Scatterplots have been in use for about two centuries, primarily for observing the relationship between two variables and commonly for supporting correlation analysis. In this paper, we report an empirical study that examines how humans’ perception of correlation using scatterplots relates to the Pearson's product‐moment correlation coefficient (PPMCC) – a commonly used statistical measure of correlation. In particular, we study human participants’ estimation of correlation under different conditions, e.g., different PPMCC values, different densities of data points, different levels of symmetry of data enclosures, and different patterns of data distribution. As the participants were instructed to estimate the PPMCC of each stimulus scatterplot, the difference between the estimated and actual PPMCC is referred to as an offset. The results of the study show that varying PPMCC values, symmetry of data enclosure, or data distribution does have an impact on the average offsets, while only large variations in density cause an impact that is statistically significant. This study indicates that humans’ perception of correlation using scatterplots does not correlate with computed PPMCC in a consistent manner. The magnitude of offsets may be affected not only by the difference between individuals, but also by geometric features of data enclosures. It suggests that visualizing scatterplots does not provide adequate support to the task of retrieving their corresponding PPMCC indicators, while the underlying model of humans’ perception of correlation using scatterplots ought to feature other variables in addition to PPMCC. The paper also includes a theoretical discussion on the cost‐benefit of using scatterplots.

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Recent advances in smart devices and online technologies have facilitated the emergence of ubiquitous learning environments for participating in different learning activities. This poses an interesting question about modality access, i.e., what students are using each platform for and at what time of day. In this paper, we present a log-based exploratory study on learning management system (LMS) use comparing three different modalities—computer, mobile, and tablet—based on the aspect of time. Our objective is to better understand how and to what extent learning sessions via mobiles and tablets occur at different times throughout the day compared to computer sessions. The complexity of the question is further intensified because learners rarely use a single modality for their learning activities but rather prefer a combination of two or more. Thus, we check the associations between patterns of modality usage and time of day as opposed to the counts of modality usage and time of day. The results indicate that computer-dominant learners are similar to limited-computer learners in terms of their session-time distribution, while intensive learners show completely different patterns. For all students, sessions on mobile devices are more frequent in the afternoon, while the proportion of computer sessions was higher at night. On comparison of these time-of-day preferences with respect to modalities on weekdays and weekends, they were found consistent for computer-dominant and limited-computer learners only. We demonstrate the implication of this research for enhancing contextual profiling and subsequently improving the personalization of learning systems such that personalized notification systems can be integrated with LMSs to deliver notifications to students at appropriate times.

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Many universities aim to improve students' 'learning to learn' (LTL) skills to prepare them for post-academic life. This requires evaluating LTL and integrating it into the university's curriculum and assessment regimes. Data is essential to provide evidence for the evaluation of LTL, meaning that available data sources must be connected to the types of evidence required for evaluation. This chapter describes a case study using an LTL ontology to connect the theoretical aspects of LTL with a university's existing data sources and to inform the design and application of learning analytics. The results produced by the analytics indicate that LTL can be treated as a dimension in its own right. The LTL dimension has a moderate relationship to academic performance. There is also evidence to suggest that LTL develops at an uneven pace across academic terms and that it exhibits different patterns in online as compared to face-to-face delivery methods.