No abstract

Many undergraduate students are required to study statistics, but often struggle understanding concepts, lack engagement, lack confidence, or feel anxious about statistics. Kahoot is a game-based learning platform that can be used to increase student engagement and learning through real-time quizzes. This study aimed to evaluate the use of Kahoot on improving students’ experience of studying statistics in an undergraduate (year 2) course. Pre and post Likert scale questionnaires (including Statistical Anxiety Measure - SAM) were used to collect student responses about their statistics study experience. Questions related to anxiety, confidence, and for the post quiz, additional questions on the impact of Kahoot on behavioural engagement. Post survey results indicate positive changes in students’ perceptions towards studying statistics in terms of anxiety and confidence. Kahoot was shown to have a significant and positive effect on student confidence and was also linked to lowered anxiety. Despite limited data, help-seeking anxiety explained over 50% of variation in final exam performance. Further research is recommended on the effect of Kahoot on student anxiety when studying statistics, particularly as it relates to confidence and performance.

Statistics is a widely taught subject in Higher Education but for many students, anxiety about statistics interferes with the learning process. Statistics anxiety workshops to help students understand and reduce statistics anxiety were developed by the authors and in 2020/21 delivered collaboratively and remotely with specific cohorts of students at three institutions. Prior to the workshops, all students within the targeted cohorts were asked to complete a survey which included measures of statistics anxiety, and asked if they were interested in attending the voluntary workshop. This enabled a comparison of the characteristics of groups who were interested or not. The workshops successfully attracted the targeted students, since those attending had higher overall statistics anxiety, software and maths anxiety, and anxiety around learning statistics. However, students with higher help seeking anxiety were less likely to attend.

Sensitivity of eigenvectors and eigenvalues of symmetric matrix estimates to the removal of a single observation have been well documented in the literature. However, a complicating factor can exist in that the rank of the eigenvalues may change due to the removal of an observation, and with that so too does the perceived importance of the corresponding eigenvector. We refer to this problem as "switching of eigenvalues". Since there is not enough information in the new eigenvalues post observation removal to indicate that this has happened, how do we know that this switching has occurred? In this paper we show that approximations to the eigenvalues can be used to help determine when switching may have occurred. We then discuss possible actions researchers can take based on this knowledge, for example making better choices when it comes to deciding how many principal components should be retained and adjustments to approximate influence diagnostics that perform poorly when switching has occurred. Our results are easily applied to any eigenvalue problem involving symmetric matrix estimators. We highlight our approach with application to a real data example.