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.

Summer vacations interrupt the rhythm of learning and may result in a loss of knowledge and skills. This study investigates summer learning losses in an Austrian sample with nine-week summer vacations. The results show losses as well as gains for students in lower secondary education (182 students between 10 and 12 years old). Students experienced losses in arithmetic problem solving (measured by the HAWIK IV intelligence test) and spelling (measured by the standardized spelling test HSP 5-9), but gains in reading (measured by the Salzburg Reading-Screening, SLS-8). Losses or gains in a knowledge domain appear to depend on the degree of practice during the summer vacation. Contrary to American studies, students could make up for their losses within nine weeks following the re-start of school. In addition, socio-economic variables such as the mother's educational background had a small impact on summer learning losses in arithmetic problem solving.

The Thinking Styles Inventories (TSI) are questionnaires for assessing individual preferences in constructing knowledge. This paper identifies several problems concerning their validity, which range from an inadequate use of factor analysis, to missing information on the measurement model, to findings indicating a low discrimination between the thinking style scales. Against this background, two studies are conducted providing detailed insights into the measurement model of the TSI in German-speaking samples (Study I: 287 apprentices; Study II: 389 students). Although results indicate a high degree of reliability according to popular statistical rules, they confirm problems with the discriminant validity and criterion validity regarding achievement. The Thinking Styles Inventories should as a result be used with caution.

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