Automatic generation of inter-passage links based on semantic similarity

This paper investigates the use and the prediction potential of semantic similarity measures for automatic generation of links across different documents and passages. First, the correlation between the way people link content and the results produced by standard semantic similarity measures is investigated. The relation between semantic similarity and the length of the documents is then also analysed. Based on these findings a new method for link generation is formulated and tested

Analysing performance of first year engineering students

Many students in the engineering disciplines do not complete their higher education degree and drop out. This problem is serious, especially for first-year university students. In this paper, we analyse how students earn the credits required for their successful completion of the first study year. Using the example of a European technical university with traditional classroom-based education, we identify three groups of students: those who pass, those who earn only enough credits for staying in the program, and those who fail. Important patterns can be found at the end of the first semester. We present a simple algorithm that identifies students who may benefit from early additional support, which would increase their chances of progression to the second year and improve the retention improvement for the university. The results are evaluated in four consecutive academic years. The data from years 2013/14 and 2014/15 have been used to develop and verify the prediction model. In study years 2015/16 and 2016/17 the model has been applied to predict at-risk students, where the university tutors intervened and provided additional support and a significant improvement was achieved

Evaluating Weekly Predictions of At-Risk Students at The Open University: Results and Issues

Improving student retention rates is a critical task not only for traditional universities but particularly in distance learning courses, which are in recent years rapidly gaining in popularity. Early indications of potential student failure enable the tutor to provide the student with appropriate assistance, which might improve the student’s chances of passing the course. Collated results for a course cohort can also assist course teams to identify problem areas in the educational materials and make improvements for future course presentations. Recent work at the Open University (OU) has focused on improving student retention by predicting which students are at risk of failing. In this paper we present the models implemented at the OU, evaluate these models on a selected course and discuss the issues of creating the predictive models based on historical data, particularly mapping the content of the current presentation to the previous one. These models were initially tested on two courses and later extended to ten courses

Visualisation of key splitting milestones to support interventions

The paper presents an approach to help staff responsible for running courses by identifying key milestones in the educational process, where the paths of successful and unsuccessful students started to split. By identifying these milestones in the already finished courses, this information can be used to plan the interventions in the next runs. This is achieved by finding the earliest time when the differences in behaviour or key performance metrics of unsuccessful students start to become significant. We demonstrate this approach in two case studies, one focused on a course level analysis and the latter on a whole academic year. This suggests its generic nature and possible applicability in various Learning Analytics scenarios

Sensitivity analysis of tangential digging forces of the bucket wheel excavator SchRs 1320 for different terraces

Researches in the field of economy of control of mining processes of minerals are continually met with a number of unsolved and newly arising problems. New deposits of brown coal are located at greater depths than before and are more difficult to mine. Problems in the field of rock mining stem from the greater diversity of rocks in mining, and it results in more difficult mining conditions. On the other hand, even greater demands are made to decrease the economic demand of mining. Current research is aimed at the most optimal utilization of energy provided on the bucket wheel of excavators, such that the energy demand of the mining process decreases, whilst preserving maximum performance. These requirements place greater demands on the precision of planning of mining and maximum economic efficiency. The presented article illustrates the possibilities of the utilization of Sobol sensitivity analysis during the investigation of the influence of the parameters of mining processes on tangential digging forces. Analyses are carried out for the mining process of bucket wheel excavator SchRs 1320. For purposes of the study, detailed measurements of operational parameters were performed on the excavator during a whole work cycle

Investigating Influence of Demographic Factors on Study Recommenders

Recommender systems in e-learning platforms, can utilise various data about learners in order to provide them with the next best material to study. We build on our previous work, which defines the recommendations in terms of two measures (i.e. relevance and effort) calculated from data of successful students in the previous runs of the courses. In this paper we investigate the impact of students’ socio-demographic factors and analyse how these factors improved the recommendation. It has been shown that education and age were found to have a significant impact on engagement with materials

INTEGRATED MICROFLUIDIC DEVICE FOR DROPLET MANIPULATION

Droplets based microfluidic systems have a big potential for the miniaturization of processes for bioanalysis. In the form of droplets, reagents are used in discrete volume, enabling high-throughput chemical reactions as well as single-cell encapsulation. Microreactors of this type can be manipulated and applied in bio-testing. In this work we present a platform for droplet generation and manipulation by using dielectrophoresis force. This platform is an integrated microfluidic device with a dielectrophoresis (DEP) chip. The microfluidic device generates microdroplets such as water in oil emulsion

Developing predictive models for early detection of at-risk students on distance learning modules

Not all students who fail or drop out would have done so if they had been offered help at the right time. This is particularly true on distance learning modules where there is no direct tutor/student contact, but where it has been shown that making contact at the right time can improve a student’s chances. This paper explores the latest work conducted at the Open University, one of Europe’s largest distance learning institutions, to identify when is the optimum time to make student interventions and to develop models to identify the at-risk students in this time frame. This work in progress is taking real time data and feeding it back to module teams as the module is running. Module teams will be indicating which of the predicted at-risk students have received an intervention, and the nature of the intervention