More than 3 million students study outside their home country, primarily at a Western university. A common belief among educators is that international students are insufficiently adjusted to higher education in their host country, both academically and socially. Furthermore, several groups of international students experience considerable amounts of stress while adapting to the culture of the host-institute. Several researchers argue that studies on adaptation of international students should widen its focus to the underlying mechanisms that leads towards this ''misalignment''. In a cross-institutional comparison among 958 students at five business schools in the Netherlands, differences in academic performance between local and international students were identified by focussing on their levels of academic and social integration. Students' academic integration was measured with the Students' Adaptation to College Questionnaire (SACQ), while students' social integration was measured with a newly developed and validated -011-9468-1 questionnaire. The results indicate that the degree of academic success of international students is multi-faceted. International students with a (mixed) western ethnic background perform well on both academic and social integration, and also attained higher studyperformance in comparison to domestic students. In contrast, international students with a non-Western background are less integrated compared to other international students. Nevertheless, they have a similar study-performance. Finally, academic adjustment is the main predictor of study-performance for Dutch, Western and Mixed-Western students. Social adjustment was negatively related to study-performance. The lack of fit for predicting long-term study success of non-Western students indicates that their academic and social integration processes are more complex and non-linear.123 High Educ (2012) 63:685-700 DOI 10.1007/s10734

Abstract. Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick's theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In a large introductory quantitative methods module, 922 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation.

World-wide, universities in health sciences have transformed their curriculum to include collaborative learning and facilitate the students’ learning process. Interaction has been acknowledged to be the synergistic element in this learning context. However, students spend the majority of their time outside their classroom and interaction does not stop outside the classroom. Therefore we studied how informal social interaction influences student learning. Moreover, to explore what really matters in the students learning process, a model was tested how the generally known important constructs—prior performance, motivation and social integration—relate to informal social interaction and student learning. 301 undergraduate medical students participated in this cross-sectional quantitative study. Informal social interaction was assessed using self-reported surveys following the network approach. Students’ individual motivation, social integration and prior performance were assessed by the Academic Motivation Scale, the College Adaption Questionnaire and students’ GPA respectively. A factual knowledge test represented student’ learning. All social networks were positively associated with student learning significantly: friendships (β = 0.11), providing information to other students (β = 0.16), receiving information from other students (β = 0.25). Structural equation modelling revealed a model in which social networks increased student learning (r = 0.43), followed by prior performance (r = 0.31). In contrast to prior literature, students’ academic motivation and social integration were not associated with students’ learning. Students’ informal social interaction is strongly associated with students’ learning. These findings underline the need to change our focus from the formal context (classroom) to the informal context to optimize student learning and deliver modern medics.

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Please cite as:Rienties, B., Brouwer, N., Lygo-Baker, S. (2013) .