Measuring learner's performance in e-learning recommender systems

A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test

Parallel classification and optimization of telco trouble ticket dataset

In the big data age, extracting applicable information using traditional machine learning methodology is very challenging. This problem emerges from the restricted design of existing traditional machine learning algorithms, which do not entirely support large datasets and distributed processing. The large volume of data nowadays demands an efficient method of building machine-learning classifiers to classify big data. New research is proposed to solve problems by converting traditional machine learning classification into a parallel capable. Apache Spark is recommended as the primary data processing framework for the research activities. The dataset used in this research is related to the telco trouble ticket, identified as one of the large volume datasets. The study aims to solve the data classification problem in a single machine using traditional classifiers such as W-J48. The proposed solution is to enable a conventional classifier to execute the classification method using big data platforms such as Hadoop. This study’s significant contribution is the output matrix evaluation, such as accuracy and computational time taken from both ways resulting from hyper-parameter tuning and improvement of W-J48 classification accuracy for the telco trouble ticket dataset. Additional optimization and estimation techniques have been incorporated into the study, such as grid search and cross-validation method, which significantly improves classification accuracy by 22.62% and reduces the classification time by 21.1% in parallel execution inside the big data environment

Wireless Underground Sensor Network: Improving EBMR Protocol for the Purpose of Energy Conservation and Data Transmission Reliability

Wireless Underground Sensor Networks have been widely used to transmit information such as transmitting environmental information. The main issue in Wireless Underground Sensor Networks is the power consumption since the reliability of data depends on the power availability and longevity. In this paper we proposed a new data transmission method in EBMR protocol by using a Dynamic Window Acknowledgement that is able to reduce the number of acknowledgement send between sender and receiver. As a result, it helps to reduce the energy consumption of network devices

Building an E-learning Recommender System Using Vector Space Model and Good Learners Average Rating

An enormous amount of learning materials in e-learning has led to the difficulty on locating suitable learning materials for a particular learning topic, creating the need for content recommendation tools within learning context. In this paper, we aim to address this need by proposing a novel framework for an e-learning recommender system. Our proposed framework works on the idea of recommending learning materials based on the similarity of content items (using Vector Space Model) and good learnerspsila average rating strategy. This paper presents the overall architecture of the proposed system and its potential implementation via a prototype design

A Multimedia Content Recommender System Using Table of Contents and Content-Based Filtering

In recent years, we have witnessed a rapid growth in the availability of digital multimedia on various application platforms and domains. Consequently, the problem of information overload has become more and more serious which causes a difficulty and time consuming for the users to locate and discovers the desired data. In this paper, we propose an architecture of a Multimedia Recommender system for Education videos that recommends relevant videos to the user based on Content-Based Filtering and the Table of Content of relevant books, that in turn will increase the dynamism and accuracy of the recommended multimedia data of their preference, preventing them from consuming time and effort in locating the desired video

Retail Site Recommendation: AI Approach for Location Analytics

Location analytics for retail business is very challenging, especially for new retailers in the market. Retail site selection costs long-term capital investment; hence a strategic site selection method is inevitable while setting up the retail business A tactical site analysis helps to know the suitable area for a particular business which helps to attract potential customers. Demographics and Geospatial information that includes nearby competitor businesses of prospective location, age groups, education, lifestyle, profession, income, and property are considered key features for analyzing the site of a retail business.[1] The existing research shows the application of different approaches to find the optimal algorithm for recommending a business of given location data

The Application areas in Swarm Robotics Intelligence in underwater environment.

Swarm robotics systems (SRS) are group of robotics, in which large groups of robots are able to display collective intelligent behavior. Control in a SRS, each robot works on its local observations about environment and shares information with neighboring robots. The swarm behavior emerges from the relation between neighboring robots with respect to the interactions between robots and the environment. Underwater Wireless Sensor Networks (UWSN) are network monitoring systems in underwater environments. Sensor nodes in these networks are equipped with sensing, acoustic communication, and computational capabilities, and can forward sensed information to sink nodes for processing and analysis in a one-hop or multi-hop manner

Utilizing Learners' Negative Ratings in Semantic Content-based Recommender System for e-Learning Forum

Nowadays, most of e-learning systems embody online discussion forums as a medium for collaborative learning that supports knowledge sharing and information exchanging between learners. The exponential growth of the available shared information in e-learning online discussion forums has caused a difficulty for learners in discovering interesting information. This paper introduces a novel recommendation architecture that is able to recommend interesting post messages to the learners in an e-learning online discussion forum based on a semantic content-based filtering and learners’ negative ratings. We evaluated the proposed elearning recommender system against exiting e-learning recommender systems that use similar filtering techniques in terms of recommendation accuracy and learners’ performance. The obtained experimental results show that the proposed e-learning recommender system outperforms other similar e-learning recommender systems that use non-semantic content-based filtering technique (CB), non-semantic contentbased filtering technique with learners’ negative ratings (CB-NR), semantic content-based filtering technique (SCB), with respect to system accuracy of about 57%, 28%, and 25%, respectively. Furthermore, the obtained results also show that the learning performance has been increased by at least 9.84% for the learners whom are supported by recommendations based on the proposed technique as compared to other similar recommendation techniques

Application of NLP on Big Data Using Hadoop: Case Study Using Trouble Tickets

Telecommunication company trouble tickets system contains reported incidents tickets that related to networks service interruption or problems. Trouble tickets system is the example of the application that deals with a large amount of textual data. In order for the team that is handling the trouble tickets system, they spend a lot of time to analyze the data manually. In this paper, Natural Language Processing (NLP) approach has been introduced to solve the problem. By applying this technique, manual of activities can be automated and reduce the time and effort to find the classification of the closing resolution code. It also helps the trouble tickets system to collect incidents trending and resource utilization. New data processing method with NLP and sublanguage introduced via big data platform to deliver faster classification computation. The outcome of this study is transformation method of the original data set into the analytics series, and identification the characteristics of the trouble tickets data set to enable the classification of the resolution code. The data processing workflow shows that the linguistics of the trouble tickets fit the sublanguage theoretical framework thus enabling to tap into the unrealized value inside it. The data processing and data transformation workflow describe the linguistics of the trouble tickets fit the sublanguage theoretical framework, therefore, supporting the research to tap into the unexposed content inside it