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The purpose of the study was to test the usability of the MyLA app prototype by its potential users. Furthermore, the Web app will be introduced in the framework of “Mobile Learning Analytics”, a cooperation project between the Cooperative State University Mannheim and University of Mannheim. The participating universities focus on the support of personalized and self-regulated learning. MyLA collects data such as learning behavior, as well as personality traits. Last but not least, the paper will contribute to the topic of learning analytics and mobile learning in higher education.

For the empirical investigation, a mixed-method design was chosen. While 105 participants took part in the conducted online survey, after testing the app prototype, seven students joined an additional eye tracking study. For the quantitative part, a selected question pool from HIMATT (highly integrated model assessment technology and tools) instrument was chosen. The eye tracking investigation consisted of three tasks the participants had to solve.

The findings showed that the students assessed the idea of the app, as well as the navigation positively. Only the color scheme of the prototype was not very attractive to a noticeable amount of the participants. So, it requires slight modifications concerning the app design. For the eye tracking study, it can be stated that the students viewed the relevant parts, and they basically had no difficulties to solve the tasks.

Due to the empirical testing of the app prototype, the project team was able to adjust the application and to add further features. Furthermore, the backend was programmed and an additional tool (MyLA dashboard) was developed for lecturers. A mutual understanding of the targets, privacy issue and relevant features are indispensable for further development of the project.

This study aims to focus on providing a computerized classification testing (CCT) system that can easily be embedded as a self-assessment feature into the existing legacy environment of a higher education institution, empowering students with self-assessments to monitor their learning progress and following strict data protection regulations. The purpose of this study is to investigate the use of two different versions (without dashboard vs with dashboard) of the CCT system during the course of a semester; to examine changes in the intended use and perceived usefulness of two different versions (without dashboard vs with dashboard) of the CCT system; and to compare the self-reported confidence levels of two different versions (without dashboard vs with dashboard) of the CCT system.

A total of N = 194 students from a higher education institution in the area of economic and business education participated in the study. The participants were provided access to the CCT system as an opportunity to self-assess their domain knowledge in five areas throughout the semester. An algorithm was implemented to classify learners into master and nonmaster. A total of nine metrics were implemented for classifying the performance of learners. Instruments for collecting co-variates included the study interest questionnaire (Cronbach’s a = 0. 90), the achievement motivation inventory (Cronbach’s a = 0. 94), measures focusing on perceived usefulness and demographic data.

The findings indicate that the students used the CCT system intensively throughout the semester. Students in a cohort with a dashboard available interacted more with the CCT system than students in a cohort without a dashboard. Further, findings showed that students with a dashboard available reported significantly higher confidence levels in the CCT system than participants without a dashboard.

The design of digitally supported learning environments requires valid formative (self-)assessment data to better support the current needs of the learner. While the findings of the current study are limited concerning one study cohort and a limited number of self-assessment areas, the CCT system is being further developed for seamless integration of self-assessment and related feedback to further reveal unforeseen opportunities for future student cohorts.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.