The reciprocal associations between social media use, political expression, and political participation are central to communication scholars. The cross-lagged panel model (CLPM) represents a common and widely advocated analytic approach to test these relationships. However, it fails to separate within- from between-person effects. In this paper, we propose a random intercept cross-lagged panel model (RI-CLPM) disaggregating within-person and between-person effects. Using three-wave panel data, we demonstrate positive associations between social media use, political expression and online as well as offline participation consistently across waves using the CLPM. However, these relations could not be observed at the within-person effects level with the RI-CLPM. This suggests that the associations between social media use, political expression and political participation are mainly driven by trait-like differences and not by individual changes over time, fundamentally challenging some of the key conclusions of previous research. Implications for communication scholarship are discussed.
Critical rationalism emphasizes scrutiny and learning from errors as essential for advancing knowledge and human problem-solving. Learning from errors is also a major concern in error management with the aim of maximizing the positive effects of errors. Against this background, this paper explores the potential adoption of a critical rationalist perspective in error management. To this end, the paper examines whether error management is suitable for reflecting the major tenets of a critical rationalist perspective in problem-solving. The analysis reveals significant correspondences between error management and critical rationalism, including conceptualization and recognition of errors, and learning through trial and error. Moreover, the paper discusses contributions and challenges that adopting the “philosophical lens” of critical rationalism for error management may entail. The findings are relevant from various respects – in a more practical sense, for example, to take precautions that encourage organizational members’ critical attitude, to impede blaming, or to follow the concept of small losses, aligning with Popper’s piecemeal engineering. In a more conceptual sense, the discussion reveals that the innate dynamics of learning and unlearning that the critical rational perspective imposes may be a major issue not only for organizational members but also, by raising questions about the appropriate and acceptable levels of dynamics in organizations.
Teachers’ mathematics-related beliefs have a substantial influence on their teaching. Given the variety of opportunities for teaching mathematical modeling supported by digital tools, related beliefs are crucial for classroom implementation. In this paper, we introduce a course that aims to prepare future mathematics teachers for teaching mathematical modeling in school using digital tools (focusing here on MathCityMap as an example). We used linear mixed models to analyze the development of 54 preservice teachers’ beliefs over the course, comparing them with 58 mathematics preservice teachers who did not attend the course. Results show an increase in course participants’ self-concepts about digital tools and mathematical modeling, as well as a tendency toward a more dynamic and less static mathematical worldview. There are hints that different course units contribute differently to the development of beliefs. These results suggest that such a course can provide a foundation for teachers to integrate digitally supported mathematical modeling into the classroom.
Our research project aims to integrate inferential statistics into the Hungarian secondary school curriculum. Between 2019 and 2023, we developed and tested an experimental curriculum incorporating simulations and Excel-based calculations. This approach addresses broader challenges in understanding statistical inference and presents our strategies for designing an experimental seminar for in-service teachers. The underlying principles of the curriculum are inspired by Complex Mathematics Education, a long-term initiative rooted in the ideas of Tamás Varga. Using a pre-post-test design and semi-structured teacher interviews, we evaluated the curriculum’s feasibility. Feedback indicates that our revised approach and materials align well with the current Hungarian curriculum reform. Findings across the project’s phases suggest that, with adequate technical support and sufficient practice tasks, even complex statistical concepts can be successfully taught to beginners. This approach supports diverse learner profiles and accommodates varying levels of prior knowledge and ability across schools. The article presents key insights from our teacher-training efforts over three phases, involving a total of 32 educators.
Today, video content constitutes a significant portion of internet traffic. This video can be viewed by a wide range of devices with varying characteristics and under different network conditions. Video transcoding is a crucial mechanism for adapting video content to this diverse array of devices and bandwidth requirements while ensuring the best possible user experience. However, video transcoding is a computationally intensive process, requiring scalable infrastructure like cloud computing to efficiently handle the complexity and volume of tasks. In this paper, we propose a novel method to predict transcoding time across different types of platforms (CPU and GPU) and codecs (H.264/AVC, H.265/HEVC). Unlike existing approaches that focus mainly on CPU-based transcoding, the proposed model explicitly considers hardware-accelerated (GPU) transcoding, where accelerators significantly influence video transcoding performance in cloud computing. The predicted transcoding time can be utilized to optimize the scheduling of transcoding tasks in cloud computing, helping to ensure optimal load balancing and minimize total transcoding time while maintaining the highest video quality. The proposed solution consists of two essential phases: (i) dataset construction and (ii) model construction. The first phase involves video selection, segmentation, and video transcoding. The second phase focuses on analyzing the most important features that influence the prediction of transcoding time and developing a machine learning-based model for accurate video transcoding time prediction. Experimental results demonstrate that the XGBoost model achieves superior prediction accuracy across both software and hardware codecs, achieving a global coefficient of determination of R^2=0.993 when evaluated on the complete dataset, which includes video segments transcoded using H.264/AVC and H.265/HEVC codecs on CPU and GPU platforms. This performance represents an improvement of approximately 7.45
