The master thesis is the last formal step in most universities around the world. However, all students do not finish their master thesis. Thus, it is reasonable to assume that the non-completion of the master thesis should be viewed as a substantial problem that requires serious attention and proactive planning. This learning analytics study aims to understand better factors that influence completion and non-completion of master thesis projects. More specifically, we ask: which student and supervisor factors influence completion and non-completion of master thesis? Can we predict completion and noncompletion of master thesis using such variables in order to optimise the matching of supervisors and students? To answer the research questions, we extracted data about supervisors and students from two thesis management systems which record large amounts of data related to the thesis process. The sample used was 755 master thesis projects supervised by 109 teachers. By applying traditional statistical methods (descriptive statistics, correlation tests and independent sample t-tests), as well as machine learning algorithms, we identify five central factors that can accurately predict master thesis completion and non-completion. Besides the identified predictors that explain master thesis completion and non-completion, this study contributes to demonstrating how educational data and learning analytics can produce actionable data-driven insights. In this case, insights that can be utilised to inform and optimise how supervisors and students are matched and to stimulate targeted training and capacity building of supervisors.

The digitally marginalised communities are in focus in the EU-funded Rural Wings project [2006][2007][2008]. The aim is to identify and analyse the user learning needs in non-connected communities and to meet these needs by providing satellite Internet broadband connections, education and tools. This article reports the findings of the user needs investigation of 31 communities in 10 countries in the initial phase of the project designed and coordinated by Stockholm University, Sweden. Each national coordinator conducted a user needs study in their country using a common framework of questions and guidelines. The sites were selected according to national and regional needs and where satellite-provided Internet is believed to be the long-term solution. The 31 communities selected can be summarised as (a) mainland/lowland communities, 10 (b) mainland/highland communities, 12 (c) island/lowland communities, 5, and (d) island/highland communities, 4. The analysis reveals common threads of lack of infrastructure, accessibility and reliability concerning information and communication technology (ICT) implementation and containment in the community. There is a plethora of reasons for wanting more reliable and frequent Internet connections. Reasons range from educational opportunities, language opportunities, governmental inclusion, information/news outlets, and medical and weather initiative capabilities. This study provides in-depth macro-summaries of each participating country's user needs analysis in total. It also includes the actual analysis of each test site based on over 31 sites spanning 10 European nations. In addition, generalisations, comparisons and differences have been composed, to provide a framework for European trends in rural ICT access.

<p class="0abstractCxSpLast">The bachelor thesis is commonly a necessary last step towards the first graduation in higher education and constitutes a central key to both further studies in higher education and employment that requires higher education degrees. Thus, completion of the thesis is a desirable outcome for individual students, academic institutions and society, and non-completion is a significant cost. Unfortunately, many academic institutions around the world experience that many thesis projects are not completed and that students struggle with the thesis process. This paper addresses this issue with the aim to, on the one hand, identify and explain why thesis projects are completed or not, and on the other hand, to predict non-completion and completion of thesis projects using machine learning algorithms. The sample for this study consisted of bachelor students’ thesis projects (n=2436) that have been started between 2010 and 2017. Data were extracted from two different data systems used to record data about thesis projects. From these systems, thesis project data were collected including variables related to both students and supervisors. Traditional statistical analysis (correlation tests, t-tests and factor analysis) was conducted in order to identify factors that influence non-completion and completion of thesis projects and several machine learning algorithms were applied in order to create a model that predicts completion and non-completion. When taking all the analysis mentioned above into account, it can be concluded with confidence that supervisors’ ability and experience play a significant role in determining the success of thesis projects, which, on the one hand, corroborates previous research. On the other hand, this study extends previous research by pointing out additional specific factors, such as the time supervisors take to complete thesis projects and the ratio of previously unfinished thesis projects. It can also be concluded that the academic title of the supervisor, which was one of the variables studied, did not constitute a factor for completing thesis projects. One of the more novel contributions of this study stems from the application of machine learning algorithms that were used in order to – reasonably accurately – predict thesis completion/non-completion. Such predictive models offer the opportunity to support a more optimal matching of students and supervisors.</p>

More than 400 students write their bachelor's or master's theses each year at the Department of Computer and Systems Sciences, Stockholm University. In order to support self-driven student thesis work and to reduce the burden on supervisors for feedback on basic skills, an IT support system called SciPro was developed. An important consideration in developing this system was to take actions to reduce plagiarism. Both prevention and detection were accomplished with the following: 1) prevention by policy guidelines, FAQ, face-to-face information, peer-reviews and transparency in the process of recurrent online thesis manuscript hand-ins; and 2) detection by automatic originality check of the final manuscript enabled by integration between SciPro and Turnitin. Explicit rules and regulations as well as frequent education about anti-plagiarism targeting both students and supervisors were also important parts of the prevention strategy. Current results include: 1) substantial improvements in policy development; 2) successful integration of anti-plagiarism software; and 3) recurrent educational activities for students and supervisors have raised the awareness of plagiarism issues at the department. Future development includes three new technical approaches in order to manage sophisticated antiplagiarism controls efficiently, with a quality standard not possible by other means, in large-scale thesis production: 1) automated and integrated (SciPro/Turnitin) recurrent anti-plagiarism controls of submitted thesis manuscripts at various stages in the thesis text production process; 2) automated anti-plagiarism controls of thesis texts submitted in SciPro by comparing consistency in style of writing between different versions of thesis manuscripts handed in by the same student during the process of producing the thesis text; and 3) an automated check of thesis manuscripts submitted to SciPro for identification of images/figures/illustrations/graphs copied from the Internet through integration of an image pattern recognition programme. These measures taken together will significantly increase thesis quality by verifying authenticity to a very high degree and systematising the anti-plagiarism procedures. They will also substantially reduce tedious, boring and immensely time consuming manual work for administrators and supervisors who need to guarantee that theses do not contain plagiarised texts or illustrations.

No abstract