Learning analytics and digital badges are emerging research fields in educational science. They both show promise for enhancing student retention in higher education, where withdrawals prior to degree completion remain at about 30 % in Organisation for Economic Cooperation and Development member countries. This integrative review provides an overview of the theoretical literature as well as current practices and experience with learning analytics and digital badges in higher education with regard to their potential impact on student retention to enhance students' first-year experience. Learning analytics involves measuring and analyzing dynamic student data in order to gain insight into students' learning processes and optimize learning and teaching. One purpose of learning analytics is to construct predictive models to identify students who risk failing a course and thus are more likely to drop out of higher education. Personalized feedback provides students with information about academic support services, helping them to improve their skills and therefore be successful in higher education. Digital badges are symbols for certifying knowledge, skills, and competencies on web-based platforms. The intention is to encourage student persistence by motivating them, recognizing their generic skills, signaling their achievements, and capturing their learning paths. This article proposes a model that synthesizes learning analytics, digital badges, and generic skills such as academic competencies. The main idea is that generic skills can be represented as digital badges, which can be used for learning analytics algorithms to predict student success and to provide students with personalized feedback for improvement. Moreover, this model may serve as a platform for discussion and further research on learning analytics and digital badges to increase student retention in higher education.

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Purpose The purpose of this paper is to examine the expectations, perceptions and role understanding of academic staff using a model of academic competencies (i.e. time management, learning skills, technology proficiency, self-monitoring and research skills). Design/methodology/approach Semi-structured interviews were conducted with ten members of academic staff at a German university. Participants’ responses to the open-ended questions were coded inductively, while responses concerning the proposed model of academic competencies were coded deductively using a priori categories. Findings Participating academic staff expected first-year students to be most competent in time management and in learning skills; they perceived students’ technology proficiency to be rather high but their research skills as low. Interviews indicated a mismatch between academic staff expectations and perceptions. Practical implications These findings may enable universities to provide support services for first-year students to help them to adjust to the demands of higher education. They may also serve as a platform to discuss how academic staff can support students to develop the required academic competencies, as well as a broader conversation about higher education pedagogy and competency assessment. Originality/value Little research has investigated the perspectives of academic staff concerning the academic competencies they expect of first-year students. Understanding their perspectives is crucial for improving the quality of institutions; their input into the design of effective support services is essential, as is a constructive dialogue to identify strategies to enhance student retention.

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