Abstract Preservice teachers (PSTs) enter teacher education programs with years of experience as students in the classroom (Lortie, Schoolteacher: A sociological study, University of Chicago, Chicago, 1975). Teacher educators aim to understand PSTs’ unique experiences and support their learning of pedagogy within and across various contexts, including content methods coursework and clinical experiences. Research indicates that coherence between these contexts is crucial (Cochran-Smith & Zeichner, Studying teacher education: The report of the AERA panel on research and teacher education, Routledge, 2009). Our study examined how PSTs conceptualized pedagogical approaches using animation and whether the approach enacted changed across content methods coursework and clinical experiences. The results reveal a discrepancy in teaching approaches, highlighting the influence of clinical experience on PST learning and underscoring the pivotal role of teaching beliefs in shaping views on teaching mathematics and science.

This study focuses on learning analytics from the perspective of elementary and secondary classroom teachers (grades one to nine). The aim is to explore teachers’ perceptions about the use of learning analytics, the challenges and opportunities associated with the tools, and the future of the analytics. The research is based on qualitative data: open-ended responses from 144 teachers. Analysis was conducted using qualitative content analysis and latent class analysis. The results highlight the prominent role of simple drill and practice applications with dashboards and differing teacher perceptions of analytics. Learning analytics helps teachers better understand class learning activities, focus their attention and attain professional development goals. Challenges discussed include the need for new skill development, increased workloads and pedagogical limitations of the current technology. The study shows the necessity for more versatile learning analytic tools with diverse pedagogical practices. It also demonstrates that teachers’ skills must be enhanced to use analytics for pedagogically sound actions.

Abstract Knowledge tracing (KT) refers to the process of efficiently tracking student achievement in online learning systems. Bayesian knowledge tracing (BKT) is a representative statistical model used for this process. Despite the widespread application of BKT in predicting student performance and modeling knowledge acquisition, methods to evaluate the consistency of measurement by BKT models remain elusive. In psychometrics, reliability is a fundamental concept that gauges the degree to which an assessment tool generates stable and consistent results. Evaluation of reliability is crucial because, without reliable measurement, the adequacy of educational assessments and the decisions based on them are compromised. To address the lack of a method to measure reliability, we propose a novel approach for estimating the reliability coefficient by extending the existing method for diagnostic classification models to time-series data. We apply the proposed method to actual response data and demonstrate its capability of assessing the reliability of BKT models, highlighting the importance of evaluating reliability and its potential for improving individualized learning experiences.

Validated instruments enable strategic decision-making in an increasingly complex technological environment. Higher Education Institutions (HEIs) should have data measuring technological maturity and readiness for cyber-physical environments, crucial to leading digital transformation and sustainable development. This study presents the validation of an instrument designed to assess academicians' perceptions of the technological maturity and digital transformation readiness of HEIs. The scale dimensions were constructed and validated in three steps: (1) operationalization of the study variables and scale development, (2) content validation through a nine-expert judgment panel with Aiken's V, and (3) factor exploratory analysis (EFA) and reliability analysis using Cronbach's Alpha method. The validation yielded three main findings: Aiken’s V coefficient yielded a value of (> 0.82), indicating substantial agreement for the content evaluation of nine experts; reliability testing produced a Cronbach’s alpha of .957, demonstrating excellent internal consistency. The Kaiser–Meyer–Olkin (KMO) measure was .919, confirming the questionnaire's suitability for 19 items. Four critical dimensions were established: (1) Cyber-Physical Systems, (2) Educational platforms and data and analytics, (3) Organizational platforms, and (4) Continuity and security plans. These results validate the instrument as a robust tool for diagnosing digital maturity in university contexts, effectively capturing the proposed dimensions essential for educational innovation. The future work to evolve this instrument could shed light on the roadmap for incorporating technological enablers, aligning vocational training with the challenges of Industry 4.0, and supported by predictive AI models that allow the creation of solid governance to lead the digital evolution.

In recent years, the emphasis on computational thinking (CT) has intensified as an effect of accelerated digitalisation. While most researchers are concentrating on defining CT and developing tools for its instruction and assessment, we focus on the characteristics of computational thinking problems (CTPs)- activities requiring CT to be solved-and how they influence the skills students can develop. In this paper, we present a comprehensive framework for systematically profiling CTPs by identifying specific components and characteristics, while establishing a link between these attributes and a structured catalogue of CT competencies. The purposes of this framework are (i) facilitating the analysis of existing CTPs to identify which abilities can be developed or measured based on their inherent characteristics, and (ii) guiding the design of new CTPs targeted at specific skills by outlining the necessary characteristics required for CT activation. To illustrate the framework functionalities, we begin by analysing prototypical activities in the literature, a process that leads to the definition of a taxonomy of CTPs across various domains, and we conclude with a case study on the design of a different version of one of these activities, the Cross Array Task (CAT), set in different cognitive environments. This approach allows an understanding of how CTPs in different contexts display unique and recurring characteristics that promote the development of distinct skills. In conclusion, this framework can inform the development of assessment tools, improve teacher training, and facilitate the analysis and comparison of existing CT activities, contributing to a deeper understanding of competency activation and guiding curriculum design in CT education.

Pre-service teacher (PST) training in Germany largely fails to prepare prospective teachers adequately for the subject-specific use of digital media. In order to promote their digital competences as well as their intention to use digital media, teacher training needs to adapt. Utilizing Theory of Planned Behavior (TPB), this study aims to identify beliefs on the subject-specific use of different digital media in biology classroom. Within a weekly course on different scientific inquiry methods, pre-service biology teachers used various digital media and wrote reflections on advantages and disadvantages of the digital media they used. Through analysis of these written reflections (n = 85) it was possible, to differentiate the TPB constructs into 38 subcategories. Across all media, we were able to identify most beliefs in the area of behavioral and control beliefs, while normative beliefs and self-efficacy expectations were rarely expressed. At the level of the inductive categories, however, the beliefs were often expressed with different frequencies depending on the medium used. The results allow a more subject-specific differentiation of TPB’s constructs for digital media use in order to design a measuring instrument to evaluate biology teacher training courses. In addition, the results indicate that PSTs’ intention to use digital media in the biology classroom also differs depending on the medium used. These findings provide actionable insights for the design of teacher training programs that specifically target beliefs, i.e. by confronting PSTs with a variety of different digital tools, to enhance the effective use of digital media in biology classrooms.