Virtual teams (i.e., geographically distributed collaborations that rely on technology to communicate and cooperate) are central to maintaining our increasingly globalized social and economic infrastructure. “Global Virtual Teams” that include members from around the world are the most extreme example and are growing in prevalence (Scott and Wildman in Culture, communication, and conflict: a review of the global virtual team literature, Springer, New York, 2015). There has been a multitude of studies examining the difficulties faced by collaborations and use of technology in various narrow contexts. However, there has been little work in examining the challenges faced by virtual teams and their use of technology to mitigate issues. To address this issue, a literature review was performed to highlight the collaboration challenges experienced by virtual teams and existing mitigation strategies. In this review, a well-planned search strategy was utilized to identify a total of 255 relevant studies, primarily focusing on technology use. The physical factors relating to distance are tightly coupled with the cognitive, social, and emotional challenges faced by virtual teams. However, based on research topics in the selected studies, we separate challenges as belonging to five categories: geographical distance, temporal distance, perceived distance, the configuration of dispersed teams, and diversity of workers. In addition, findings from this literature review expose opportunities for research, such as resolving discrepancies regarding the effect of tightly coupled work on collaboration and the effect of temporal dispersion on coordination costs. Finally, we use these results to discuss opportunities and implications for designing groupware that better support collaborative tasks in virtual teams.

/pdf/challenges-and-barriers-in-virtual-teams-a-literature-review-39624e922j.pdf

Challenges and barriers in virtual teams: a literature review

An Assessment of Associate Degree Nursing Students' Learning Style Using the Kolb Learning Style Inventory Version

Adaptive learning is an approach toward personalized learning and places the concept of “learner-centered education” into practice. With the rapid development of artificial intelligence and other technologies in recent years, there have been many breakthroughs in adaptive learning. Thus, it is important to gain insight into the evolution of related research and to track the research frontiers to further promote its development. This study used CiteSpace and VOSviewer to conduct a bibliometric analysis of 644 adaptive learning journal papers indexed in the WoS database from 2000 to 2022. This study presented a general view of the field of adaptive learning research over the last two decades using quantitative analysis. Currently, adaptive learning research is rapidly developing. In terms of the major research forces, a core group of authors including Qiao J. F., Han H. G. and Song Q has been formed; the major publishing country in this field is China; the core publishing journals include IEEE Transactions on Neural Networks and Learning Systems. Four major research topics in this field were identified using cluster analysis, namely the application of deep learning in educational data analysis, the development and application of adaptive learning model in AI education, the development and application of intelligent tutoring system in tutoring and teaching, cutting-edge modeling technology for feature modeling and knowledge tracing. Through evolution analyses, the logic of adaptive learning research’s development was determined; that is, technological changes have played a key role in the development of this field. Following the logic, we presented three frontiers of adaptive learning with burst terms: feature extraction, adaptation model and computational modeling. Adaptive learning is a core research topic for both computer science and educational technology disciplines, and it is also an important field where emerging technologies empowering education and teaching can play a part. The findings of the study clearly presented the current research status, evolutionary logic and research frontiers of this topic, which can provide references for the further development of this research field.

/pdf/research-landscape-of-adaptive-learning-in-education-a-kam5e7jz.pdf

Research Landscape of Adaptive Learning in Education: A Bibliometric Study on Research Publications from 2000 to 2022

This study aims to investigate an adaptive curriculum serving as a medium to build public trust at Islamic Elementary School (SDI) Tompokersan Lumajang, East Java. It is qualitative using phenomenological methods. Data were collected using interview, observation and documentation techniques. They were analyzed through several steps: data collection, data reduction, data presentation and conclusions. The results showed that the implementation of an adaptive curriculum was done by optimizing the role of school committees, developing excellent school, offering variations in extracurricular activities to motivate students, and providing counseling guidance programs. This research suggests the importance of innovation in delivering education to meet the social dynamics and global change.

https://journal.uinsgd.ac.id/index.php/jpi/article/download/17163/7609

Building Public Trust in Islamic School through Adaptive Curriculum

The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually, delivering the educational content in a personalized manner. In addition, the sequencing of this content-called Adaptive Curriculum Sequencing (ACS)-is important to avoid cognitive overload and disorientation. The search for an optimal sequence from ever-growing databases is an NP-Hard combinatorial optimization problem. Although some approaches have been proposed, it is challenging to assess their contributions due to the lack of benchmark data available. This paper presents a procedure to create synthetic dataset to evaluate ACS approaches and, as a concept proof, analyzes metaheuristics usually used in ACS approaches: Genetic Algorithm, Particle Swarm Optimization (PSO) and Prey-Predator Algorithm using student's learning goals and their extrinsic and intrinsic information. We also propose an approach based on Differential Evolution (DE). The computational experiments include synthetic datasets with a varied amount of learning materials and real-world datasets for comparison. The results show that DE performed better than the other methods when less than 500 learning materials are used while PSO performed better for larger problems.

/pdf/a-comparative-analysis-of-metaheuristics-applied-to-adaptive-2njs1syy1r.pdf

A comparative analysis of metaheuristics applied to adaptive curriculum sequencing

The presentation of learning materials in a sequence, which considers the association of students’ individual characteristics with those of the knowledge domain of interest, is an effective learning strategy in online learning systems, especially if related to traditional approaches However, this sequencing, called Adaptive Curriculum Sequencing (ACS), represents a problem that falls in the NP-Hard class of problems given the diversity of sequences that could be chosen from ever-larger repositories of learning materials Thus, metaheuristics are usually employed to tackle this problem This study aims to present a systematic review and mapping of the literature to identify, analyze, and classify the published solutions related to the ACS problem addressed by metaheuristics We considered 61 studies in the mapping and 58 studies in the review from 2005 to 2018 Even though the problem is longstanding, it is still discussed, especially considering new modeling and used metaheuristics In this sense, we emphasize the use of Swarm Intelligence and Genetic Algorithm Moreover, we have identified that various parameters were considered for students and knowledge domain modeling, however, few student’s intrinsic parameters have been explored in ACS literature

Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature

Recommendation systems (RS) have been used in many scenarios, from entertainment to health. Inside the RS area, Educational Recommendation Systems (ERS) are becoming popular, been used for different types of recommendations such as recommending materials, exercises, and learning paths. As ERS works in a different scenario of classics RS, ERS requires specific evaluation metrics. However, the task of evaluating ERS is difficult since the educational field has its features to be analyzed. To help other researchers in this field, this work presents a systematic mapping on methods used for evaluating ERS. This study analyzed 91 papers of the last five years and provide an overview of the main methodologies, subject, metrics, and trends in the evaluation of ERS.

/pdf/evaluating-educational-recommendation-systems-a-systematic-41d7hqrsdf.pdf

Evaluating Educational Recommendation Systems: a systematic mapping

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Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature

A comparative analysis of metaheuristics applied to adaptive curriculum sequencing

Evaluating Educational Recommendation Systems: a systematic mapping