Abstract:

Universities and university teachers are strenuously aiming at ensuring students’ learning and study success, and by this, achieving high retention rates. Not only the didactically meaningful preparation and presentation of contents and the application-oriented design of courses guarantee students’ success, but also an individual and motivating teacher support for students is required [1]. However, it is particularly the support for students which presents a challenge to university teachers, especially in mass lectures, where teachers, on the one hand side, do not even know the students individually, and, on the other hand side, do not have the time to personally discuss questions or exam results with every student.

Bearing this in mind, in our project [project name anonymized] we develop a so-called Workbench (MWB). We consider this as a solution to combine the established infrastructures with innovative applications and tools. This supports mentoring in a way that allows both, university teachers and students to stick to their routines by using their well-known Learning Management System (LMS) alongside specialised tools for delivering or gaining individual assistance and feedback. Thus, particularly students do not have to use new or different tools and can benefit from changes that will appear to their learning activities and progress instead. The design and implementation of the MWB, firstly, aim to bring together data from different systems, secondly, intend to generate feedback externally, and, thirdly, shows this feedback to students as well as teachers in the existing LMS.

Therefore, the MWB contains various interactive mentoring elements that aims to increase learning motivation and learning success, as well as to support digital teaching and self-regulated learning.

The article outlines the results of an initial needs and requirements analysis that was conducted to contribute to the technological expansion of the previously established interactions and tools of the MWB in terms of didactical, technological and organizational aspects. Based on user surveys with teachers, learners and administrative staff at the [university anonymized], target group-specific empirical values and further perspectives of usage were discovered to create an overview of the user’s needs.

The results are the basis for further developments of the MWB and the interactions between the systems within AI-supported mentoring in university teaching. In addition, the development potential and the further steps that are necessary to support university teaching in the future using digital tools of self-learning artificial intelligence are shown.

References:
[1] M. Bülow-Schramm, M. Merkt, and H. Rebenstorf, “Studienerfolg aus Studierendensicht–Ergebnisse der ersten Erhebungswelle des Projekts USuS,” in Der Bologna-Prozess aus Sicht der Hochschulforschung: Analysen und Impulse für die Praxis, Arbeitspapier Nr.148, CHE, pp. 167–177, 2011.

Keywords:

Mentoring, Artificial intelligence, Higher Education, Technology Enhanced Learning, Learning Management System.