Learning analytics represent a promising approach for fostering personalized learning processes. Most applications of this technology currently do not use textual data for providing information on learning, or for deriving recommendations for further development. This paper presents the results of three studies aiming to make textual information usable. In the first study, the iota concept is introduced as a new content analysis measure to evaluate inter-coder reliability. The main advantage of this new concept is that it provides a reliability estimation for every single category, allowing deeper insight into the quality of textual analysis. The second study simulates the process of content analysis, comparing the new iota concept with well-established measures (e.g., Krippendorff’s Alpha, percentage agreement). The results show that the new concept covers the true reliability of a coding scheme, and is not affected by the number of coders or categories, the sample size, or the distribution of data. Furthermore, cut-off values are derived for judging the quality of the analysis. The third study employs the new concept, as it analyzes the performance of different artificial intelligence (AI) approaches for interpreting textual data based on 90 different constructs. The texts used here were either created by apprentices, students, and pupils, or were taken from vocational textbooks. The paper shows that AI can reliably interpret textual information for learning purposes, and also provides recommendations for optimal AI configuration.

Achieving a sustainable economic system is a key challenge facing society. However, sustainable business to date has been only minimally considered when it comes to the requisites and curricula of business trainees. It generally has been left up to schools and teachers to provide their students with sustainable business skills. This involves creating teaching and training that effectively harmonize with learner requirements. To support teachers in this process, the following develops a sustainability-oriented innovation competence typology using a latent profile analysis based on data gathered from 1,149 business trainees who were in the first, second, or third year of their apprenticeship. This typology can be used to plan and develop classroom teaching. Competency assessment was done using a multiple-choice test along with a questionnaire to determine students’ beliefs about sustainable development. The latent profile analysis revealed six groups of learner competence profiles, each of which require specific teaching when it comes to achieving sustainable innovation skills. Based on these, the following paper develops recommendations for specific teaching methods and lessons that effectively promote business trainee sustainability-oriented innovation competence, while at the same time including their specific requirements into teaching.

Learning analytics represent a promising approach for fostering personalized learning processes. Most applications of this technology currently do not use textual data for providing information on learning, or for deriving recommendations for further development. This paper presents the results of three studies aiming to make textual information usable. In the first study, the iota concept is introduced as a new content analysis measure to evaluate inter-coder reliability. The main advantage of this new concept is that it provides a reliability estimation for every single category, allowing deeper insight into the quality of textual analysis. The second study simulates the process of content analysis, comparing the new iota concept with well-established measures (e.g., Krippendorff’s alpha, percentage agreement). The results show that the new concept covers the true reliability of a coding scheme, and is not affected by the number of coders or categories, the sample size, or the distribution of data. Furthermore, cut-off values are derived for judging the quality of the analysis. The third study uses the new concept, analyzing the performance of different artificial intelligence (AI) approaches for interpreting textual data based on 90 different constructs. The texts used here were either created by apprentices, students, and pupils, or were taken from vocational textbooks. The paper shows that AI can reliably interpret textual information for learning purposes, and also provides recommendations for optimal AI configuration.

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