Introduction to Vol. 14 No. 1 (2026)

 

Dear Reader,

I am pleased to announce the publication of a new issue of Frontline Learning Research (our first of 2026), with three more issues in the pipeline.

This issue holds five articles. Between them, they provide theoretical and methodological advances in educational research, focusing on mechanistic explanations, classroom interaction dynamics, the research-practice gap in education, debiasing of algorithms, and group regulatory actions.

Pennings et al. started from the premise that many educational and social phenomena are dynamic and change over time. To study such phenomena, intensive longitudinal data and time-series analyses are essential. Yet such methods remain largely underused in educational sciences, due to their perceived complexity and the dominance of group-level prediction. Complex Dynamic Systems (CDS) perspectives offer promising tools for studying educational phenomena that unfold over time, addressing limitations of traditional nomothetic approaches. CDS emphasises idiographic methods that capture individual, context-dependent processes and within-person change. In their paper they introduce an accessible approach to CDS research by explaining and illustrating how time-series decomposition with spectral analysis can reveal trends, cycles, and level of synchronisation. By breaking down time-series into interpretable components, researchers can better understand dynamic educational processes and avoid misrepresenting complex phenomena. Their contribution illustrates the application of time-series decomposition and spectral analysis with time series data of teacher behaviour, student behaviour, and teacher physiology in four classrooms. The application of time-series analysis is discussed considering the differences in the teachers’ dynamic profiles as well as the potential to study a large variety of other educational topics.

As Artificial Intelligence (AI) algorithms are increasingly used in education, research shows that the use of these algorithms is not without cost. Instead, AI algorithms are prone to biases which are discussed a lot in various domains. The core strength of the contribution of Grimm et al. is to anchor the discussion of biases in the specific domain of physics education and to discuss the biases in front of a description of the domain-specific inequalities along physics identity development of students. The database consists of the written answers of 527 students to around 30 items from a five-week-period of physics classes in a digital learning environment. Two concrete biases of AI algorithms in physics education and possible approaches to identify and reduce these biases are investigated quantitatively. In a critical discussion from a feminist and de-colonial perspective, it is highlighted that the chosen approaches seem to have promising potentials to mitigate negative effects on under-served students´ physics identity development. Besides, relevant limitations lead to conclusions that additional counter-measures are needed in order to break out of the vicious cycle of reproduction of historically grown inequalities in physics education. The domain-specific analysis can serve as orientation for other domains as well in order to tackle the challenges of AI algorithmic bias effectively and efficiently.

In their paper, Ludvigsen et al. explore whether mechanisms and mechanistic explanations, as articulated within the new mechanistic philosophy, can provide a shared conceptual foundation for strengthening cumulative knowledge production in learning research. They argue that adopting a mechanistic meta stance can enhance the quality and cumulativity of learning research by offering a framework for precise and integrative constitutive causal explanations that connect multiple levels of analysis in the study of learning.

Korpershoek posits that there is a disconnection between the knowledge that researchers produce and the knowledge that practitioners use in their daily routines. This so-called science-practice gap is one of the main scientific challenges in this era in applied fields such as educational sciences, because it implies that practitioners fail to adopt evidence-informed practices. His conceptual paper presents a theory synthesis with the aim to unravel the complex challenge of reducing the science-practice gap in education. Based on an integrative conceptual literature review, he argues that new leads to reduce the science-practice gap in education lie in a fundamental understanding of (a) the knowledge conceptualization problem, (b) the knowledge production problem, and (c) the knowledge transfer problem. We face complex societal challenges that can only be solved by collective engagement and collaboration between researchers and practitioners to address these three fundamental problems. In his paper, he proposes that the interchange between science and practice should start with the question “What knowledge do we need to improve educational practice?”.

Finally, Bakhtiar et al. started from the premise that collaborative problem solving requires student groups to not only co-construct their ideas as a group but also regulate several aspects of their teamwork. Although prior research has linked group regulation to the group’s task performance, less is known about the immediate products of regulation. Specifically, limited research has examined the types of regulation that could promote productive knowledge sharing during ongoing collaborative interactions. Their study explores how six groups of varying performance levels exercised regulatory actions between moments of productive knowledge sharing (PKS) during online collaboration.They employed a computational technique to analyse the temporal changes in groups’ idea networks to identify episodes where groups’ idea exchanges reached a productive level. Interpretations of students’ regulatory actions as interleaved between PKS episodes were facilitated by Epistemic Network Analysis (ENA). Findings highlight the importance of examining multiple facets of regulatory actions at both the group and individual levels while contextualizing these actions within meaningful knowledge sharing episodes. Socioemotional regulation was notably lacking during pre-task planning in low-performing groups. The lack of shared planning to reaffirm the group’s goal before starting the task appears to relate to groups experiencing behavioural coordination challenges, which likely reduce the chances of achieving PKS. Following instances of PKS, high-performing groups often monitored and evaluated their ideas or cognition, whereas low-performing groups focused on monitoring how the task was being completed. Patterns of individual members’ regulation across the ENA space revealed that members of high-performing groups regulated actively by diversifying their actions. The implications for supporting collaboration are discussed.

Kind regards,

Professor, Dr. Nina Bonderup Dohn

Editor-in-Chief, Frontline Learning Research