An investigation of mobile learning readiness in higher education based on the theory of planned behavior

This study investigated the current state of college students' perceptions toward mobile learning in higher education. Mobile learning is a new form of learning utilizing the unique capabilities of mobile devices. Although mobile devices are ubiquitous on college campuses, student readiness for mobile learning has yet to be fully explored in the United States. The paper describes a conceptual model, based on the theory of planned behavior (TPB), which explains how college students' beliefs influence their intention to adopt mobile devices in their coursework. Structural equation modeling was used to analyze self-report data from 177 college students. The findings showed that the TPB explained college students' acceptance of m-learning reasonably well. More specifically, attitude, subjective norm, and behavioral control positively influenced their intention to adopt mobile learning. The results provide valuable implications for ways to increase college students' acceptance of mobile learning.     

Mobile and ubiquitous learning in higher education settings. A systematic review of empirical studies

Mobile and ubiquitous learning are increasingly attracting academic and public interest, especially in relation to their application in higher education settings.The systematic analysis of 36 empirical papers supports the view that knowledge gains from instructionist learning designs are facilitated by distributed and more frequent learning activities enabled by push mechanisms. They also lend themselves to the activation of learners during classroom lectures. In addition, and as a particular advantage of mobile technology, “hybrid” designs, where learners create multimodal representations outside the classroom and then discussed their substantiated experiences with peers and educators, helped to connect learning in formal and more informal and personalized learning environments.Generally, empirical evidence that would favour the broad application of mobile and ubiquitous learning in higher education settings is limited and because mobile learning projects predominantly take instructionist approaches, they are non-transformatory in nature. However, by harnessing the increasing access to digital mobile media, a number of unprecedented educational affordances can be operationalised to enrich and extend more traditional forms of higher education.     

An implementation of remote laboratory for secondary science education

This paper reports our classroom implementation of a new remote laboratory (RL) system, which was developed by using innovative ideas and methods for applying technology‐enhanced learning to secondary school science education (Grades 7–9 or Ages 12–14). The newly developed RL system, which involves 8 remote experiments, was tested with 32 secondary school students from a local public school in order to evaluate its usability, learning, and their perception. The present study was carried out by using a mixed research method, including a questionnaire survey (open‐ended questions) and interviews. The corresponding research tools were specifically developed to collect data on students' perceptions and the implementation issues of the RL system. The survey results revealed that there was no major refinement required for the RL system, which was good enough for further adoption in secondary science education because its methods used to conduct online experiments could (a) extend/enhance the existing practices (with virtual/simulation experiments only) of e‐learning and (b) largely induce students' favourable views and perceptions in their own learning. Besides, negative comments and suggestions for improvement were purposely collected during a follow‐up period with an aim to pinpoint any ways in which the RL system and its design could be refined. They turned out to be very minor and easily fixed, and so the refined RL system is educationally suitable for use in laboratory activities and demonstrations to enhance the learning and teaching of science within and outside the secondary school environment.     

Competence-based recommender systems: a systematic literature review

ABSTRACT

Competence-based learning is increasingly widespread in many institutions since it provides flexibility, facilitates the self-learning and brings the academic and professional worlds closer together. Thus, the competence-based recommender systems emerged taking the advantages of competences to offer suggestions (performance of a learning experience, assistance of an expert or recommendation of a learning resource) to the user (learner or instructor). The objective of this work is to conduct a new Systematic Literature Review (SLR) concerning competence-based recommender systems to analyse in relation to their nature and assessment of competences an others key factors that provide more flexible and exhaustive recommendations. To do so, a SLR research methodology was followed in which 25 competence-based recommender systems related to learning or instruction environments were classified according to multiple criteria. We evaluate the role of competences in these proposals and enumerate the emerging challenges. Also a critical analysis of current proposals is carried out to determine their strengths and weakness. Finally, future research paths to be explored are grouped around two main axes closely interlinked; first about the typical challenges related to recommender systems and second, concerning ambitious emerging challenges.     

Empirical evidence about the UML: a systematic literature review

The Unified Modeling Language (UML) was created on the basis of expert opinion and has now become accepted as the ‘standard’ object‐oriented modelling notation. Our objectives were to determine how widely the notations of the UML, and their usefulness, have been studied empirically, and to identify which aspects of it have been studied in most detail. We undertook a mapping study of the literature to identify relevant empirical studies and to classify them in terms of the aspects of the UML that they studied. We then conducted a systematic literature review, covering empirical studies published up to the end of 2008, based on the main categories identified. We identified 49 relevant publications, and report the aggregated results for those categories for which we had enough papers— metrics, comprehension, model quality, methods and tools and adoption. Despite indications that a number of problems exist with UML models, researchers tend to use the UML as a ‘given’ and seem reluctant to ask questions that might help to make it more effective. Copyright © 2010 John Wiley & Sons, Ltd.     

A systematic review of machine learning techniques for software fault prediction

BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults.MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized.ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module/class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models.ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.     

An examination of the impact of learning objects in secondary school

Very few studies have systematically evaluated the effect of learning objects in secondary school classrooms. The vast majority of studies have focussed on higher education. The current study examined the impact of learning objects from the perspective of 850 students and 27 teachers (50 classrooms) of science, mathematics, or social science. The results suggest that teachers typically spend 1 to 2 h finding and preparing for learning object based lesson plans that focus on the review of previous concepts. Both teachers and students are positive about the learning benefits, quality, and engagement value of learning objects, although teachers are more positive than students. Student performance increased significantly – almost 30% – when learning objects were used in conjunction with a variety of teaching strategies. It is reasonable to conclude that learning objects are a viable teaching tool in a secondary school environment.     

Applications of machine learning to BIM: A systematic literature review

As Building Information Modeling (BIM) workflows are becoming very relevant for the different stages of the project’s lifecycle, more data is produced and managed across it. The information and data accumulated in BIM-based projects present an opportunity for analysis and extraction of project knowledge from the inception to the operation phase. In other industries, Machine Learning (ML) has been demonstrated to be an effective approach to automate processes and extract useful insights from different types and sources of data. The rapid development of ML applications, the growing generation of BIM-related data in projects, and the different needs for use of this data present serious challenges to adopt and effectively apply ML techniques to BIM-based projects in the Architecture, Engineering, Construction and Operations (AECO) industry. While research on the use of BIM data through ML has increased in the past decade, it is still in a nascent stage. In order to asses where the industry stands today, this paper carries out a systematic literature review (SLR) identifying and summarizing common emerging areas of application and utilization of ML within the context of BIM-generated data. Moreover, the paper identifies research gaps and trends. Based on the observed limitations, prominent future research directions are suggested, focusing on information architecture and data, applications scalability, and human information interactions.     

A systematic review of empirical research on learning with 3D printing technology

Although 3D printing (3DP) technology has become an increasingly popular educational tool in recent years, very little is known about the learning benefits of this technology. This systematic literature review synthesized empirical research on learning with 3DP in various educational settings, focusing on publication and study participants' characteristics, curriculum areas, research methodologies, instructional approaches, educational outcomes and benefits. A comprehensive survey of published and unpublished studies identified 78 empirical studies that met the inclusion criteria. In addition to positive effects on learning, 3DP facilitated innovative curriculum development and created opportunities for cross-disciplinary research. The findings revealed five major trends in 3DP learning: (1) prepare a new generation of engineers, (2) democratize additive manufacturing technology and production, (3) support learning using low-cost 3D printed learning aids, (4) produce assistive technologies, and (5) promote creativity and innovation. The review identifies critical gaps in the literature and offers suggestions for future research.     

A systematic survey of deep learning in breast cancer

In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers. Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.     

Virtual reality in K-12 and higher education: A systematic review of the literature from 2000 to 2019

This study is a systematic review of 20 years of research on the usage of virtual reality (VR) in K-12 and higher education settings, which aims to consolidate, evaluate, and communicate evidence that can inform both the theory and practice of VR-based instruction. A total of 149 articles were selected from three major academic databases using search strings and manual screening protocols. The literature analysis emphasized four interrelated aspects of VR-based instruction: instructional context, instructional design, technological affordances, and research findings. The results revealed evolving trends in the VR literature in terms of publication patterns, pedagogical assumptions, equipment usage, and research methodologies, as well as the contextual factors behind VR adoption in education. Additionally, a meta-analysis was conducted to examine the efficacy of VR-based instruction, with results indicating an overall medium effect and several moderating factors. Finally, practical implications and a future research agenda for VR-based instruction are discussed.     

Motivating the learning of science topics in secondary school: A constructivist edutainment setting for studying Chaos

In this paper, we present an Edutainment (education plus entertainment) secondary school setting based on the construction of artifacts and manipulation of virtual contents (images, sound, and music) connected to Chaos.     

A systematic literature review on the use of machine learning in code clone research

Context:Research related to code clones includes detection of clones in software systems, analysis, visualization and management of clones. Detection of semantic clones and management of clones have attracted use of machine learning techniques in code clone related research.Objective:The aim of this study is to report the extent of machine learning usage in code clone related research areas.Method:The paper uses a systematic review method to report the use of machine learning in research related to code clones. The study considers a comprehensive set of 57 articles published in leading conferences, workshops and journals.Results:Code clone related research using machine learning techniques is classified into different categories. Machine learning and deep learning algorithms used in the code clone research are reported. The datasets, features used to train machine learning models and metrics used to evaluate machine learning algorithms are reported. The comparative results of various machine learning algorithms presented in primary studies are reported.Conclusion:The research will help to identify the status of using machine learning in different code clone related research areas. We identify the need of more empirical studies to assess the benefits of machine learning in code clone research and give recommendations for future research.     

Impediments for software test automation: A systematic literature review

Automated software testing is a critical enabler for modern software development, where rapid feedback on the product quality is expected. To make the testing work well, it is of high importance that impediments related to test automation are prevented and removed quickly. An enabling factor for all types of improvement is to understand the nature of what is to be improved. We have performed a systematic literature review of reported impediments related to software test automation to contribute to this understanding. In this paper, we present the results from the systematic literature review: The list of identified publications, a categorization of identified impediments, and a qualitative discussion of the impediments proposing a socio‐technical system model of the use and implementation of test automation.     

Evolutions in machine learning technology for financial distress prediction: A comprehensive review and comparative analysis

In recent years, financial distress prediction (FDP), also known as corporate failure prediction or bankruptcy prediction, has gained significant importance due to its impact on organizations, especially during unexpected events like pandemics and wars. Machine learning (ML) models have emerged as innovative and essential tools in predicting financial distress, leveraging the ever-increasing volume of databases and computing power. This study utilizes bibliographic techniques to contribute to the field's literature review to address the disorganized nature of the existing literature on FDP, reduce confusion, and provide clarity to domain researchers. These techniques enable identifying the progress of articles published over the years, influential authors, and highly cited articles. Additionally, the study examines crucial aspects of data preprocessing, such as missing data, imbalanced data, feature selection, and outliers, as they significantly impact the robustness and performance of ML models. Furthermore, it discusses essential models employed in FDP, focusing on recent advancements that represent promising trends. In conclusion, this study contributes to the field by uncovering novel trends and proposing possible directions for advancing FDP research. These findings will guide researchers, practitioners, and stakeholders in their quest for improved prediction and decision-making in financial distress.     

A systematic review of systematic review process research in software engineering

ContextMany researchers adopting systematic reviews (SRs) have also published papers discussing problems with the SR methodology and suggestions for improving it. Since guidelines for SRs in software engineering (SE) were last updated in 2007, we believe it is time to investigate whether the guidelines need to be amended in the light of recent research.ObjectiveTo identify, evaluate and synthesize research published by software engineering researchers concerning their experiences of performing SRs and their proposals for improving the SR process.MethodWe undertook a systematic review of papers reporting experiences of undertaking SRs and/or discussing techniques that could be used to improve the SR process. Studies were classified with respect to the stage in the SR process they addressed, whether they related to education or problems faced by novices and whether they proposed the use of textual analysis tools.ResultsWe identified 68 papers reporting 63 unique studies published in SE conferences and journals between 2005 and mid-2012. The most common criticisms of SRs were that they take a long time, that SE digital libraries are not appropriate for broad literature searches and that assessing the quality of empirical studies of different types is difficult.ConclusionWe recommend removing advice to use structured questions to construct search strings and including advice to use a quasi-gold standard based on a limited manual search to assist the construction of search stings and evaluation of the search process. Textual analysis tools are likely to be useful for inclusion/exclusion decisions and search string construction but require more stringent evaluation. SE researchers would benefit from tools to manage the SR process but existing tools need independent validation. Quality assessment of studies using a variety of empirical methods remains a major problem.