No abstract

BackgroundCollaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students’ performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance.MethodsInteraction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students’ performance was calculated, and automatic linear regression was used to predict students’ performance.ResultsBy using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user’s position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student’s position and role in information relay in online case discussions, combined with the strength of that student’s network (social capital), can be used as predictors of performance in relevant settings.ConclusionBy using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students’ and teachers’ interactions that can be valuable in guiding teachers, improve students’ engagement, and contribute to learning analytics insights.Electronic supplementary materialThe online version of this article (10.1186/s12909-018-1126-1) contains supplementary material, which is available to authorized users.

An introduction to computational thinking that traces a genealogy beginning centuries before the digital computer. A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, “computational thinking” has become part of the K–12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview, tracing a genealogy that begins centuries before digital computers and portraying computational thinking as pioneers of computing have described it. The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes. Mathematically trained experts (known as “computers”) who performed complex calculations as teams engaged in CT long before electronic computers. The authors identify six dimensions of today's highly developed CT—methods, machines, computing education, software engineering, computational science, and design—and cover each in a chapter. Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity.

The analysis of students' online activities in a blended medical education course by means of LA techniques can help early predict underachieving students, and can be used as an early warning sign for timely intervention.

To ensure online collaborative learning meets the intended pedagogical goals (is actually collaborative and stimulates learning), mechanisms are needed for monitoring the efficiency of online collaboration. Various studies have indicated that social network analysis can be particularly effective in studying students’ interactions in online collaboration. However, research in education has only focused on the theoretical potential of using SNA, not on the actual benefits they achieved. This study investigated how social network analysis can be used to monitor online collaborative learning, find aspects in need of improvement, guide an informed intervention, and assess the efficacy of intervention using an experimental, observational repeated-measurement design in three courses over a full-term duration. Using a combination of SNA-based visual and quantitative analysis, we monitored three SNA constructs for each participant: the level of interactivity, the role, and position in information exchange, and the role played by each participant in the collaboration. On the group level, we monitored interactivity and group cohesion indicators. Our monitoring uncovered a non-collaborative teacher-centered pattern of interactions in the three studied courses as well as very few interactions among students, limited information exchange or negotiation, and very limited student networks dominated by the teacher. An intervention based on SNA-generated insights was designed. The intervention was structured into five actions: increasing awareness, promoting collaboration, improving the content, preparing teachers, and finally practicing with feedback. Evaluation of the intervention revealed that it has significantly enhanced student-student interactions and teacher-student interactions, as well as produced a collaborative pattern of interactions among most students and teachers. Since efficient and communicative activities are essential prerequisites for successful content discussion and for realizing the goals of collaboration, we suggest that our SNA-based approach will positively affect teaching and learning in many educational domains. Our study offers a proof-of-concept of what SNA can add to the current tools for monitoring and supporting teaching and learning in higher education.

No abstract

The media environment has radically changed over the past few decades. Transition and transformation of media platforms has enabled algorithms and automation to take over media processes such as production, content generation, curation, delivery, recommendation, and filtering of information. It has also enabled tracking of users' actions, data mining, profiling, and the use of computational and machine learning techniques for purposes like behavior engineering, targeted advertisement, spread of mis-and disinformation, swaying political moods, and many others. In the field of media literacy education, the need to understand algorithm-driven media requires educators to rethink the connections between media literacy education and computing education. This article provides an overview of some computational mechanisms of today's media, and it provides new perspectives for media literacy education. The article suggests ways of intertwining media literacy education with computing education in order to improve students' readiness to cope with modern media and to become critical and skilled actors to navigate in the today's media landscape.

The use of mobile phone technology has increasingly been advocated to assist smallscale farmers. Accordingly, numerous studies have been conducted on the impact, effectiveness, user's attitude, assessment, empowerment, and the potential use of mobile phone technology in agriculture. This study explores the challenges that small-scale farmers in sub-Saharan Africa face when using a mobile phone technology in crop farming projects and proposes areas for future improvement. The study used a systematic literature search conducted by authors at 3 levels, in which 134 studies initially identified were then narrowed to 11. These 11 studies generated 7 projects that use specialized applications in a farming value chain. The findings from the study indicate some of the challenges faced by small-scale farmers, including the lack of their involvement in the initial phase of the invention process. Other obstacles include low trust and transparency, inappropriate use of foreign language (English) in a local cultural context, bureaucracy, and theft of mobile phones. On the basis of these results, the authors conclude that there are generalized factors for understanding deficiencies experienced by small-scale farmers, which ought to be understood by all crop farming stakeholders. These factors can be used by software engineers to design future technologies beneficial to small-scale farmers. KEYWORDSchallenges, farming information, mobile phone, small-scale farmers, sub-Saharan Africa | INTRODUCTIONSeveral studies postulate that mobile phone technology use is important to farmers and supports crop production (Baumüller, 2013;CIARD, 2012;De Silva & Ratnadiwakara, 2008;Duncombe, 2016;Furuholt & Matotay, 2011;Gayi & Tsowou, 2016;Wellard, Rafanomezana, Nyirenda, Okotel, & Subbey, 2013). The use of mobile phone technology in the crop farming value chain enables small-scale farmers (SSFs) to access farming information that supports optimal decision-making and increases crop productivity. Consequently, it is envisaged that SSFs using mobile phone technology may have the potential to improve food security and reduce poverty in sub-Saharan Africa (SSA). Mungera and Karfakis (2013) describe SSFs as people who own between 0.1 and 10 hectares of land. Small-scale farmers commonly have access to less than 2 hectares of land (NEPAD, 2013;Vanlauwe et al., 2014). This study reiterates that SSFs are characterized by limited access to land, low skills, family labour, and subsistence farming practices based on rudimentary inputs and are rainfall dependent with low bargaining power.In Tanzania and Zambia, 75% of the total population derives their livelihoods from agriculture (Kalinda, Filson, & Shute, 2010;Misaki, Apiola, & Gaiani, 2016). Hence, an innovation that increases agricultural productivity cannot be ignored as it improves the livelihood of most farmers. Studies that address challenges facing SSFs may be pivotal in reversing a decline in food production and deserve attention (Kalinda et al., 2010). One of the many difficulties SS...