Scholarly insight Spring 2018: a Data wrangler perspective

In the movie classic Back to the Future a young Michael J. Fox is able to explore the past by a time machine developed by the slightly bizarre but exquisite Dr Brown. Unexpectedly by some small intervention the course of history was changed a bit along Fox’s adventures. In this fourth Scholarly Insight Report we have explored two innovative approaches to learn from OU data of the past, which hopefully in the future will make a large difference in how we support our students and design and implement our teaching and learning practices. In Chapter 1, we provide an in-depth analysis of 50 thousands comments expressed by students through the Student Experience on a Module (SEAM) questionnaire. By analysing over 2.5 million words using big data approaches, our Scholarly insights indicate that not all student voices are heard. Furthermore, our big data analysis indicate useful potential insights to explore how student voices change over time, and for which particular modules emergent themes might arise. In Chapter 2 we provide our second innovative approach of a proof-of-concept of qualification path way using graph approaches. By exploring existing data of one qualification (i.e., Psychology), we show that students make a range of pathway choices during their qualification, some of which are more successful than others. As highlighted in our previous Scholarly Insight Reports, getting data from a qualification perspective within the OU is a difficult and challenging process, and the proof-of-concept provided in Chapter 2 might provide a way forward to better understand and support the complex choices our students make. In Chapter 3, we provide a slightly more practically-oriented and perhaps down to earth approach focussing on the lessons-learned with Analytics4Action. Over the last four years nearly a hundred modules have worked with more active use of data and insights into module presentation to support their students. In Chapter 3 several good-practices are described by the LTI/TEL learning design team, as well as three innovative case-studies which we hope will inspire you to try something new as well. Working organically in various Faculty sub-group meetings and LTI Units and in a google doc with various key stakeholders in the Faculties, we hope that our Scholarly insights can help to inform our staff, but also spark some ideas how to further improve our module designs and qualification pathways. Of course we are keen to hear what other topics require Scholarly insight. We hope that you see some potential in the two innovative approaches, and perhaps you might want to try some new ideas in your module. While a time machine has not really been invented yet, with the increasing rich and fine-grained data about our students and our learning practices we are getting closer to understand what really drives our students

Early evaluation of Unistats: user experiences

This paper sets out the findings of the user evaluation of Unistats.UK Higher Education Funding Bodie

A Student Retention Model: Empirical, Theoretical and Pragmatic Considerations

This research-in-progress paper draws on an extensive body of literature related to student retention. The purpose of this study is to develop a student retention model utilising student demographic data and a combination of data from student information systems, course management systems and other similar tools to accurately predict academic success of students at our own institution. Our research extends Tinto’s model by incorporating a number of components from Bean’s, Astin’s and Swail’s model. Our proposed eclectic model consists of seven components, identified as determinants of student retention. The strength in the model lies in its ability to help institutions work proactively to support student retention and achievement. The proposed research methodology to be used in this study is “a mixed-methods concurrent triangulation strategy”. The results are expected to indicate which of the factors are most important in developing an information system to predict and suggest interventions to improve retention

Educational data mining for tutoring support in Higher Education: a web-based tool case study in engineering degrees

This paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students' performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students' data help an analyst to discover patterns. The coordinated visualization of aggregated students' performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students' behavior at different degrees. The analysis of the impact of the student's explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios.This work was supported by the Erasmus+ Key Action 2 Strategic Partnerships KA203, funded by the European Commission, under Grant 2016-1-ES01-KA203-025452.info:eu-repo/semantics/publishedVersio

Analytical Collaboration for Student Graduation Success: Relevance of Analytics to Student Success in Higher Education

Institutional research (IR) experts can offer a wealth of knowledge to many campus departments, such as assessment. Utilization of dashboard techniques is a good way to improve the quality of university information that is available to support assessment research and do so in an efficient way. Institutional research at Wayne State University implemented a Graduation and Retention Tracking dashboard to assist in improving graduation rates of its students facing challenges from academic and financial holds and saw a significant increase in its graduation rate from a low of 26% to 47%. Strong communication and collaboration is needed to develop a dashboard that meets university requirements while also helping to answer important questions related to assessment projects. These collaborations resulted in cooperation across campus lines essential to important progress toward cross-cutting issues of retention and graduation