Search results

This exploratory research project investigated how mainland Chinese business students studying overseas conceptualize and describe the learning effectiveness of ten different instructional techniques commonly encountered in their business courses. A large numbers of mainland Chinese students enroll in business courses in private international institutions in Singapore – dislocated from their home cultures – but needing to adapt to Western learning curricula and ultimately to acquire proficiency in Western business practices. Certain instructional techniques are likely to bridge the cultural gap better than others. The paper aims to discuss these issues.

Twenty consenting students selected from 400+ geographically diverse Chinese students participating in a larger study provided face-to-face interview information on how different instructional techniques stimulated different aspects of content acquisition, learner group dynamics, decision-making, learning efficiency, comfort, flexibility, familiarity, and applicability.

Interviewees' free-form descriptions of “learning effectiveness” included phrases such as “quality of learning”, “control over my learning”, “scope of knowledge”, “efficiency of learning”, “gaining/acquiring knowledge”, “understanding theories”, “flexibility in time and place”, “applicability of new information”, “attractive learning environment”, “[absence of] ambiguity and uncertainty”, “security and ease of mind”, etc. Their 340 descriptors were classified into 30 qualitative indicator categories, four of them common to many instructional techniques and ten more specific to individual techniques.

Although Chinese mainland students generally prefer rote-learning styles of instructional techniques due to their prolonged exposure to it, rote-style techniques may not always be the preferred choices for learning effectiveness and adaptation to new culture norms and practices. This paper reports qualitative “consultations” with learners in new cultures and argues for holistic and engaged approaches to learning effectiveness for students dislocated from their home cultures while providing a starting-point for further research in mainland Chinese students' Western-based business education in Singapore and elsewhere.

The purpose of this paper is to investigate a new trend of training mainland Chinese students in Western‐style business education in Singapore. The paper examines the influence of the inferred learning effectiveness and cultural dislocation variables when measured across ten commonly used instructional techniques.

The use of consensual qualitative research allowed the data to be qualitatively analysed. The random selection of 20 participants represents mainland Chinese students, from the northern, southern, eastern and western regions. The study reports the level of knowledge acquisition, the relationship between comfort and knowledge acquired and the differences between the active and passive instructional techniques on students' learning effectiveness.

Rote‐learning styles of instructional techniques may not be the Chinese students' only preferred choice in terms of acquisition of knowledge and how they learn most effectively.

The present exploratory study provides a starting‐point for further research into understanding how to teach Western‐based business education to mainland Chinese students in Singapore.

The findings will give institutions conducting Western‐based education programs in Singapore an advantage in providing effective learning pedagogies, and will assist in increasing their quality, which will enable them to nurture well‐qualified business professionals.

The quality of the educational standard and its compatibility with the Asian client base are further enhanced both in terms of contents' intensity and educational services provided to students.

The paper offers practical help from the perspective of the curriculum design and development of an effective business educational framework to sustain profitability by offering tailor‐made, superior quality course programs.

Incorporating student voice to improve both academic and institutional performances is the contemporary innovative way to enhance and ensure quality in higher education. Higher education organizations are developing a culture and an encouraging environment for the students where they can express their opinions and be an integral part and partner of educational improvement process. The purpose of this paper is to explore students preferred learning and teaching methods for management education, to study current intended learning outcome and practiced teaching methods, to investigate prerequisites to implement students expected teaching methods in the college-level management education of National University.

Nature of the study is exploratory and descriptive as well. Primary data were collected using focus group discussions, surveys conducted using structured and closed-ended questions and in-depth, face-to-face interviews employed to collect data from academic staff.

The key findings include the need for bringing changes in teaching techniques at college-level management education. Furthermore, the study has explored challenging issues which can hinder changes in teaching techniques.

The study pointed to the need of including student voice to keep improving teaching techniques that can satisfy students' learning needs continuously.

The study adds the body of knowledge on incorporating student voice to improve the quality of higher education teaching techniques and in other services as well in Bangladesh.

The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper penalty function, regularization laws are embedded into the structure of common least square solutions to increase the numerical stability, sparsity, accuracy and robustness of regression weights. Several regularization techniques have been proposed so far which have their own advantages and disadvantages. Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques. However, the proposed numerical and deterministic approaches need certain knowledge of mathematical programming, and also do not guarantee the global optimality of the obtained solution. In this research, the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine (ELM).

To implement the required tools for comparative numerical study, three steps are taken. The considered algorithms contain both classical and swarm and evolutionary approaches. For the classical regularization techniques, Lasso regularization, Tikhonov regularization, cascade Lasso-Tikhonov regularization, and elastic net are considered. For swarm and evolutionary-based regularization, an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered, and its algorithmic structure is modified so that it can efficiently perform the regularized learning. Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme. To test the efficacy of the proposed constraint evolutionary-based regularization technique, a wide range of regression problems are used. Besides, the proposed framework is applied to a real-life identification problem, i.e. identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine, for further assurance on the performance of the proposed scheme.

Through extensive numerical study, it is observed that the proposed scheme can be easily used for regularized machine learning. It is indicated that by defining a proper objective function and considering an appropriate penalty function, near global optimum values of regressors can be easily obtained. The results attest the high potentials of swarm and evolutionary techniques for fast, accurate and robust regularized machine learning.

The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine (OP-ELM). The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system, and also increases the degree of the automation of OP-ELM. Besides, by using different types of metaheuristics, it is demonstrated that the proposed methodology is a general flexible scheme, and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

The purpose of this paper is to report the perceptions of students taking the Master of Project Management Programme at the University of Botswana regarding their transformative experience called “prayer”. The term “prayer” was coined because of it being the first learning activity of the lecture; and at a conceptual level, to convey reverence towards the gift of learning. “Prayer” as a learning and teaching technique involves each student identifying material containing project management concepts or issues which they present to a class of peers using any appropriate means followed by discussion and peer assessment. The material presented may be an article from a newspaper or magazine. It may be a personal documented story or a story told around a picture, artefact, poster or video relating to a project management issue.

Students’ perceptions were obtained by means of a self-administered questionnaire containing open-ended questions. Content analysis was used to analyse the responses.

The results of the study indicated that “prayer” provided students ingredients of transformative learning. It also proved to be a worthwhile technique for inculcating some of the graduate attributes articulated by this university and for incorporating adult learning principles.

The technique can be used to compliment traditional techniques in teaching and learning in project management training. The limitations of the results are due to the self-reporting nature of the approach and the fact that the technique has been tried on one group.

There is a possibility that the technique can be extended to other disciplines such as business administration where students examine cases in the public domain to illustrate concepts learnt in class.

The originality lies in its packaging of a technique the think is worth sharing among project management educators. This is because the learning activity described engages students simultaneously in research, review, presentation, and communication as well as reflection, collaborative discourse and self and peer assessment.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

The after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.

We propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.

We found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.

This approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.

This approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.

Sustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.

This is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

Data mining is widely considered necessary in many business applications for effective decision-making. The importance of business data mining is reflected by the existence of numerous surveys in the literature focusing on the investigation of related works using data mining techniques for solving specific business problems. The purpose of this paper is to answer the following question: What are the widely used data mining techniques in business applications?

The aim of this paper is to examine related surveys in the literature and thus to identify the frequently applied data mining techniques. To ensure the recent relevance and quality of the conclusions, the criterion for selecting related studies are that the works be published in reputed journals within the past 10 years.

There are 33 different data mining techniques employed in eight different application areas. Most of them are supervised learning techniques and the application area where such techniques are most often seen is bankruptcy prediction, followed by the areas of customer relationship management, fraud detection, intrusion detection and recommender systems. Furthermore, the widely used ten data mining techniques for business applications are the decision tree (including C4.5 decision tree and classification and regression tree), genetic algorithm, k-nearest neighbor, multilayer perceptron neural network, naïve Bayes and support vector machine as the supervised learning techniques and association rule, expectation maximization and k-means as the unsupervised learning techniques.

The originality of this paper is to survey the recent 10 years of related survey and review articles about data mining in business applications to identify the most popular techniques.

The popular total quality management (TQM) approach has tended to focus on internal processes, rather than external issues such as competitiveness and market appeal, and is more reactive and adaptive than anticipative. The time has come to go beyond TQM and to understand the nature and application of organizational learning. Learning organizations envision change, are committed to generating and transferring new knowledge and innovation, and have learned how to learn. TQM may be embedded in the learning organization, but TQM is but the first step or wave in transforming and creating organizations which continuously expand their abilities to change and shape their futures. This article first defines and identifies the characteristics of a learning organization, then explores some techniques to develop and transform an organization into a learning organization, and finally suggests some traditional and newer techniques, such as data envelopment analysis (DEA), as ways to measure and evaluate organizational learning.