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.     

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 A comprehensive survey of published and unpublished studies identified 78 empirical studies that met the inclusion criteria.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.     

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.     

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.     

Inquiry-based mobile learning in secondary school science education: A systematic review

Recent years have seen a growing call for inquiry-based learning in science education, and mobile technologies are perceived as increasingly valuable tools to support this approach. However, there is a lack of understanding of mobile technology-supported inquiry-based learning (mIBL) in secondary science education. More evidence-based, nuanced insights are needed into how using mobile technologies might facilitate students' engagement with various levels of inquiry and enhance their science learning. We, therefore, conducted a robust systematic literature review (SLR) of the research articles on mIBL in secondary school science education that have been published from 2000 to 2019. We reviewed and analysed 31 empirical studies (34 articles) to explore the types of mIBL, and the benefits and constraints of mIBL in secondary school science education. The findings of this SLR suggest new research areas for further exploration and provide implications for science teachers' selection, use and design of mIBL approaches in their teaching.     

A systematic literature review on Internet of things in education: Benefits and challenges

With close to 20.4 billion devices connected to the Internet to be deployed by 2020, Internet of things (IoT) is already being leveraged in diverse sectors. Now, because of the ubiquitous nature of IoT devices, schools and academic institutions are looking to incorporate IoT in educational activities. With the increased use of IoT in the education domain, it is of utmost importance to study how this technology with its distinguished system functions such as sensing and decision making can support and challenge the pedagogical processes for all interrelated actors (faculty, students, and staff) as well as all involved assets (e.g., libraries, classrooms, and labs). Although there have been several contributions on the inclusion of IoT into the education domain, there is still a lack of consolidated and coherent views on this subject. Hence, we are motivated to close the gap of knowledge and embarked on mapping out the published studies available. This study presents the results of a systematic literature review focusing on the benefits and the challenges faced in education in integrating IoT into the curriculum and educational environments. Different mapping views of the extracted studies are provided as long as a summary of the already implemented tools and a list of gap research questions yet to be investigated.     

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.     

Tablet use in schools: a critical review of the evidence for learning outcomes

The increased popularity of tablets in general has led to uptake in education. We critically review the literature reporting use of tablets by primary and secondary school children across the curriculum, with a particular emphasis on learning outcomes. The systematic review methodology was used, and our literature search resulted in 33 relevant studies meeting the inclusion criteria. A total of 23 met the minimum quality criteria and were examined in detail (16 reporting positive learning outcomes, 5 no difference and 2 negative learning outcomes). Explanations underlying these observations were analysed, and factors contributing to successful uses of tablets are discussed. While we hypothesize how tablets can viably support children in completing a variety of learning tasks (across a range of contexts and academic subjects), the fragmented nature of the current knowledge base, and the scarcity of rigorous studies, makes it difficult to draw firm conclusions. The generalizability of evidence is limited, and detailed explanations as to how, or why, using tablets within certain activities can improve learning remain elusive. We recommend that future research moves beyond exploration towards systematic and in‐depth investigations building on the existing findings documented here.     

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.     

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 literature review of Linked Data‐based recommender systems

Recommender systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in RS that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data have been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations, use of more datasets considering the heterogeneity introduced, creation of new hybrid RS for adding information, definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets, and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

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.     

Architecting cloud‐enabled systems: a systematic survey of challenges and solutions

The literature on the challenges of and potential solutions to architecting cloud‐based systems is rapidly growing but is scattered. It is important to systematically analyze and synthesize the existing research on architecting cloud‐based software systems in order to build a cohesive body of knowledge of the reported challenges and solutions. We have systematically identified and reviewed 133 papers that report architecture‐related challenges and solutions for cloud‐based software systems. This paper reports the methodological details, findings, and implications of a systematic review that has enabled us to identify 44 unique categories of challenges and associated solutions for architecting cloud‐based software systems. We assert that the identified challenges and solutions classified into the categories form a body of knowledge that can be leveraged for designing or evaluating software architectures for cloud‐based systems. Our key conclusions are that a large number of primary studies focus on middleware services aimed at achieving scalability, performance, response time, and efficient resource optimization. Architecting cloud‐based systems presents unique challenges as the systems to be designed range from pervasive embedded systems and enterprise applications to smart devices with Internet of Things. We also conclude that there is a huge potential of research on architecting cloud‐based systems in areas related to green computing, energy efficient systems, mobile cloud computing, and Internet of Things. Copyright © 2016 John Wiley & Sons, Ltd.     

Compiler testing: a systematic literature analysis

Compilers are widely-used infrastructures in accelerating the software development,and expected to be trustworthy.In the literature,various testing technologies have been proposed to guarantee the quality of compilers.However,there remains an obstacle to comprehensively characterize and understand compiler testing.To overcome this obstacle,we propose a literature analysis framework to gain insights into the compiler testing area.First,we perform an extensive search to construct a dataset related to compiler testing papers.Then,we conduct a bibliometric analysis to analyze the productive authors,the influential papers,and the frequently tested compilers based on our dataset.Finally,we utilize association rules and collaboration networks to mine the authorships and the communities of interests among researchers and keywords.Some valuable results are reported.We find that the USA is the leading country that contains the most influential researchers and institutions.The most active keyword is“random testing”.We also find that most researchers have broad interests within small-scale collaborators in the compiler testing area.