A hybrid PSO with Naïve Bayes classifier for disengagement detection in online learning

2016 ◽  
Vol 50 (2) ◽  
pp. 215-224 ◽  
Author(s):  
GopalaKrishnan T ◽  
P Sengottuvelan
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Online Learning ◽  
Pso Algorithm ◽  
Learning Systems ◽  
Learning System ◽  
Classification Model ◽  
Data Set ◽  
Content Type ◽  
E Learning

Purpose – The ultimate objective of the any e-Learning system is to meet the specific need of the online learners and provide them with various features to have efficacious learning experiences by understanding their complexities. Any e-Learning system could be much more improved by tracking students commitment and disengagement on that course, in turn, would allow system to have personalized involvements at appropriate times in order to re-engage learners. Motivations play a important role to get back the learners on the track could be done by analyzing of several attributes of the log files. This paper aims to analyze the multiple attributes which cause the learners to disengage from an online learning environment. Design/methodology/approach – For this improvisation, Web based learning system is researched using data mining techniques in education. There are various attributes characterized for the disengagement prediction using web log file analysis. Though, there have been several attempts to include motivating characteristics in e-Learning systems are adapted, presently influence on cognition is acknowledged mostly. Findings – Classification is one of the predictive data mining technique which makes prediction about values of data using known results found from different data sets. To find out the optimal solution for identifying disengaged learners in the online learning systems, Naive Bayesian (NB) classifier with Particle Swarm Optimization (PSO) algorithm is used which will classify the data set and then perform the independent analysis. Originality/value – The experimental results shows that the use of unrelated variables in the class attributes will reduce the accuracy and reliability of a any classification model. However, the hybrid PSO algorithm is clearly more apt to find minor subsets of attributes than the PSO with NB classifier. The NB classifier combined with hybrid PSO feature selection method proves to be the best feature selection capability without degrading the classification accuracy. It is further proved to be an effective method for mining large structural data in much less computation time.

How do students perceive their own and their peers' progress in e-learning?

2020 ◽  
Vol 38 (1) ◽  
pp. 49-74
Author(s):  
Sibel Somyürek ◽  
Peter Brusilovsky ◽  
Ayça Çebi ◽  
Kamil Akhüseyinoğlu ◽  
Tolga Güyer
Keyword(s):  
Self Regulation ◽  
Database Management System ◽  
Learning Systems ◽  
Learning System ◽  
Learner Modeling ◽  
Data Set ◽  
Content Type ◽  
E Learning ◽  
Subjective Views ◽  
Peer Models

PurposeInterest is currently growing in open social learner modeling (OSLM), which means making peer models and a learner's own model visible to encourage users in e-learning. The purpose of this study is to examine students' views about the OSLM in an e-learning system.Design/methodology/approachThis case study was conducted with 40 undergraduate students enrolled in advanced programming and database management system courses. A Likert-type questionnaire and open-ended questions were used to obtain the students' views. System usage data were also analyzed to ensure the richness and diversity of the overall data set.FindingsThe quantitative data of the students' views were analyzed with descriptive statistics; the results are presented as graphics. The qualitative data of the students' views were examined by content analysis to derive themes. These themes are organized into four subtopics: the students' positive views, their negative views, their improvement suggestions and their preferences about using similar OSLM visualizations in other e-learning systems. The students' subjective views are discussed in the context of their recorded interactions with the system.Research limitations/implicationsCompetition due to seeing peer models was considered by participants both as positive and negative features of the learning system. So, this study revealed that, the ways to combine peer learner models to e-learning systems that promote positive competition without resulting social pressure, still need to be explored.Practical implicationsBy combining open learner models with open peer models, OSLM enhances the learning process in three different ways: it supports self-regulation, encourages competition and empowers self-evaluation. To take advantage of these positive contributions, practitioners should consider enhancing e-learning systems with both own learner and peer model features.Originality/valueDespite increasing interest in OSLM studies, several limitations and problems must be addressed such as sparsity of data and lack of study of different contexts and cultures. To date, no published study in this area exists in Turkey. The purpose of this study is to fill this gap by examining OSLM features in an e-learning system from the perspectives of Turkish students by using both their system interaction data and their subjective views.

A sustainable quality assessment model for the information delivery in E-learning systems

2018 ◽  
Vol 46 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Shahid Farid ◽  
Rodina Ahmad ◽  
Mujahid Alam ◽  
Atif Akbar ◽  
Victor Chang
Keyword(s):  
Quality Assessment ◽  
State Of The Art ◽  
Statistical Tests ◽  
Information Delivery ◽  
Learning Systems ◽  
Learning System ◽  
Assessment Model ◽  
Content Type ◽  
E Learning

Purpose The purpose of this study is to propose a sustainable quality assessment approach (model) for the e-learning systems keeping software perspective under consideration. E-learning is becoming mainstream due to its accessibility, state-of-the-art learning, training ease and cost effectiveness. However, the poor quality of e-learning systems is one of the major causes of several failures reported. Moreover, this arena lacks well-defined quality assessment measures. Hence, it is quite difficult to measure the overall quality of an e-learning system effectively. Design/methodology/approach A pragmatic mixed-model philosophy was adopted for this study. A systematic literature review was performed to identify existing e-learning quality models and frameworks. Semi-structured interviews were conducted with e-learning experts following empirical investigations to identify the crucial quality characteristics of e-learning systems. Various statistical tests like principal component analysis, logistic regression, chi-square and analysis of means were applied to analyze the empirical data. These led to an adequate set of quality indicators that can be used by higher education institutions to assure the quality of e-learning systems. Findings A sustainable quality assessment model for the information delivery in e-learning systems in software perspective has been proposed by exploring the state-of-the-art quality assessment/evaluation models and frameworks proposed for the e-learning systems. The proposed model can be used to assess and improve the process of information discovery and delivery of e-learning. Originality/value The results obtained led to conclude that very limited attention is given to the quality of e-learning tools despite the importance of quality and its effect on e-learning system adoption and promotion. Moreover, the identified models and frameworks do not adequately address quality of e-learning systems from a software perspective.

An m-learning framework for ERP systems in higher education

2014 ◽  
Vol 11 (4) ◽  
pp. 287-301 ◽  
Author(s):  
Brenda Scholtz ◽  
Mando Kapeso
Keyword(s):  
Higher Education ◽  
Success Factors ◽  
Theoretical Framework ◽  
Learning Systems ◽  
Learning System ◽  
Erp Systems ◽  
Content Type ◽  
Erp System ◽  
E Learning ◽  
Learning Projects

Purpose – The purpose of this paper is to investigate the factors of m-learning approaches which can be used for enterprise resource planning (ERP) system training and to propose a theoretical framework for m-learning of ERP systems. Design/methodology/approach – A literature review of several theories relating to success factors for mobile learning (m-learning) and electronic learning (e-learning) are analysed and a theoretical framework of success factors for m-learning of ERP systems is proposed. Two field studies are undertaken to identify the features of e-learning and m-learning systems which users enjoyed and which related to the factors identified in the theoretical framework. The technology acceptance model (TAM) was used to evaluate the acceptance, usefulness and perceived ease of use (PEOU) of the two systems evaluated in the field study, the openSAP e-learning application and the SAP Learn Now m-learning application. Findings – The results confirmed several of the theoretical elements identified in the framework and the m-learning system was rated positively for PEOU and perceived usefulness (PU). The findings confirmed other studies showing the importance of the quality of course content in e-learning and m-learning projects. Research limitations/implications – The empirical study was limited to a small number of participants in higher education. However, a deeper understanding of the factors influencing m-learning for ERP systems was obtained. Practical implications – The study provides a valuable practical contribution because the framework can be used in the improved design of an ERP m-learning approach, which in turn can lead to an improvement in ERP training and education programmes and ultimately ERP project success. Originality/value – Several studies propose the use of m-learning systems. However, research related to the factors impacting on m-learning projects for ERP system training is limited. The paper presents original work and the results provide a valuable contribution to several theories of m-learning.

scholarly journals Using E-learning System in Jordanian Universities during the COVID-19 Pandemic: Benefits and Challenges

2021 ◽  
Vol 14 (3) ◽  
pp. 49
Author(s):  
Ahmad Abu-Al-Aish
Keyword(s):  
Online Learning ◽  
Higher Education Policy ◽  
Learning Systems ◽  
Learning System ◽  
Successful Implementation ◽  
University Management ◽  
Policy Makers ◽  
Face To Face ◽  
E Learning ◽  
Traditional Class

During the Coronavirus Disease 2019 (COVID-19) pandemic and the national lockdowns implemented in countries around the world, many universities worldwide made the transition from face-to-face delivery to online learning using e-learning systems. However, the successful transition from traditional class-based learning to online learning depends greatly on understanding the challenges related to the implementation and use of e-learning systems, as well as the technical and management factors that need to be enhanced. This study aimed to investigate the challenges related to the use of e-learning systems in Jordanian universities and to explore the technical and management aspects that impacted the successful implementation and use of e-learning systems during COVID-19. To achieve the study objectives, a questionnaire was developed by the researcher and distributed online to lecturers working at Jordanian universities. A total of 184 lecturers participated in the study. Based on the findings, the study provides recommendations which will help higher education policy makers, university management teams, and software developers build strategies to ensure the successful implementation and use of e-learning systems during the COVID-19 pandemic.

Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System

2017 ◽  
pp. 1-23 ◽  
Author(s):  
Renuka Mahajan
Keyword(s):  
Data Mining ◽  
Recommender System ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
Comprehensive List ◽  
Research Areas ◽  
New Models ◽  
E Learning ◽  
Research Intensity

This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what' can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what' can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning.

Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type

2019 ◽  
Vol 53 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Aisha Yaquob Alsobhi ◽  
Khaled Hamed Alyoubi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Experience ◽  
Assistive Technologies ◽  
Learning Systems ◽  
Learning System ◽  
Fixed Cost ◽  
Learning Material ◽  
Content Type ◽  
E Learning

PurposeThrough harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type.Design/methodology/approachThe DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases.FindingsAn experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes.Originality/valueThe DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

scholarly journals The research trends in recommender systems for e-learning

2019 ◽  
Vol 14 (1) ◽  
pp. 12-27
Author(s):  
Jiemin Zhong ◽  
Haoran Xie ◽  
Fu Lee Wang
Keyword(s):  
Learning Outcomes ◽  
Recommender Systems ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
Content Type ◽  
Evaluation Indicator ◽  
Recommendation Algorithms ◽  
E Learning ◽  
Key Indicators

Purpose A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems. Design/methodology/approach The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system. Findings The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations. Originality/value The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

Evaluation of an IoT-based e-learning testbed

2017 ◽  
Vol 13 (1) ◽  
pp. 2-13 ◽  
Author(s):  
Masafumi Yamada ◽  
Miralda Cuka ◽  
Yi Liu ◽  
Tetsuya Oda ◽  
Keita Matsuo ◽  
...  
Keyword(s):  
Learning Systems ◽  
Learning System ◽  
Raspberry Pi ◽  
Delivery Ratio ◽  
Hop Count ◽  
Content Type ◽  
Link State ◽  
Constant Bit Rate ◽  
E Learning ◽  
Optimized Link State Routing

Purpose This paper aims to present the design and implementation of an Internet of Things (IoT)-based e-learning testbed using Raspberry Pi mounted on Raspbian operating system (OS). Design/methodology/approach The testbed is composed of five Raspberry Pi B+ computers. The experiments are carried out in the department floor considering an non line of sight (NLoS) environment. Single constant bit rate (CBR) flows were transmitted over user datagram protocol (UDP), and data were collected for five metrics: throughput, packet delivery ratio (PDR), hop count, delay and jitter using the Iperf. Findings The implemented testbed was evaluated using experiments. The experimental results showed that the nodes in the testbed were communicating smoothly, and by using attention value, the learner concentration is increased. Research limitations/implications The performance of the Optimized Link State Routing (OLSR) protocol was analyzed in a floor environment considering the NLoS scenario. However, this testbed can be implemented to other protocols also. Originality/value Because of the opportunities provided by the internet, people are taking advantage of e-learning courses, and enormous research efforts have been dedicated to the development of e-learning systems. To date, many e-learning systems are proposed and used practically. However, in these systems, the e-learning completion rate is low. To deal with this problem, an IoT-based e-learning system was implemented to increase the e-learning completion ratio by increasing the learner concentration.

A comprehensive literature review on the medical learning systems in the developing countries

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahdi Bohlouli ◽  
Omed Hassan Ahmed ◽  
Ali Ehsani ◽  
Marwan Yassin Ghafour ◽  
Hawkar Kamaran Hama ◽  
...  
Keyword(s):  
Developing Countries ◽  
Literature Review ◽  
The Body ◽  
Learning Systems ◽  
Learning System ◽  
Clinical Learning ◽  
Learning Mechanisms ◽  
Content Type ◽  
E Learning ◽  
Medical Learning

PurposeMany people have been dying as a result of medical errors. Offering clinical learning can lead to better medical care. Clinics have conventionally incorporated direct modality to teach personnel. However, they are now starting to take electronic learning (e-learning) mechanisms to facilitate training at work or other suitable places. The objective of this study is to identify and prioritize the medical learning system in developing countries. Therefore, this paper aims at describing a line of research for developing medical learning systems.Design/methodology/approachNowadays, organizations face fast markets' changing, competition strategies, technological innovations and accessibility of medical information. However, the developing world faces a series of health crises that threaten millions of people's lives. Lack of infrastructure and trained, experienced staff are considered essential barriers to scaling up treatment for these diseases. Promoting medical learning systems in developing countries can meet these challenges. This study identifies multiple factors that influence the success of e-learning systems from the literature. The authors have presented a systematic literature review (SLR) up to 2019 on medical learning systems in developing countries. The authors have identified 109 articles and finally selected 17 of them via article choosing procedures.FindingsThe paper has shown that e-learning systems offer significant advantages for the medical sector of developing countries. The authors have found that executive, administrative and technological parameters have substantial effects on implementing e-learning in the medical field. Learning management systems offer a virtual method of augmented and quicker interactions between the learners and teachers and fast efficient instructive procedures, using computer and Internet technologies in learning procedures and presenting several teaching-learning devices.Research limitations/implicationsThe authors have limited the search to Scopus, Google Scholar, Emerald, Science Direct, IEEE, PLoS, BMC and ABI/Inform. Many academic journals probably provide a good picture of the related articles, too. This study has only reviewed the articles extracted based on some keywords such as “medical learning systems,” “medical learning environment” and “developing countries.” Medical learning systems might not have been published with those specific keywords. Also, there is a requirement for more research with the use of other methodologies. Lastly, non-English publications have been removed. There could be more potential related papers published in languages other than English.Practical implicationsThis paper helps physicians and scholars better understand the clinical learning systems in developing countries. Also, the outcomes can aid hospital managers to speed up the implementation of e-learning mechanisms. This research might also enable the authors to have a role in the body of knowledge and experience, so weakening the picture of the developing country's begging bowl is constantly requesting help. The authors hoped that their recommendations aid clinical educators, particularly in developing countries, adopt the trends in clinical education in a changing world.Originality/valueThis paper is of the pioneers systematically reviewing the adoption of medical learning, specifically in developing countries.

Dimensionality and data reduction in telecom churn prediction

Kybernetes ◽  
2014 ◽  
Vol 43 (5) ◽  
pp. 737-749 ◽  
Author(s):  
Wei-Chao Lin ◽  
Chih-Fong Tsai ◽  
Shih-Wen Ke
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Data Reduction ◽  
Data Preprocessing ◽  
Churn Prediction ◽  
Important Data ◽  
Data Set ◽  
Content Type ◽  
Customer Churn

Purpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of this paper, performing these data preprocessing tasks, is to make the mining algorithm produce good quality mining results. Design/methodology/approach – Based on a real telecom customer churn data set, seven different preprocessed data sets based on performing feature selection and data reduction by different priorities are used to train the artificial neural network as the churn prediction model. Findings – The results show that performing data reduction first by self-organizing maps and feature selection second by principal component analysis can allow the prediction model to provide the highest prediction accuracy. In addition, this priority allows the prediction model for more efficient learning since 66 and 62 percent of the original features and data samples are reduced, respectively. Originality/value – The contribution of this paper is to understand the better procedure of performing the two important data preprocessing steps for telecom churn prediction.

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