Keywords: R in education, Learning patterns, Learning styles The world of education is changing more than ever. In the university of the 21st century, there is no room for one-way education with a summative evaluation at the end of the teaching period. Instead, there is a need for formative assessment, including frequent and individual feedback (Lindblom Ylanne and Lonka 1998). However, when the number of students is large, providing individual feedback requires a huge amount of effort and time. This effort is intensified when the subject matter taught allows for a certain flexibility to solve problems. Although data analysis can provide useful insights about learning styles and patterns of students both at the time of learning and afterwards, this abstract shows that it can also be leveraged for providing fast feedback. This abstract both incorporates a procedure to provide large-scale (semi-)individual feedback by using systematic assignments, as well as insights into different learning styles by combining information on the assigments and the final scores of the students. The case used is a course on explorative data analysis (EDA) taught to a group of circa 80 first year business engineering students at Hasselt University, covering a diverse set of topics such as data manipulation, visualization, import and tidying. During the course, students have to complete assignments on a regular interval in order to fully administer the new skills, each arranged around a specific topic. These assignments come in the form of Rmarkdown files in which the students have to complete R-chunks appropriately. Each Rmarkdown file is then re-run by the education team, and the data generated for each student is used for evaluation. Each problem which the students have to solve in these assignments is labelled by the education team with the principles it assesses. For example, in the case of visualization, it might have to do with using appropriate aestethics, appropriate geoms, appropriate context (e.g. titles, labels), etc. By mapping these labels and the scores of the student, a precise learning profile for each student can be constructed which indicates his weaknesses and his strenghts (Vermunt and Vermetten 2004). By using this information, students can be clustered in different groups, which can then be addressed with tailored feedback on their progress and pointers to useful additional exercises in order to remedy those areas in which they perform less good. In a second step, an ex post analysis can be done by combining the learning profiles created with the final grades and possibly other information such as educational background. This information can be employed to find which group of students represent problem cases, i.e. having a high probability of failing for the course. These insights can proof useful in future editions of the course, as a mechanism for rapid identification of students who might have difficulties with certain concepts. Moreover, it can be used to adapt the course, such that certain concepts which proof the be problematic are highlighted in a different or in a more comprehensive manner throughout the course (Tait and Entwistle 1996). References Lindblom Ylanne, Sari, and Kirsti Lonka. 1998. “Individual Ways of Interacting with the Learning Environment. Are They Related to Study Success?” Learning and Instruction 9 (1). Elsevier: 1–18.
Tait, Hilary, and Noel Entwistle. 1996. “Identifying Students at Risk Through Ineffective Study Strategies.” Higher Education 31 (1). Springer: 97–116.
Vermunt, Jan D, and Yvonne J Vermetten. 2004. “Patterns in Student Learning: Relationships Between Learning Strategies, Conceptions of Learning, and Learning Orientations.” Educational Psychology Review 16 (4). Springer: 359–84.
