Showing papers in "International Journal of Web-based Learning and Teaching Technologies in 2020"
TL;DR: A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work, which is a hybrid algorithm based on text clustering, text classification, frequent-term calculation, and taxonomic term-mapping technique.
Abstract: A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work. ML-EC2 is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping technique. It is an example of classification using text clustering technique. It studies the problem where each email cluster represents a single class label while it is associated with set of cluster labels. It is multi-label text-clustering-based classification algorithm in which an email cluster can be mapped to more than one email category when cluster label matches with more than one category term. The algorithm will be helpful when there is a vague idea of topic. The performance parameters Entropy and Davies-Bouldin Index are used to evaluate the designed algorithm.
14 citations
TL;DR: This paper would experiment by forecasting the demand using multiple linear regression (EMLR-DF) for different food commodities and implements the model to assists the farmers in demand based constructive farming and results have proved the proposed EMLR-DF is more effective and accurate.
Abstract: Demand planning plays a very strategic role in improving the performance of every business, as the planning for a whole lot of other activities depends on the accuracy and validity of this exercise. The field of agriculture is not an exception; demand forecasting plays an important role in this area also, where a farmer can plan for the crop production according to the demand in future. Hence, a system which could forecasts the demand for day-to-day food harvests and assists the farmers in planning the crop production accordingly may lead to beneficial farming business. This paper would experiment by forecasting the demand using multiple linear regression (EMLR-DF) for different food commodities and implements the model to assists the farmers in demand based constructive farming. Implementation results have proved the effectiveness of the proposed system in educating the farmers in producing the yields mapping to the demand. Implementation and comparison results have proved the proposed EMLR-DF is more effective and accurate.
10 citations
TL;DR: In this article, the authors compared the perceptions of Spanish pre-service and in-service EFL teachers about ICT, e-learning and MOOCs, and the uses of these technologies.
Abstract: Despite its numerous advantages, globalization, information society and the emergence of information and communication technologies (ICT) have caused quick changes in society, provoking the rapid obsolescence of knowledge and the appearance of new concepts. In this context, new professional demands for teachers are required to help students develop necessary competences for the 21st century. At this juncture, ICT, e-learning tools, and MOOCs have arisen as remarkable training resources for teacher lifelong learning with an undeniable potential; however, it seems relevant to study whether teachers agree in the usefulness of such tools. In this light, this article investigates and compares the perceptions of Spanish pre-service and in-service EFL teachers about ICT, e-learning and MOOCs, and the uses of these technologies. The results obtained allow the researchers to analyze whether the possibilities of these resources correspond to the real necessities of EFL teachers.
8 citations
TL;DR: The value of utilizing alternative assessment approaches in web-based learning environments as means of improving student performance, particularly when designing educational products, may have theoretical and pedagogical implications for learners and teachers.
Abstract: The aim of the present study is to identify the impact of the alternative web-based self and peer assessment approaches on improving the quality of student educational projects. In this context, a study was carried out during the second semester of the 2017-2018 academic year among 48 postgraduate students at King Faisal University. Results indicated that both self and peer-assessment approaches are effective when assessing the quality of educational products. The results also showed that the extent of student experience with the self-assessment approach affects their assessment credibility and objectivity. This study emphasized the value of utilizing alternative assessment approaches in web-based learning environments as means of improving student performance, particularly when designing educational products. It may have theoretical and pedagogical implications for learners and teachers.
5 citations
TL;DR: This paper illustrates the cloud-based telemonitoring framework that implements healthcare automation system for myocardial infarction (MI) disease classification that reduces both data storage space and transmission bandwidth which facilitates accessibility to quality care in much reduced cost.
Abstract: This paper illustrates the cloud-based telemonitoring framework that implements healthcare automation system for myocardial infarction (MI) disease classification. For this purpose, the pathological feature of ECG signal such as elevated ST segment, inverted T wave, and pathological Q wave are extracted, and MI disease is detected by the rule-based rough set classifier. The information system involves pathological feature as an attribute and decision class. The degree of attributes dependency finds a smaller set of attributes and predicted the comprehensive decision rules. For MI decision, the ECG signal is shared with the respective cardiologist who analyses and prescribes the required medication to the first-aid professional through the cloud. The first-aid professional is notified accordingly to attend the patient immediately. To avoid the identity crisis, ECG signal is being watermarked and uploaded to the cloud in a compressed form. The proposed system reduces both data storage space and transmission bandwidth which facilitates accessibility to quality care in much reduced cost.
4 citations
TL;DR: A novel key-based blind method for RGB image steganography where multiple images can be hidden simultaneously is described which provides enhanced security as well as improve the quality of the stego.
Abstract: Steganography is a widely-used technique for digital data hiding. Image steganography is the most popular among all other kinds of steganography. In this article, a novel key-based blind method for RGB image steganography where multiple images can be hidden simultaneously is described. The proposed method is based on Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) which provides enhanced security as well as improve the quality of the stego. Here, the cover image has been taken as RGB although the method can be implemented on grayscale images as well. The fundamental concept of visual cryptography has been utilized here in order to increase the capacity to a great extent. To make the method more robust and imperceptible, pseudo-random number sequence and a correlation coefficient have been used for embedding and the extraction of the secrets, respectively. The robustness of the method is tested against steganalysis attacks such as crop, rotate, resize, noise addition, and histogram equalization. The method has been applied on multiple sets of images and the quality of the resultant images have been analyzed through various matrices namely ‘Peak Signal to Noise Ratio,' ‘Structural Similarity index,' ‘Structural Content,' and ‘Maximum Difference.' The results obtained are very promising and have been compared with existing methods to prove its efficiency.
4 citations
TL;DR: The authors' goal is to handle big social data effectively using cost-effective tools for fetching as well as querying unstructured data and algorithms for analysing scalable, uninterrupted data streams with finite memory and resources.
Abstract: Social media data (SMD) is driven by statistical and analytical technologies to obtain information for various decisions. SMD is vast and evolutionary in nature which makes traditional data warehouses ill suited. The research aims to propose and implement novel framework that analyze tweets data from online social networking site (OSN; i.e., Twitter). The authors fetch streaming tweets from Twitter API using Apache Flume to detect clusters of users having similar sentiment. Proposed approach utilizes scalable and fault tolerant system (i.e., Hadoop) that typically harness HDFS for data storage and map-reduce paradigm for data processing. Apache Hive is used to work on top of Hadoop for querying data. The experiments are performed to test the scalability of proposed framework by examining various sizes of data. The authors' goal is to handle big social data effectively using cost-effective tools for fetching as well as querying unstructured data and algorithms for analysing scalable, uninterrupted data streams with finite memory and resources.
4 citations
TL;DR: This research has suggested that the proposed Proposed Algorithm could be applied to different internet service providers and could be useful for improving quality and efficiency.
Abstract: The web is the largest world-wide communication system of computers. The web has local, academic, commercial and government sites. As the types of websites increases in numbers, the cost and accuracy of manual classification became cumbersome and cannot satisfy the increasing internet service demands, thereby automated classification became important for better and more accurate search engine results. Therefore, this research has proposed an algorithm for classifying different websites automatically by using randomly collected textual data from the webpages. This research also contributed ten dictionaries covering different domains and used as training data in the classification process. Finally, the classification was carried out using the proposed and Naïve Bayes algorithms and found the proposed algorithm outperformed on the scale of accuracy by 1.25%. This research suggests that the proposed algorithm could be applied to any number of domains if the related dictionaries are available. KeyWoRDS Classification, Dictionary, Dynamically, Feature, Matching, Text, Website
4 citations
TL;DR: The article presents international and local experience, relevant post-school education and training policies and key variables and themes that impact on e-learning, as well as proposals and recommendations from research into e- learning in the retail sector.
Abstract: E-learning is of increasing importance in delivering flexible and distributed programmes for workforce skill development such as induction, product knowledge, systems compliance, and customer service. This research consists of a desktop exploratory review of e-learning concepts, policies, surveys, and a set of proposals and recommendations from research into e-learning in the retail sector. The article presents international and local experience, relevant post-school education and training policies and key variables and themes that impact on e-learning. Institutional approaches in supporting e-learning within different countries are also contrasted. The outcomes are general recommendations regarding the focus, alignment and integration of e-learning for the retail sector, with activities proposed to support e-learning.
4 citations
TL;DR: This research takes up an adaptive resource ranking approach, and improves the effectiveness of NDFS algorithm by scheduling jobs in those ranked resources, thereby increasing the number of job deadlines met and service quality agreements met.
Abstract: Virtual resources team up to create a computational grid, which is used in computation-intensive problem solving. A majority of these problems require high performance resources to compute and generate results, making grid computation another type of high performance computing. The optimization in computational grids relates to resource utilization which in turn is achieved by the proper distribution of loads among participating resources. This research takes up an adaptive resource ranking approach, and improves the effectiveness of NDFS algorithm by scheduling jobs in those ranked resources, thereby increasing the number of job deadlines met and service quality agreements met. Moreover, resource failure is taken care of by introducing a partial backup approach. The benchmark codes of Fast Fourier Transform and Matrix Multiplication are executed in a real test bed of a computational grid, set up by Globus Toolkit 5.2 for the justification of propositions made in this article.
3 citations
TL;DR: The study found no significant difference between the influence of web-based self and peer-assessment approaches on academic self-efficacy in terms of the two ASEAWA factors investigated: Academic Achievement and Academic Development.
Abstract: The aim of the present study was to identify the effect of web-based self and peer-assessment approaches on improving pre-service student teachers’ academic self-efficacy. In this context, a study was carried out during the second semester of the 2018 academic year among 48 pre-service student teachers enrolled in the Teacher Preparation Program, at the College of Education at King Faisal University. The Academic Self-Efficacy of Alternative Web-Based Assessment survey questionnaire (ASEAWA) was used for the purpose of this study. The results highlighted the value of utilizing self and peer-assessment approaches to enhance pre-service student teacher academic self-efficacy. The study found no significant difference between the influence of web-based self and peer-assessment approaches on academic self-efficacy in terms of the two ASEAWA factors investigated: Academic Achievement and Academic Development. This study has several implications for designers and developers of teacher preparation programs as well as for further research in the field. KEyWoRdS Academic Self-Efficacy, Alternative Assessment, Peer-Assessment, Pre-Service Teachers, Self-Assessment, Web-Based Learning
TL;DR: In this article, a theoretical model of Self-Regulated Learning (SRL) in Adaptive Learning Environments (ALE) and the related questionnaire as a measurement tool was designed and empirically evaluated.
Abstract: Adaptive Learning (AL), a new web-based online learning environment, requires self-regulated learners who act autonomously. However, to date, there appears to be no existing model to conceptualize different aspects of SRL skills in Adaptive Learning Environments (ALE). The purpose of this study was to design and empirically evaluate a theoretical model of Self-Regulated Learning (SRL) in ALE's and the related questionnaire as a measurement tool. The proposed theoretical model, namely, “Adaptive Self-Regulated Learning (ASR)”, was specified to incorporate the SRL skills into ALE's. Based on this model, the Adaptive Self-regulated Learning Questionnaire (ASRQ) was developed. The reliability and validity of the ASRQ were evaluated via the review of a content expert panel, the Cronbach's alpha coefficients, and confirmatory factor analysis. Overall, the results supported the theoretical framework and the new ASRQ in an ALE. In this article, the theoretical and practical implications of the findings were discussed.
TL;DR: This article uses Deming's PlanDoCheckAct (PDCA) cycle for longitudinal assessment and improvement of the AIS course and suggests ways to engage students and to train them for future challenges.
Abstract: The boundaries between accounting and technology is becoming fuzzier as accounting companies are becoming consulting companies. Digital economies are changing business models and companies that do not adept can become obsolete very fast. Even professional organizations are recommending using technology to modernize, automate and expedite accounting discipline. Therefore, it is necessary to train personnel to become competent in both technology and accounting. Universities are fulfilling this requirement by offering courses such as Accounting Information Systems, data analytics, big data, etc. This article uses Deming's PlanDoCheckAct (PDCA) cycle for longitudinal assessment and improvement of the AIS course. Instead of re-inventing the wheel, instructors can learn from our experience. This article would be useful for instructors trying different and emerging approaches. In addition, this article would be useful for instructors trying to engage students and to train them for future challenges.
TL;DR: This article makes an attempt to synthesize the different types of ways of learning; the self- determined learning strategies along with the prevailing theories of learning styles hypothesis.
Abstract: This article describes how every learner is a unique creative individual responsible for paving his/her own way of learning in a preclusion of external restraints. Learners apply a bunch of idiosyncratic means to segue the information into knowledge. The various implications of such manipulated formulation by the learners implies strategic responses to new information and indicates a rational commitment to learn in many different ways. Pertaining to this we have also different versions of learning styles and strategies and their categories. The growing innovative and multiple dynamic ways of learning here bring diffidence to the existence of those stipulated types of learning styles and strategic traditions. This article makes an attempt to synthesize the different types of ways of learning; the self- determined learning strategies along with the prevailing theories of learning styles hypothesis.
TL;DR: This quantitative study examined the frequency of usage and teacher perception of educational technology by K - 12 public school teachers in three geographic settings: urban, rural, and suburban to better understand trends in the professional environment.
Abstract: This quantitative study examined the frequency of usage and teacher perception of educational technology by K - 12 public school teachers in three geographic settings: urban, rural, and suburban. The objective aimed to uncover any significant relationship between variables in an effort to better understand trends in the professional environment. A survey of 2,200 educators in a Mid-Atlantic state revealed significant differences of perception and usage. The inquiry discovered teachers from urban schools trailed suburban and rural schools in nearly all objectives. Suburban schools reported the highest perception levels of technology effectiveness, trailed consistently by their rural peers. Current teachers, administrators, and teacher educators may utilize this research to personalize technologies for their student population and develop strategies to increase teacher perception of technology, particularly in the urban setting.
TL;DR: In this article, the flipped classroom (FC) method changed the teaching practices and encouraged active learning, using pre-class videos made the class time available for active discussions, and the strongest predictor of perceived enjoyment was perceived enjoyment and the weakest predictor was relative advantage.
Abstract: Information technology and the Internet has enabled faculty and educational institutions to implement new teaching methods to enrich the educational environment. The flipped classroom (FC) method changed the teaching practices and encouraged active learning. Using pre-class videos made the class time available for active discussions. This study utilized two samples to compare student perceptions on the challenges and benefits of such a method. The first sample included 200 students from a leading university in one of the Gulf Region countries, and the second sample included 114 students in Jordan. Results indicated a domination of higher means for the Jordanian sample, and the for males sample. Results partially supported the model for the Gulf university students and overall sample, but fully for the Jordanian sample. The strongest predictor of FC was perceived enjoyment and the weakest predictor was relative advantage. More results and discussion are reported at the end.
TL;DR: A hierarchical scheme is proposed to classify string instruments without using MFCC-based features, and a neural network, k-NN, Naive Bayes' and Support Vector Machine have been used to classify.
Abstract: Automatic recognition of instrument types from an audio signal is a challenging and a promising research topic. It is challenging as there has been work performed in this domain and because of its applications in the music industry. Different broad categories of instruments like strings, woodwinds, etc., have already been identified. Very few works have been done for the sub-categorization of different categories of instruments. Mel Frequency Cepstral Coefficients (MFCC) is a frequently used acoustic feature. In this work, a hierarchical scheme is proposed to classify string instruments without using MFCC-based features. Chroma reflects the strength of notes in a Western 12-note scale. Chroma-based features are able to differentiate from the different broad categories of string instruments in the first level. The identity of an instrument can be traced through the sound envelope produced by a note which bears a certain pitch. Pitch-based features have been considered to further sub-classify string instruments in the second level. To classify, a neural network, k-NN, Naive Bayes' and Support Vector Machine have been used.
TL;DR: This research provides web-based learning about web service quality which will be utilized for prediction, recommendation and the selection of trusted web services in the pool of web services available globally.
Abstract: In secure web application development, the role of web services will not continue if it is not trustworthy. Retaining customers with applications is one of the major challenges if the services are not reliable and trustworthy. This article proposes a trust evaluation and decision model where the authors have defined indirect attribute, trust, calculated based on available direct attributes in quality web service (QWS) data sets. After getting training of such evaluation and decision strategies, developers and customers, both will use the knowledge and improve the QoS. This research provides web-based learning about web service quality which will be utilized for prediction, recommendation and the selection of trusted web services in the pool of web services available globally. In this research, the authors include designs to make decisions about the trustworthy web services based on classification, correlation, and curve fitting to improve trust in web service prediction. In order to empower the web services life cycle, they have developed a quality assessment model to incorporate a security and performance policy.