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

Detecting Ambiguity in Requirements Analysis Using Mamdani Fuzzy Inference

Natural language is the most common way to specify requirements during elicitation of requirements as stakeholders can better specify the services they want from a particular system. However, it is arguable that requirements gathered in natural language is free from error especially ambiguity. Ambiguity in requirements can cause requirement engineers or system analysts to perceive the requirements according to their understanding instead of stakeholders understanding. This study attempts to detect ambiguity mainly vagueness as early as possible using Mamdani fuzzy inference when analyzing requirements. Dataset used in this study comprises raw requirements that are still in natural language form. In order to create fuzzy rules, the analysis of the requirements in natural language involves the process of capturing the text patterns of the requirements. The results show that it is possible to use Mamdani fuzzy inference that can detect ambiguity in requirements analysis phase

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

In order to support personalized learning, an adaptive learning system should have a capability to provide each student with a suitable learning material regarding his profile. However, the issue of student varieties in acquiring every Domain Knowledge Concept (DKC), and a range of DKC important variations in a particular learning material produced a complex dependency that causes a difficulty in the learning material selection process.  Existing rule-based learning material selection approach requires the definition of a huge set adaptation rules. However, this approach usually results in inaccurate and incorrect selection due to the inconsistent, insufficient and confluence of the defined rules.  Consequently, the process of learning material selection is hard to be algorithmized, therefore, intelligent methods are applied to handle the complexity challenges. This research proposes a significance weight approach that represents the complex dependency of learning material selection problem to substitute the rules definition in the selection process. In addition, this research proposes an intelligent learning model that combines unsupervised and supervised machine learning techniques to accurately select the learning material for a particular student adaptively. The unsupervised machine learning technique is vital in obtaining a learning material classification and labelling based on the proposed significance weight. Meanwhile, the supervised machine learning technique, the Multilayer Perceptron Artificial Neural Networks conducts the adaptation process that will assign the student to suitable learning materials regarding his performance upon specific DKC. With 98% achievement of classification accuracies, this model can be considered as highly accurate in selecting a correct and suitable learning material based on student’s domain knowledge level

The student's perceptions on the usability of industrial training system and its implication

Industrial training refers to placement of students in the industry or organization for a certain period of time in order for them to apply their theoretical knowledge in the actual career world. Industrial training is one of the main components in the Computer Science curriculum in Universiti Teknologi Malaysia (DIM). Meanwhile, Industrial Training Systems (ITS) is a web based system which is developed to manage the industrial training process in DIM. ITS consist of four major modules for students which are student registration, student placement, student online log book and student assessment. Beside the four modules, ITS also facilitated with communication features like email notification and announcements. Hence, ITS become a solution to improve the current process of managing and monitoring the industrial training which were previously being done manually. Indirectly, ITS gives an opportunity for students to utilize internet technology as part of their effective learning tool during industrial training. The aim of this paper is to analyze the satisfaction rate of students towards ITS from the aspect of usability. Usability is used to measures the usable and functional of IT'ss functions from the aspect of learn ability, efficiency, memorability, errors and satisfaction. A survey has been distributed online to undergraduate students who have used ITS during their industrial training period from year 2012 until 2015. The results are derived from satisfactory Likert scales which indicate that students were satisfied with the functionalities provided by ITS

Adaptation technology for personalization in E-Learning systems

Adaptation and personalization are being very important in the elearning systems currently. This situation arises due to the overloading and accessibility of learning material resources available offered by those systems. Aborting these characteristics, learner may found themselves lost in the cyberspace without achieving their goal or learning objectives. There are two reasons as stated by Brusilovsky(1996) to emphasize the importance of adaptivity in the field of e-learning. Firstly, due to the differences of learning goals, learning styles, preferences, knowledge and background among the learners that requires a different learning activity or content to be presented. Moreover, the profile of the learner must be updated because the knowledge level of the learner increases as the learning process continues. Secondly, an adaptive system can help the learner to navigate through a course by providing user specific learning experience. For example, the same content and presentation can be difficult to understand for a beginner but for an advanced leaner it is not important and boring (Brusilovsky, 1996). Hence, the factors of knowledge level and learner preferences for different learner that can be varied greatly require an e-learning system with the capability to be adaptive

Rough set approach for classifying student's learning style : a comparative analysis

The student’s interaction in e-learning which were captured in the log file can be intelligently examined to diagnose students’ learning style. This is important since a student’s behaviour while learning online is among the significant parameters for adaptation in e-learning system. Currently, Felder Silverman (FS) is a common learning style model that is frequently used by many researchers for personalizing learning materials based on learning style. There are four learning style dimensions in FS model and most researches need to develop four classifiers to map the characteristics into the dimensions. Such approach is quite tedious in terms of data pre-processing and it also time consuming when it comes to classification. Therefore, this study propose mapping the students’ characteristics into Integrated Felder Silverman (IFS) learning styles, by combining the four learning dimensions in FS model into sixteen learning styles. However, the most crucial problem for IFS model is the difficulties in identifying the significant pattern for the classifier that has high dimension and large number of classes. In this study, fifteen features have been identified as the granule learning features for IFS. Comparative analysis of the Rough Set performance between IFS classifier and the conventional four classifiers shows that the proposed IFS gives higher classification accuracy and rule coverage in identifying student’s learning style. However, Rough Sets generate very large rules for IFS compared to the conventional FS four classifiers

Identifying sign of grit among self-paced MOOC students based on clickstream data

Previous research that investigate and predict student’s performance share various results concerning the factors that drive the students to retain and success. The factor ranging from intrinsic to extrinsic. However, few MOOC research attempt to scrutinize grit factor. Previous researches results are from survey. However, self-report survey is insufficient because the students may have different interpretation about the questions and measurement for themselves. Therefore, this is a case study that attempts to investigate the sign of grit among self-paced MOOC students using proposed features based on clickstream data. Also, the study observes whether the grit traits have relation with student’s performance. This study found that many students have high grade despite having low grit traits. However, statistical analysis shows that there is still significant correlation between grit traits and student’s grade. Other factor like unrecorded effort outside online learning may contribute to this result which can be investigated by future study. There is potential for better result by combining proposed method with collected survey

A comparative study on quality characteristics in designing educational applications

Traditional learning methods have significantly changed with the adaptation of modern technologies. Teachers and students have become more interested to build their knowledge when exploring learning materials using various devices and applications. ISO/IEC 25010:2011 and ISO 9241-11:2008 are referred to regarding five quality characteristics in designing educational applications. This paper reports the comparative study that investigates the quality characteristics in existing educational applications by applying the Kirkpatrick Model, which comprises four logical levels in the educational process. The investigation of the quality characteristics involved four types of online educational applications. The analysis shows that more than half (56.66%) of the compared characteristics were not found in the selected educational applications. Thus, the study concludes that the compared educational applications remain to have issues if software developers or software engineers do not consider the five quality characteristics, which include user interface aesthetics, appropriateness recognisability, understandability, effectiveness and satisfaction from the users’ perspectives

A model for adaptive selection of learning material in an intelligent learning system using combination of supervised and unsupervised machine learning techniques

One of the issues in personalizing student’s learning experience is the complexity to select the learning material that is suitable to the profile of each student such as his performance, level of knowledge, learning style and etc. A huge set of adaptatio n rules is required in order to design a highly adaptive learning system that can handle such complicated task. However, the adaptation rules definition task really depends on the effort of domain expert because only the domain expert is reliable in formul ating the scheme for adapting student’s profile and the learning material. We propose d an approach for intelligent and adaptive selection of learning materials that provide a consistent selection of suitable learning materials as well as reducing the depen dency on domain experts’ effort in adaptation rules definition, on the other hand, the domain expert point of view can still be encapsulated for a reliable domain knowledge concept representation. The proposed model treats the adaptation process as a super vised classification task that will assign student to suitable learning materials regarding to his performance upon specific domain knowledge concepts. Some experiments using K - means and Self - Organizing Map (SOM) for clustering and Artificial Neural Networ ks (ANN) for classification are implemented towards the learn ing materials data and student performance data as well. The experimental results have favo u rably shown that the proposed model using domain concept based clustering and classification to be prac tical and effective in solving the adaptive selection of learning material problem