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Conceptual change theories assume that knowledge structures grow during the learning process but also get reorganized. Yet, this reorganization process itself is hard to examine. By using concept maps, we examined the changes in students' knowledge structures and linked it to conceptual change theory. In a longitudinal study, thirty high-achieving students (M = 14.41 years) drew concept maps at three timepoints across a teaching unit on magnetism and electrostatics. In total, 87 concept maps were analyzed using betweenness and PageRank centrality as well as a clustering algorithm. We also compared the students' concept maps to four expert maps on the topic. Besides a growth of the knowledge network, the results indicated a reorganization, with first a fragmentation during the unit, followed by an integration of knowledge at the end of the unit. Thus, our analysis revealed that the process of conceptual change on this topic was non-linear. Moreover, the terms used in the concept maps varied in their centrality, with more abstract terms being more central and thus more important for the structure of the map. We also suggest ideas for the usage of concept maps in class.

To help students acquire mathematics and science knowledge and competencies, educators typically use multiple external representations (MERs). There has been considerable interest in examining ways to present, sequence, and combine MERs. One prominent approach is the concreteness fading sequence, which posits that instruction should start with concrete representations and progress stepwise to representations that are more idealized. Various researchers have suggested that concreteness fading is a broadly applicable instructional approach. In this theoretical paper, we conceptually analyze examples of concreteness fading in the domains of mathematics, physics, chemistry, and biology and discuss its generalizability. We frame the analysis by defining and describing MERs and their use in educational settings. Then, we draw from theories of analogical and relational reasoning to scrutinize the possible cognitive processes related to learning with MERs. Our analysis suggests that concreteness fading may not be as generalizable as has been suggested. Two main reasons for this are discussed: (1) the types of representations and the relations between them differ across different domains; and (2) the instructional goals between domains and subsequent roles of the representations vary.

Active learning yields better learning outcomes than traditional, lecture-based teaching. Common approaches in large lecture courses are activating elements during the lectures and warm-up activities using online learning environments. However, implementing warm-up exercises, on which students work on by themselves makes formative feedback increasingly important. We have studied students' views on warm-up online exercises, focusing on the role of automated feedback. We compared two types of feedback: feedback which guided the student towards the correct answer and feedback that guided the student towards the correct reading materials. We have also examined the correlation between students' views on the exercises and learning outcomes, as measured by exercise and exam points. The exercises were administered with a Moodle-based system focusing on teaching and assessment using a computer-based algebra kernel. The study was conducted during the first introductory mechanics course in physics at the University of Helsinki. We concluded that the preferred type of feedback was dependent on the student, i.e. different students prefer different kinds of feedback. Importantly, the students who completed the exercises alone reported benefiting more from them, which shows that the warm-up exercises successfully extended support for students outside of classroom hours.

We discuss here conceptual change and the formation of robust learning outcomes from the viewpoint of complex dynamic systems (CDS). The CDS view considers students' conceptions as context dependent and multifaceted structures which depend on the context of their application. In the CDS view the conceptual patterns (i.e. intuitive conceptions here) may be robust in a certain situation but are not formed, at least not as robust ones, in another situation. The stability is then thought to arise dynamically in a variety of ways and not so much to mirror rigid ontological categories or static intuitive conceptions. We use computational modelling to understand the generic dynamic and emergent features of that phenomenon. The model is highly simplified and idealized, but it shows how context dependence, described here by an epistemic landscape structure, leads to the formation of context dependent robust states that can be viewed as attractors in learning, and how owing to the sharply defined nature of these states, learning appears as a progression of switches from one state to another, giving thus the appearance of conceptual change as switches from one robust state to another. Finally, we discuss the implications of the results in directing attention to the design of learning tasks and their structure, and how empirically accessible learning outcomes might be related to these underlying factors.

Multiple external representations (MERs) are useful for teaching complex content in science education. An open question is whether there is an especially effective way to sequence MERs. On the one hand, the so‐called concreteness fading approach suggests starting instruction with more concrete representations and proceeding stepwise to more idealized representations. The effectiveness of this fading approach is, however, supported mainly by studies in mathematics education, while the results in physics are equivocal. On the other hand, presenting different representations simultaneously may support linking, that is, the comparison and contrast of representations, which may benefit learning. In an experimental classroom study (N = 187), we compared concreteness fading and simultaneous presentation of MERs for learning a challenging physics content in high school, namely, Faraday's law. We found no significant differences between conditions in posttest performance, and an equivalence test with bounds d = −0.5 to 0.5 showed that both approaches performed equally. The results align with previous findings questioning the superiority of concreteness fading over other ways of sequencing MERs. Therefore, facilitating students' understanding of a complex physics content may involve more than determining the optimal order of presenting MERs. We discuss limitations of the present study and implications for future research and practice. [ABSTRACT FROM AUTHOR]

We discuss here conceptual change and the formation of robustlearning outcomes from the viewpoint of complex dynamicsystems, where students’ conceptions are seen as context de-pendent and multifaceted structures which depend on the con-text of their application. According to this view the conceptualpatterns (i.e. intuitive conceptions) may be robust in a cer-tain situation but are not formed, at last not as robust ones, inanother situation. The stability is then thought to arise dynami-cally in a variety of ways and not so much mirror rigid ontolog-ical categories or static intuitive conceptions. We use compu-tational modelling in understanding the generic dynamic andemergent features of that phenomenon. The model shows howcontext dependence, described here through structure of epis-temic landscape, leads to formation of context dependent ro-bust states. The sharply defined nature of these states makeslearning to appear as a progression of switches from state toanother, given appearance of conceptual change as switch fromone robust state to another.

In learning conceptual knowledge in physics, a common problem is the incompleteness of a learning process, where students' personal, often undifferentiated concepts take on more scientific and differentiated form. With regard to such concept learning and differentiation, this study proposes a systemic view in which concepts are considered as complex, dynamically evolving structures. The dynamics of the concept learning and differentiation is driven by the competition of model utility in explaining the evidence. Based on the systemic view, we introduce computational model, which represents the essential features of the conceptual system in the form of directed graph (DGM), where concepts are nodes connected to other conceptual elements (nodes) in the graph. The results of a DGM are then compared to the empirical findings to identify differentiation between concepts of electric current and voltage based on a re-analysis of previously published empirical findings on upped secondary school students' learning paths in the context of DC circuits. The comparison shows that the model predicts and explains many relevant, empirically observed features of the learning paths of concept learning and differentiation, such as: 1) Context-dependent dynamics, 2) the persistence of ontological shift and concept differentiation, and 3) the effects of communication on individual learning paths. The systemic view and the DGM model based on it make these generic features of interest in concept learning and differentiation understandable and show that these features are associated with the guidance of theoretical knowledge. Finally, we discuss briefly the implications of the results on teaching and instruction.

One well-known learning obstacle is that students rarely use the concepts in the way that scientists use them. Rather, students mix up closely related concepts and are inclined towards matter-based conceptualisations. Furthermore, some researchers have argued that certain difficulties are rooted in the student's limited repertoire of causal schemes. These two aspects are conveniently represented in the recent proposal of the systemic view of concept learning. We applied this framework in our analyses of university students' explanations of DC circuits and their use of concepts such as voltage, current and resistance. Our data consist of transcribed group interviews, which we analysed with content analysis. The results of our analysis are represented with directed graphs. Our results show that students had a rather refined ontological knowledge of the concepts. However, students relied on rather simple explanation models, but few students were able to modify their explanations during the interview. Based on the analysis, we identified three processes of change: model switch, model refinement and model elaboration. This emphasises the importance of relevant relational knowledge at a later stage of learning. This demonstrates how concept individuation and learning of relational structures occurs (and in which order) and sets forth interesting research questions for future research.

Model-based learning (MBL) has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, I argue that viewing models as relational categories provides a well-motivated cognitive basis for MBL. I discuss the different roles of models and modeling within MBL (using ready-made models, constructive modeling, and generative modeling) and discern the related cognitive aspects brought forward by the reinterpretation of models as relational categories. I will argue that relational knowledge is vital in learning novel models and in the transfer of learning. Moreover, relational knowledge underlies the coherent, hierarchical knowledge of experts. Lastly, I will examine how the format of external representations may affect the learning of models and the relevant relations. The nature of the learning mechanisms underlying students' mental representations of models is an interesting open question to be examined. Furthermore, the ways in which the expert-like knowledge develops and how to best support it is in need of more research. The discussion and conceptualization of models as relational categories allows discerning students' mental representations of models in terms of evolving relational structures in greater detail than previously done.

CaO‐Al2O3‐B2O3‐based slag is among the most promising “nonreactive” mold fluxes for continuous casting of high‐aluminum steel. However, CaO‐Al2O3‐B2O3‐based slag system exhibits a stronger crystallization ability compared to traditional mold fluxes. Herein, calcium oxide in CaO‐Al2O3‐10%B2O3 slag is partially replaced by the same mass of barium oxide (5 and 10 mass%) to adjust the crystallization ability. The crystallization of glassy slags is then investigated using a differential scanning calorimeter and their crystallization kinetics are analyzed using the Matusita–Sakka model. The structural evolution of glassy slags from room temperature to 1200 °C is also investigated using in situ Raman spectroscopy. The kinetic analysis shows that the crystallization process of CaO‐Al2O3‐B2O3‐BaO glassy slags follows the surface crystallization mechanism. The predominant crystalline products are calcium monoaluminate (CaAl2O4) and calcium borate (Ca3(BO3)2). The partial replacement of calcium oxide by barium oxide inhibits the growth of CaAl2O4 in the slag by increasing the activation energy of crystal growth. The high‐temperature Raman spectroscopy study shows that the band between 700 and 900 cm−1 gets weaker relative to the band between 450 and 650 cm−1 at high temperatures. This indicates the strengthening of bending vibration of Al‐O‐Al, which is consistent with the precipitation of CaAl2O4 with full oxygen bridging. [ABSTRACT FROM AUTHOR]

The strength of coke is of major importance for efficient blast furnace operation. There are only a few studies related to compressive coke hot strength, possibly due to experimental difficulties. In this work, it has been demonstrated that the Gleeble thermomechanical simulator is suitable for evaluating coke hot strength and offers many benefits compared to purpose-built devices reported in the literature. This finding could open coke hot strength research to a wider number of scientists. The compressive hot strength of coke was evaluated at 1000 and 1600°C by testing 50 samples at both temperatures. The yield strength and ultimate strength of the coke were, respectively, 46 and 20% lower at 1600°C than 1000°C. Stress-strain curves showed that the coke was brittle at 1000°C but partially plastic at 1600°C. The significantly lower coke strength at 1600°C could help explain the smaller coke lump size found near the tuyere level in quenched blast furnaces. [ABSTRACT FROM AUTHOR]

The article presents a study which evaluates the possible production of mono-, di- and hemicalcium ferrite phases via melt for reduction kinetics investigations. Scanning electron microscopy (SEM-EDS) and x-ray diffraction (XRD) were used to identify mineral compositions of calcium ferrite samples. Results of the study indicate the possible production of the samples of calcium ferrites via melting, in which thermodynamic calculations were used to estimate conditions needed for calcium ferrites.