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The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

The purpose of this study was to establish a massive online open course (MOOC)-based map of higher education knowledge and apply it to university libraries. It hoped to provide more targeted and personalized learning services for every learner.

In this study, MOOC and university library information services were outlined, the development status of MOOC at home and abroad and the development of university library information services were introduced, and the necessity and significance of MOOC in developing information services in university libraries were analyzed. What is more, the knowledge map of university libraries was explored. The four modules include the construction of data sets, the identification of related entities from plain text, the extraction of entity relationships and the practical application of knowledge maps. For the logical relationship of the course, a combination of knowledge base and machine learning was adopted. In the knowledge map application module, the knowledge map was visualized. Aiming at the generation of personalized learning scheme, a prior data set was constructed by means of the knowledge base. The original problem was considered as a multi-classification problem. K-nearest neighbor classifier divided all courses into four academic years to obtain all courses. According to the course stage, the personalized learning scheme of some majors in higher education was obtained.

The experiment showed that it was feasible to apply the higher education knowledge map based on MOOC to university libraries. In addition, it was effective to divide the course into four stages by classifier. In this way, the specific professional training program can be obtained, the information service of the university library can be improved, and the accuracy and richness of the entire learning program can be increased.

Due to the limitations of conditions, time and other aspects, there were not many opportunities to visit the field library, which led to limited level and imperfect research. There were many proper nouns and professional terms in foreign references, but my English translation ability was limited. The relevant investigation on foreign studies may not be detailed and comprehensive enough, and the analysis and induction of influencing factors of university library information service may not be rigorous and concise enough.

As the base of university information dissemination, the university library is the source of knowledge. At the same time, it is also the temple of students’ independent learning and the media of mainstream culture and improving its own information service level is also in line with the trend of The Times. Under this background, this research studied the influence of MOOC on university library information service and focused on the challenges and opportunities faced by university library information service in the MOOC environment, so as to continuously improve its cultural serviceability and better serve teachers and students.

Since the birth of MOOC, they have exerted great influence and enlightenment on universities and relevant educational institutions within a few years. European and American universities take an active part in the construction of the MOOC platform and explore how to make better use of the library to build MOOC resources in practice. It is also a hot topic for university libraries to participate in the construction of MOOC information resources. Therefore, the study of this topic has both theoretical and practical significance.

The application of massive open online courses (MOOCs) helps integrate sustainable development goals (SDGs) into architectural curricula. The essence of MOOC development is building an education platform that promotes the sharing and continuing of global education resources.

This study establishes a four-dimensional evaluation model based on the four characteristics of MOOCs. The quadrilateral area evaluation method is used to create an evaluation radar chart to comprehensively evaluate satisfaction and demand in the traditional teaching model of architectural technology. This study discusses whether the curriculum is suitable for the development of MOOCs and how to optimize the sustainable pedagogical mode according to its characteristics to meet future teaching needs and realize the sustainable development of education.

Satisfaction evaluation found that current education is not open enough from the students' perspective; therefore, MOOCs enhance students' participation and significantly reduce future learning costs. Through demand evaluation, it was found that both teachers and students believed that the lack of direct and effective communication between them and the difficulty in ensuring the learning effect were problems that must be addressed in MOOCs.

This study focused on the sustainability of MOOCs in curriculum development. It emphasizes the combination of MOOCs' teaching modes and the course itself and provides specific guidance and suggestions for improving the course. It uses an evaluation method for objective analysis and visualization.

The purpose of this paper is to explore the potential of using massive open online courses (MOOCs) for employee learning and development in organizational settings by reviewing the literature and examining several cases from a virtual human resource development (HRD) perspective.

To collect information on MOOCs in the workplace, the authors reviewed peer- and non-peer-reviewed articles, book chapters, books, white papers, corporate websites and blogs, and business magazines published between 2008 and 2018.

A total of 18 cases were summarized to present the use of MOOCs in organizational settings for training and development, organizational development, career development, and professional development. For a more in-depth review, three cases were selected and the details were introduced in terms of context, process/progress, and outcomes: McAfee, Rabobank, and Library and Information Science.

When organizations decide to use MOOCs for their employee development and learning, practitioners could also establish the selection criteria of MOOCs for specific purposes based on their organizational contexts by assessing the effectiveness of existing MOOC programs.

This study highlights the use of MOOCs in organizations to examine their potential as a support tool for virtual HRD to facilitate employee learning and development in the workplace.

The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).

Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.

The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).

This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

The purpose of this paper is to examine hiring managers’ perceptions of massive open online courses (MOOCs) as compared to traditional degree-conferred forms of higher education in relation to hiring and employment decisions.

A literature review is presented along with a triangulated theoretical framework. Using online survey data, quantitative methods reveal findings related to the main research question: what are hiring managers’ attitudes toward MOOCs as a form of post-secondary education?

Analysis of the data reveals that hiring managers have a clear preference for traditionally educated job applicants but employer demographics, apart from organizational procedures, do not significantly impact their overall perceptions of MOOCs’ value.

Most of the research is based on anecdotal research. Very little has been written on how to fix this problem.

This paper illustrates implications of MOOCs’ future development and implementation both in higher education and in the labor-force. The main implication is that MOOCs represent neither a panacea to the issues facing higher education and the American labor-force nor an alarming threat to stakeholders appreciative of the status quo.

This paper fills a current research gap as evidenced in the literature; employers’ perceptions of MOOC-educated job applicants when compared to traditionally educated/degree-conferred job applicants. By determining the value of MOOCs as employers pragmatically view them, stakeholder groups can better determine their future positioning of MOOC-related resources in addition to time and money allocated in MOOCs’ direction.

The purpose of this paper is to understand the outcomes of Massive Open Online Courses (MOOCs) in the light of COVID-19 concerning the students of higher educational institutions (HEIs) in India. The COVID-19 has disrupted the normal teaching-learning role across the world and has put everyone in a nightmare. HEIs are now requesting students to take up MOOCs to explore and attain knowledge and the same is even followed by the corporate institutions. MOOCs are one of the crisis management solutions to ensure that education is continuous and not disrupted.

The data included in this research has been collected from students of HEIs across India using a convenient sampling method. The collected data was exposed to a factor analysis using a principal component analysis (PCA) technique to reduce multiple dimensions.

The various stakeholders such as the government, HEIs and the MOOC providers have to play a crucial role in developing intellectual human assets for the nation’s growth and progression by extending flexible and cost-effective learning facilities. Education should be free to boost up the learning motivation, and hence it is the responsibility of the stakeholders to provide few courses free of cost, which will increase the rate of enrolment and student participation.

From the research, it is well understood that MOOCs are useful to keep oneself updated with the market and industry trends especially when the world is focusing on business analytics, artificial intelligence and other technologically driven topics and concepts.

The present study contributes to the growing body of evidence that MOOCs play an important role in providing flexibility in learning. In the future, if there are similar crisis, which will disrupt education, then the best alternative will be MOOC through which many stakeholders will benefit. Education should be free to boost up the learning motivation, and hence it is the responsibility of the stakeholders to provide few courses free of cost, which will increase the rate of enrolment and student participation.

Education is a service for the society which will have a long-term positive impact on improving the standard of living of the people. Hence, MOOCs can be one of the educational elements to provide learning opportunities to all age groups.

This study has explored the perception of MOOCs among the students of HEIs in India in the COVID-19 pandemic. The fresh data collected from the students is a reflection of their experiences during the COVID-19 lockdown. Indeed, it is quite surprising to know that majority of the respondents have arranged to learn during the pandemic, which shows the thirst and urge to learn. Digital technology and tools are welcomed and accepted by the student community.

The purpose of this study is to investigate the factors underlying the adoption of massive open online courses (MOOCs), using technology-user-environment (TUE) framework and self-determination theory (SDT) as the theoretical frameworks.

The primary data were collected from the field surveys conducted in the universities and academic institutions located in National Capital Region of Delhi, India, using convenience sampling technique. Structural equation modelling was used to test the hypothesized relationships in the proposed model.

The findings indicate that the learners’ intention to adopt MOOCs is significantly influenced by intrinsic motivation, social recognition, perceived value and perceived usefulness. On the other hand, the personal readiness, self-regulation of learners and peer influence are not found to have any significant impact on MOOCs adoption intention.

The findings of the study will be helpful for MOOCs providers and other stakeholders. The MOOCs providers should emphasize on providing courses from renowned universities in cutting-edge areas which are self-paced and cost-effective. The academic institutions should provide credit benefits to the students in lieu of completing courses through MOOCs. Likewise, employers should also recognize the certificates awarded by MOOCs and give due credit to the learners who complete such certifications.

The study has contributed to the existing literature on MOOCs adoption by combining constructs from TUE and SDT. To the best of the author’s knowledge, this has been a first attempt to combine these two frameworks to study the learners’ adoption behaviour for MOOCs in Indian context. The integration of these two frameworks provides a more comprehensive model of factors with increased explanatory ability (72.6 per cent) to describe the adoption intention of MOOCs.

Freelancers are a growing population of working adults with limited to no organizational support. Yet, their strategies to navigate job search, especially in turbulent times, are unknown. To address this gap, the author hypothesized and examined a sequential mediation model whereby freelancer protean career orientation (PCO) influences job search strategies through career competencies (i.e. knowing why, how and with whom to work) and job search self-efficacy (JSSE).

Data were collected from a sample of 87 Canadian freelancers during the height of the COVID-19 pandemic.

The results supported the sequential mediation from PCO to job search strategies through two of the career competencies (knowing why and how) and JSSE. The mediating role of knowing whom was not supported.

Policy makers and learning institutions can provide freelancers with opportunities to develop transferable skills through massive open online courses (MOOCs). Employers of freelancers can design motivating jobs that provide freelancers with on-the-job learning and development opportunities.

The insignificant mediating role of knowing whom (i.e. professional networks) implies that large networks might not be necessarily beneficial in times of crisis. Thus, the role of networks might be more complex than the literature has proposed.

This study brings into focus an overlooked population of workers: freelancers. It investigates a sequential mediation through which freelancer PCO impacts job search strategies. In addition, it compares the effectiveness of career competencies in unfolding the proposed sequential mediation.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.