This empirical study investigates students' learning choices for mathematics and statistics in a blended learning environment, composed of both online and face-to-face learning components. The students (N = 730) were university freshmen with a strong diversity in prior schooling and a wide range of proficiency in quantitative subjects. In this context, we investigated the impact that individual differences in achievement emotions (enjoyment, anxiety, boredom, hopelessness) had on students' learning choices, in terms of the intensity of using the online learning mode versus the face-to-face mode. Unlike the general level of learning activities, which is only minimally influenced by achievement emotions, these emotions appear to have a moderately strong effect on a student's preference for online learning. Following this, we explored the antecedents of achievement emotions. Through the use of path-modeling, we conclude that while goal setting behavior only marginally impacts achievement emotions, effort views—a crucial component of the social-cognitive model of implicit theories of intelligence—have a substantial impact on achievement emotions.
LA/EDM research results indicate that data integration from multiple sources can improve the accuracy of a learner profile and subsequent adaptation and personalization of content. Exploration of students’ behavior within educational contexts that support multimodality and mobility could lead to shaping a holistic picture of how, when and where learning occurs.
The paper reports an examination of the literature on experimental case studies conducted in the domain from 2008 to 2013. Although the search terms identified 209 mature pieces of research work, the inclusion criteria limited the key studies to 40. The research questions, methodology and findings of these published papers were analysed and categorized. The authors used non-statistical methods to evaluate and interpret findings of the collected studies. Their results highlighted four distinct major directions for learning analytics / educational data mining empirical research. The paper also discusses the value added by this research