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In the contemporary society, the ANC (active noise cancellation) technology has become more and more popular. It has grown from its originally military and industry fields into commercial fields, and it is still being implemented into more and more areas and fields. In this paper, we briefly introduce some principal of ANC algorithms and contrast their results with the traditional ones and focus on challenges for future applications.

The purpose of this note is to discuss some aspects of recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in Shah et al. (Nonlinear Dyn. 88(2):839–858, 2017). It is observed that these algorithms do not always converge, whereas they have apparently no advantage over the CLMS and NLMS algorithms whenever they converge. Our claims are based on analytical reasoning and are supported by numerical simulations.

This paper describes a model of a Hierarchical Learning Management System (HLMS) for the insurance industry. The problem is that LMSs are widely used, but not suitable for each educational environment and domain. The existing LMSs are with the general purpose and do not reflect the specific needs of different domains. The proposed LMS is a specific hierarchic system specially created for the insurance industry. The model uses a hierarchic approach to share, organize and present the learning content. It allows for building an LMS specific for the insurance industry which is reliable, efficient, fast, and easy to use by the insurance professionals.

Blind multi-user detection algorithm is an effective method to overcome multi-access interference and influence of near-far effect. This paper briefly describes commonly used algorithms of blind multi-user detection technology, introduces the application of the least mean square algorithm (LMS) and compares the performance with other algorithms. Aiming at the shortcomings of the LMS algorithm, the variable step size LMS algorithm and the relatively low complexity LMK algorithm based on the change of the input signal vector are studied.

Purpose Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale. Design/methodology/approach This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment. Findings Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods. Originality/value The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

This research paper focuses on various gaps associated with the Learning Management System (LMS) and their remedies. LMS is a software application platform upon which multiple tasks related to online tutoring are created. For organizations, it’s crucial that the risks associated with any automated process are kept as low as possible. This should also pertain to selecting the LMS platform for educating new professionals to the organization. To execute this, organizations, should carry out due research before incorporating any system as their primary LMS. Even though, they provide a lot of benefits for organizations integrating such platforms. Choosing faulty LMS for training recruits can lead to a variety of issues later on. Thus, it becomes essential to select the best LMS platform available in the market, and the one suits the organization’s needs. The work proposed in this paper is listing together a number of problems that exists in any given LMS framework and trying to alter them according to the needs of the organization so that they provide a feasible solution and deliver a better guidance to the recruits.

Online learning platforms play an important role in modern education. However, they may not be sufficiently represented in educational institutions. In this regard, the research objectives are set, namely: organize a comparative analysis of several popular distance learning platforms (Moodle, Open edX and NEO LMS) in accordance with the criteria (system features, content support, content creation, user management, reporting system; conduct consultation with university teachers, during which they will highlight the benefits of distance education systems from the point of view of the teacher; and test students, who use the online platform for learning in order to clarify its impact on academic performance. While consulting with 40 teachers of Russian and Chinese universities (Moscow State University, Higher School of Economics, Peking University, Tsinghua University), the following advantages of distance platforms have been highlighted: greater freedom of access, lower education prices, the possibility of dividing the content of the e-course into modules, flexibility of education, the ability to keep up with modern pace of life, and the ability to define criteria for assessing knowledge. According to the results of testing, after working with the Moodle platform, students (300 people in total) with different academic performance have improved their results. The most significant improvement has occurred among students with “unsatisfactory” grades; more than 50% have improved their results. Analysis of distance learning systems, testing of students and consultation with teachers allow saying that such platforms help to make education more accessible and convenient. Besides, information technologies are introduced in the educational process. That is why it is important to implement such platforms in higher education.

In order to develop an e-learning system as a method of education that frees both the teacher and learner from the constraints of time and space, it is necessary to develop software and to build the network equipment required to operate the software. The most basic system consists of a web server, a database server, and a video server. However, these elements are vulnerable to both internal and external threats. As for the web, database, and video servers, it is possible to respond to such threats by operating more than two devices, but this inevitably increases the cost of building the equipment. Therefore, this study proposed the use of a cloud service, such as AWS (Amazon Web Service), in order to save on the costs of purchasing, installing, and operating the servers, as well as a service designed to strengthen security by employees or trainees who understand the internal situation of the training institute. In other words, this study proposed the development of an efficient Learning Management System (LMS) platform and proved its efficiency using a test bed over a period of three years. The major contribution of this study is that the design of the proposed LMS has been improved to provide a more efficient performance than the existing LMSs by surmounting the traffic overload problem often found in video services. This is achieved by utilizing a lesser number of servers and maintaining the balance of the loads. Also, the interface used for the system can be adaptable to most of the web servers as they support Java, Android, and HTML-based systems. As a cloud-based LMS, this system has been tested for its efficiency and effectiveness for a period of three years during which the results have been satisfactory.

Most organizational researchers understand the detrimental effects of measurement errors in testing relationships among latent variables and hence adopt structural equation modeling (SEM) to control for measurement errors. Nonetheless, many of them revert to regression-based approaches, such as moderated multiple regression (MMR), when testing for moderating and other nonlinear effects. The predominance of MMR is likely due to the limited evidence showing the superiority of latent interaction approaches over regression-based approaches combined with the previous complicated procedures for testing latent interactions. In this teaching note, we first briefly explain the latent moderated structural equations (LMS) approach, which estimates latent interaction effects while controlling for measurement errors. Then we explain the reliability-corrected single-indicator LMS (RCSLMS) approach to testing latent interactions with summated scales and correcting for measurement errors, yielding results which approximate those from LMS. Next, we report simulation results illustrating that LMS and RCSLMS outperform MMR in terms of accuracy of point estimates and confidence intervals for interaction effects under various conditions. Then, we show how LMS and RCSLMS can be implemented with Mplus, providing an example-based tutorial to demonstrate a 4-step procedure for testing a range of latent interactions, as well as the decisions at each step. Finally, we conclude with answers to some frequently asked questions when testing latent interactions. As supplementary files to support researchers, we provide a narrated PowerPoint presentation, all Mplus syntax and output files, data sets for numerical examples, and Excel files for conducting the loglikelihood values difference test and plotting the latent interaction effects.

The use of Learning Management System (LMS) in academic institutions is becoming an imperative for many institutions. The success of LMS in academic institutions may be initiated by instructors’ adoption; however, LMS survives in the long run by learners’ continuous adoption and use. Consequently, the objective of this article is to examine the critical factors that influence the success of LMS in blended learning in terms of actual usage, perceived usefulness, perceived ease of use, and user satisfaction from the learners’ perspective. The study also examines how these success measures impact learners’ continuous intention to use LMS in blended learning. These critical factors are related to the major entities of LMS adoption: learner characteristics (computer anxiety, technology experience, self-efficacy, and personal innovativeness), instructor characteristics (attitude, teaching style, control, and responsiveness), LMS characteristics (system quality, information quality, and service quality), classmates characteristics (attitude and interaction), course characteristics (quality and flexibility), and organization characteristics (management support and training). Based on 512 learners, the results showed that all of these factors are critical to one or several success measures, except for learner self-efficacy, instructor online responsiveness, and management support. The results also showed that all success measures are critical to learners’ continuous intention to use LMS in blended learning.