A systematic review and trend analysis of personal learning environments research

The concept of personal learning environments (PLEs) is relatively new and is continuously developing. Over the past decade, there has been a significant upsurge in the number of PLEs-related research. Nevertheless, there is a lack of recent systematic reviews and trend analysis covering many PLEs studies; to the best of our knowledge. Therefore, the current systematic review is significant and indispensable in reviewing journal articles that discussed PLEs between 2000 and 2020. We searched Web of Science, Scopus, Sciences Direct, JSTOR, Springer, Google Scholar, and IEEE Xplore for studies published in English without limit in location or time to retrieve accurate results. Trend graphics for the extracted themes were also analyzed using descriptive statistics in Excel. According to the defined inclusion criteria, one hundred forty-eight articles were selected for the analysis. This study reveals that literature on PLEs has progressed from 2000 to 2020; the majority of PLEs-related articles were published between 2011 and 2020, with the year 2013 having the highest number of published articles (17 articles), followed by 16 papers published in both years 2014 and 2017. We found that the published PLEs research originated from 46 countries; 26 (17.6%) were from Spain. The majority of the authors had education, computer science, information technology and engineering backgrounds. This review also showed that numerous platforms had been used in PLEs research, with Web 2.0 the most commonly used platform. We noted that the most common objectives of the included articles were PLEs custom system development, analysis of the PLEs, description of experiments, investigations, development of factor models, framework development, and examination. The most common theoretical perspectives in the published articles were self-regulated learning, self-directed learning, and constructivism. The current systematic review and trend analysis can become a guidance platform for researchers, educators, policymakers or even journal publishers for future research in PLEs research

The Association between Success Center Utilization and a Technical College’s Student Retention

This study was conducted to examine the association between the Student Success Center and student retention at a South Carolina technical college. Recognizing the low retention rates of technical colleges in South Carolina and nationally, the college opened a Student Success Center in 2012; however, an analysis of the center’s effect on retention rates had not been conducted. With a better understanding of this relationship, the college can plan for future use of the center to strengthen retention. The key research question was focused on the association between Student Success Center attendance and student retention using an ex post facto design involving two dichotomous variables: attendance at the Student Success Center and retention over 3 years. A sample of 18,712 students was drawn from archival data maintained by the college to compare students who used the center and those who did not use the center, excluding transfer students and middle college students. Frequency percentage statistics were generated for the two dichotomous categorical variables in the study: center utilization and retention. Chi-square analysis with Yates correction was used to test for a significant association between the two variables. Findings showed evidence of a statistically significant association between center utilization and retention, χ2 (1) = 162.23, p \u3c 0.0001, indicating that student engagement with the Student Success Center contributed to resiliency as reflected in student retention. Therefore, this study contributed to research on the association between student support services for community college students and student retention, encouraging social change by strengthening practical solutions to the challenges faced by these students

Veri Madenciliği Yöntemleri ile Kurumsal E-Öğrenme Başarı Modeli Geliştirilmesi

The dynamic and more demanding nature of today’s life conditions force people and corporations to invest in life-long education. It is important to make this continuous learning process more affordable and accessible to larger groups of people. At this point, e-learning seems to be more convenient way of learning than formal education especially for working adults because of their time and place constraints and their need for flexibility. The crucial concern is whether the e-learning process is useful or not and under what conditions it brings more value to adult learners. Thus, the core research question guiding this study is: What are the most significant factors influencing corporatee-learning success? The study aims to answer this question by developing e-learning success models via data mining. After a number of data preprocessing activities, a combination of descriptive and predictive data mining methodologies are applied on the data set. Most of the independent factors (learner demographics, learner experience, and course characteristics) are discovered to have power at different levels for explaining variance in e-learning success. Course program characteristics like content type, existence of certification are explored having a strong influence on the success of e-learning process.Talepkar ve dinamik yaşam koşulları, bireyleri ve kurumları yaşam boyu eğitime yatırım yapmaya zorlamaktadır. Önemli olan nokta, sürekli eğitimi mümkün olduğu kadar çok kişi için erişilebilir hale getirmektir. Elektronik öğrenme, zaman ve yer kısıtlarından dolayı özellikle çalışan yetişkinler için örgün eğitimden daha elverişli bir yöntem olarak görülebilir. Bu noktada önemli olan husus, sunulan elektronik öğrenme sürecinin yararlı olup olmadığı ve hangi koşullar altında öğrencilere daha fazla fayda sağlayacağıdır. Bu olgu, çalışmaya yön veren temel araştırma sorusunu ortaya çıkarmıştır: Kurumsal elektronik öğrenmenin verimliliğini ve başarısını etkileyen en önemli faktörler nelerdir? Çalışmada, veri madenciliği metotları kullanılarak geliştirilen elektronik öğrenme başarı modelleri ile bu sorunun cevaplanması amaçlanmıştır. Veri seti üzerinde yapılan bazı ön temizleme işlemleri sonrası veri seti üzerinde tanımlayıcı ve tahmine yönelik veri madenciliği modelleri uygulanmıştır. Bağımsız faktörlerin birçoğunun başarıdaki varyansı farklı seviyelerde açıklayabildiği sonucu çıkarılmıştır. Elektronik dersin türü, sertifikalı olup olmama gibi özelliklerin elektronik öğrenme başarısına daha güçlü etkisi olduğu görülmüştür

A critical reflection on the affordances of web 3.0 and artificial intelligence in life sciences education

Life Sciences Education has become increasingly important in today's rapidly changing world, as it equips students with the knowledge and skills needed to tackle complex global challenges in various biology fields. With the emergence of Web 3.0 and Artificial Intelligence (AI), numerous opportunities exist to revolutionize Life Sciences Education and enhance student learning. However, integrating these technologies into traditional teaching methods poses significant challenges. This paper aims to explore the opportunities and challenges of Web 3.0 and AI in Life Sciences Education and provide recommendations for successful integration. The opportunities of Web 3.0 and AI in Life Sciences Education include enhanced personalized learning, increased engagement, access to vast amounts of data, and innovative assessment strategies. However, ethical concerns related to AI, integration with traditional teaching methods, training and professional development for educators, and cost and accessibility issues are among the challenges. The paper also provides case studies of successful implementation and recommendations for addressing ethical concerns, professional development, funding and accessibility, and collaboration between educators and technology experts. The paper concludes with implications for future research and practice in Life Sciences Education

Social personalized e-learning framework

This thesis discusses the topic of how to improve adaptive and personalized e-learning in order to provide novel learning experiences. A recent literature review revealed that adaptive and personalized e-learning systems are not widely used. There is a lack of interoperability between adaptive systems and learning management systems, in addition to limited collaborative and social features. First of all, this thesis investigates the interoperability issue via two case studies. The first case study focuses on how to achieve interoperability between adaptive systems and learning management systems using e-learning standards and the second case study focuses on how to augment e-learning standards with adaptive features. Secondly, this thesis proposes a new social framework for personalized e-learning, in order to provide adaptive and personalized e-learning platforms with new social features. This is not just about creating learning content, but also about developing new ways of learning. For instance, in the presented vision, adaptive learning does not refer to individuals only, but also to groups. Furthermore, the boundaries between authors and learners become less distinct in the Web 2.0 context. Finally, a new social personalized prototype is introduced based on the new social framework for personalized e-learning in order to test and evaluate this framework. The implementation and evaluation of the new system were carried out through a number of case studies.EThOS - Electronic Theses Online ServiceUniversity of Warwick. Dept. of Computer ScienceGBUnited Kingdo

ECO D2.6 Web 2.0 requirements analysis

ECO sMOOCs are social and seamless and the pedagogical design puts the learner central, taking an active role and learning through interactions and connections with others. The platforms have to provide the features not only support social interaction but promote and enhance these. This deliverable puts forward what features can scaffold interactions, taking into account lessons learned from popular social media.Part of the work carried out has been funded with support from the European Commission, under the ICT Policy Support Programme, as part of the Competitiveness and Innovation Framework Programme (CIP) in the ECO project under grant agreement n° 21127

Characteristics of Smartphone Applications for Nutrition Improvement in Community Settings: A Scoping Review

Reproduced by permission of Oxford University Press https://academic.oup.com Copyright © 2019 American Society for NutritionSmartphone applications are increasingly being used to support nutrition improvement in community settings. However, there is a scarcity of practical literature to support researchers and practitioners in choosing or developing health applications. This work maps the features, key content, theoretical approaches, and methods of consumer testing of applications intended for nutrition improvement in community settings. A systematic, scoping review methodology was used to map published, peer-reviewed literature reporting on applications with a specific nutrition-improvement focus intended for use in the community setting. After screening, articles were grouped into 4 categories: dietary self-monitoring trials, nutrition improvement trials, application description articles, and qualitative application development studies. For mapping, studies were also grouped into categories based on the target population and aim of the application or program. Of the 4818 titles identified from the database search, 64 articles were included. The broad categories of features found to be included in applications generally corresponded to different behavior change support strategies common to many classic behavioral change models. Key content of applications generally focused on food composition, with tailored feedback most commonly used to deliver educational content. Consumer testing before application deployment was reported in just over half of the studies. Collaboration between practitioners and application developers promotes an appropriate balance of evidence-based content and functionality. This work provides a unique resource for program development teams and practitioners seeking to use an application for nutrition improvement in community settings

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts