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Academic social-networking sites (ASNS) such as Academia.edu and ResearchGate are becoming very popular among academics. These sites allow uploading academic articles, abstracts, and links to published articles; track demand for published articles, and engage in professional interaction. This study investigates the nature of the use and the perceived utility of the sites for academics. The study employs the Uses and Gratifications theory to analyze the use of ASNS. A questionnaire was sent to all faculty members at three academic institutions. The findings indicate that researchers use ASNS mainly for consumption of information, slightly less for sharing of information, and very scantily for interaction with others. As for the gratifications that motivate users to visit ASNS, four main ones were found: self-promotion and ego-bolstering, acquisition of professional knowledge, belonging to a peer community, and interaction with peers.

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While ubiquitous in everyday use, in reality, social media usage within higher education teaching has expanded quite slowly. Analysis of social media usage of students and instructors for teaching, learning, and research purposes across four countries (Russia, Turkey, Germany, and Switzerland) showed that many higher education instructors actively use social media for private purposes. However, although they understand that their students also use it for learning purposes, and instructors sense the potential of social media in teaching, they mostly refrain from doing so due to various barriers. In response, an openly accessible trilingual Social Media Toolkit was developed which analyzes the teaching scenario with several questions, before suggesting, based on an algorithm, the best matching class of social media, complete with advice on how to use it for teaching purposes. This paper explains the rationale behind the toolkit, its development process, and examines instructors’ perceptions towards it.

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This study aimed to analyze the student’s behaviour in relation to their degree of commitment, participation, and contribution in a MOOC based on a social learning approach. Interaction data was collected on the learning platform and in social networks, both of which were used in the third edition of a social MOOC course. This data was then studied via statistical methods and analysis of social networks. This study assumes that social communities would arise around the course, would remain over time, and that participants would even contribute with new proposals. The findings indicated that social learning communities are built and continue only while the course is open and while the teachers are involved in fostering participation. Although this study is limited, the design criteria of the course, the pedagogical model on which this is supported, and the methods applied for this analysis provide other researchers and educators with clues for better understand the dynamic process of social learning in social MOOCs.

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The tangible and intangible needs of people with disabilities have been neglected in research agendas over the past decades, particularly in China. Bringing more benefits to people with disabilities and combining information delivery with Internet technology to improve their quality of life is impossible if their special needs are ignored. This study employed the crowdsourcing through social media method to investigate information needs of people who are disabled. The results indicate that using the popular social media platforms WeChat and QQ can be an effective means of reaching out to this group. Information needs can be mainly divided into internal and external needs. Leisure, tourism, and entertainment as well as assistive devices are major internal information needs, whereas work, entrepreneurship, and living services are the main external information needs.

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With its total number of users (around 62 million) throughout the world, it is important to determine the views of academics who use Edmodo (the leading SLN. In this respect in the first part of this two-part research, the purpose was to examine academics’ (n=50) use of technology and social networks. As for the purpose of the second part, it was to determine the views of 12 academics—selected from the academics participating in the first part—who had experience in Edmodo about the basic features of Edmodo and about its use in education. In the study carried out with the mixed method, the qualitative and quantitative data were collected with an online questionnaire. The findings obtained were interpreted within the framework of cooperative learning and the theories of “Diffusion of Innovations” and “Uses and Gratifications,” and the related themes were formed. As a result, the academics with experience in Edmodo reported their views about the benefits of use of the Edmodo in education. Regarding the differences between Edmodo and social networks, the results suggested that the former was used completely for educational purposes and that it did not involve any unnecessary components.

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Teachers participate in social networking sites to share knowledge and collaborate with other teachers to create education-related content. In this study we selected several communities in order to better understand the networks that these participants establish in Twitter and the role that the social network plays in their activity within the community, especially related with peer production. We analyzed the topology of these networks in two ways: a) the indirect relations by counting followers and followed people; and b) the conversational networks by counting mentions in tweets. We also analyzed the communities’ websites in order to elucidate whether their production was lightweight or heavyweight peer production. Results indicate that teacher networks adopt a community clusters archetype in which some teachers act as bridges between several groups. Although these networks do not form a tight crowd, their degree of tightness is superior to that of the general networks established in Twitter. Our results also indicate that the degree of tightness is important for sustaining heavyweight peer production and strong leadership can play a crucial role in establishing long-term commitment to a collective task.

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The aim of this paper is to present the learning and teaching styles using the Social Learning Environment (SLE), which was developed based on the computer supported collaborative learning approach. To avoid burdening learners with multiple platforms and tools, SLE was designed and developed in order to integrate existing systems, institutional learning, management systems, and Facebook. With SLE, a learner is exposed to instructional learning material, problem-based, project-based, and social learning. This work focuses on presenting and analyzing effectiveness of implemented teaching and learning scenarios that are used by means of SLE. This study investigates the use of SLE as a learning environment and it examines student satisfaction and online activity while using it. The aim is to evaluate the overall effectiveness of integrating SLE with online learning and to determine how using SLE effects student learning. This analysis points out the advantages and disadvantages of using SLE in online learning and provides recommendations for future improvements.

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Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present. As manual metadata generation is time-costly and often eschewed by the authors of the social web resources, automatic generation is a fertile area for research as several kinds of metadata, such as author or topic, can be generated or extracted from the contents of a document. In this paper we propose a novel metadata generation system aimed at automatically tagging distance learning resources. This system is based on a recently-created intelligent pattern classifier; specifically, it trains on a corpus of example documents and then predicts the topic of a new document based on its text content. Metadata is generated in order to achieve a better integration of the web resources with the social networks. Experimental results for a two-class problem are promising and encourage research geared towards applying this method to multiple topics.

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The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is modeled in this paper as a binary pattern classification problem. The latter problem is then solved through the application of our new proposed associative model. The solution proposed here allows the alignment of two different ontologies —both in the Learning Objects Metadata (LOM) format— into a single ontology of LOs for ODL in LOM format, without redundant objects and with all inherent advantages for handling ODL LOs. The proposed model of pattern classification was validated through experiments, which were done on data taken from the Ontology Alignment Evaluation Initiative (OAEI) 2014 campaign, as well as on data taken from two known educative content repositories: ADRIADNE and MERLOT. The obtained results show a high performance when compared against some of the classifier algorithms present in the state of the art.

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The Color Coded Cards system as a possibly effective class management tool is the focus of this research. The Color Coded Cards system involves each student being given a card with a specific color based on his or her behavior. The main objective of the research is to find out whether this system effectively improves students’ behavior, thus leading to better class management. The research was conducted with 350 students as subjects, and the data were gathered by means of a questionnaire administered over a period of three years. A qualitative method was applied to analyze the results and draw precise conclusions. The results were interesting and promising.

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Although educational practitioners have adopted social media to their online or mobile communities, little attention has been paid to investigate the social media messages related to online or mobile learning. The purpose of this research is to identify social media influencers and trends by mining Twitter posts related to online learning and mobile learning. We identified the influencers on Twitter by three different measures: the number of tweets posted by each user, the number of mentions by other users for each user, and the number of retweets for each user. We also analyzed the trends of online learning and mobile learning by the following perspectives: the descriptive statistics of the related tweets, the monthly and hourly line charts of the related tweets, the descriptive statistics of the related retweets, the volume trends of the most retweeted tweets, and the top 10 hashtags of the related tweets. The results of this study can provide educational practitioners different ways of understanding and explaining the public opinions toward online learning and mobile learning.

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Massive Open Online Courses and e-learning represent the future of the teaching-learning processes through the development of Information and Communication Technologies. They are the response to the new education needs of society. However, this future also presents many challenges such as the processing of online forums when a huge number of messages are generated. These forums provide an excellent platform for learning and connecting students of the subject, but the difficulties in following and searching the vast volume of information that they generate may produce the opposite effect. In this paper, we propose a computational method for enabling the educational process in huge online learning communities. This method analyses the forum information through Natural Language Processing techniques and extract the main topics discussed. The results generated improves the management of the forums, increases the effectiveness of the teachers’ explanations and reduces the time spent by students to follow the course. The proposal has been complemented with a real case study that shows promising results.

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This paper presents We-Share, a social annotation application that enables educators to publish and retrieve information about educational ICT tools. As a distinctive characteristic, We-Share provides educators data about educational tools already available on the Web of Data while allowing them to enrich such data with their experience using technology in the classroom. We-Share evaluation entails an empirical study where 23 educators enriched tool descriptions available on the Web of Data out of their own experience. The results suggest that experiential annotations published by educators using We-Share improve the satisfaction and confidence of other educators when discovering and selecting ICT tools. Further, most educators found We-Share an easy-to-use application suitable to share and retrieve information about educational ICT tools.

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The quality of Education in Chile is a controversial topic that has been in the public debate in the last several years. To ensure quality in graduate programs, accreditation is compulsory. The current article presents a model to improve the process of self-regulation. The main objective was to design a Model of Quality Assurance for Postgraduate Programs in order to constitute a theoretical, mathematical, and informatics reference that would optimize the processes of self-regulation, self-evaluation, and accreditation of master and doctorate programs from the Universidad Católica de la Santísima Concepción, Chile. This descriptive research is based on a mixed methods approach. The proposal was intended through theoretical and empirical references related to the accreditation systems. The analysis process was conducted with key informants, and the informatics instrument was created and validated through expert judgment. After the analysis, the model was optimized considering the expert’s suggestions. As a result of the optimization process, a matrix of eight dimensions was obtained and it is available online in order to be used by the heads of postgraduate programs. Finally, a model with four main stages was achieved in order to install a self-regulation and a self-evaluated culture that leads to accreditation as evidence of the quality of postgraduate programs.