Face-to-Face attendance and online engagement in a large Data Analysis module

Business programmes traditionally include a business statistics component that focuses on the application of statistical methods. Data Analysis is the application of statistical techniques to describe and explore a set of data. The aim of Data Analysis for Decision Makers (DADM) is to use data analysis to highlight useful information that aids decision making. DADM is a core module for all (circa 550 per annum) UCD undergraduate business students and is delivered in Semester 1 of first year. Anecdotal evidence suggested that attendance at DADM face-to-face sessions was low. Anecdotal evidence also suggested that students employed a just-in-time approach to their learning in response to assessment tasks. This article describes a project to evaluate attendance and online engagement in the DADM module. We wish to identify which learning resources best aid student learning (as measured by their overall grade). This will inform policy and practice in learning resource investment. Students perceive maths as hard and business students in particular perceive data analysis and statistics as boring and irrelevant to the real world. There is a significant challenge to engage business students with quantitative topics and convince them of the relevance of these topics to their future careers in business. Frameworks, such as the American Statistical Association Guidelines for Assessment and Instruction in Statistics Education (GAISE), provide a set of recommended practices. Experience shows that some students on the business programmes struggle to adapt from a secondary school teacher-led learning environment to a student-centred university learning environment. For this reason, development of learning resources in line with the GAISE recommendations is the focus of this work rather than exploration of more novel approaches such as Expectation Led Planned Organisation. This article describes the DADM learning resources; contact time (lecture and tutorial attendance), eLearning (Blackboard eLearning and online practice questions) and assessment (Continuous assessment and exam) activities. Active Learning (AL) approaches were used during face-to-face contact time. Technology supported learning was available outside contact time and timely assessment during the semester was used as a means to encourage learning. Following ethical approval, attendance and online activity data were gathered in semester 1, 2013/2014. The data are being used to answer the following research questions: 1. Do business students attend their scheduled Data Analysis lectures and tutorials? 2. Do business students engage with online resources in a timely fashion? 3. Does academic performance depend on lecture and tutorial attendance? 4. Does academic performance depend on online engagement? 5. Are there clusters of students who exhibit a preference for a particular mode of engagement? Preliminary analysis shows; • attendance at tutorials is correlated with assessment tasks; • attendance at tutorials is higher than at lecture; • the number of lectures attended affect the grade in a small but significant manner, each attended lecture improves a student overall result by about 0.8% (holding other factors fixed), Analysis of online activity is ongoing.