environment of education domain. By following the proposed semantic data mapping process, the turtle file format is produced as a result of the first part of the process. Finally, this file is used to be combined and integrated with other turtle files in order to map and link with other data representation of other applications