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Background: Formative assessments are needed to enable monitoring how student knowledge develops throughout a unit. Constructed response items which require learners to formulate their own free‐text responses are well suited for testing their active knowledge. However, assessing such constructed responses in an automated fashion is a complex task and requires the application of natural language processing methodology. In this article, we implement and evaluate multiple machine learning models for coding energy knowledge in free‐text responses of German K‐12 students to items in formative science assessments which were conducted during synchronous online learning sessions. Dataset: The dataset we collected for this purpose consists of German constructed responses from 38 different items dealing with aspects of energy such as manifestation and transformation. The units and items were implemented with the help of project‐based pedagogy and evidence‐centered design, and the responses were coded for seven core ideas concerning the manifestation and transformation of energy. The data was collected from students in seventh, eighth and ninth grade. Methodology: We train various transformer‐ and feature‐based models and compare their ability to recognize the respective ideas in students' writing. Moreover, as domain knowledge and its development can be formally modeled through knowledge networks, we evaluate how well the detection of the ideas within responses translated into accurate co‐occurrence‐based knowledge networks. Finally, in terms of the descriptive accuracy of our models, we inspect what features played a role for which prediction outcome and if the models pick up on undesired shortcuts. In addition to this, we analyze how much the models match human coders in what evidence within responses they consider important for their coding decisions. Results: A model based on a modified GBERT‐large can achieve the overall most promising results, although descriptive accuracy varies much more than predictive accuracy for the different ideas assessed. For reasons of comparability, we also evaluate the same machine learning architecture using the SciEntsBank 3‐Way benchmark with an English RoBERTa‐large model, where it achieves state‐of‐the‐art results in two out of three evaluation categories. Lay Description: What is already known about this topic?: Formative assessments are needed to test and monitor the development of learners' knowledge throughout a unit to provide them with appropriate automated feedback.Constructed response items which require learners to formulate their own free‐text responses are well suited for testing their active knowledge.Assessing constructed responses in an automated fashion is a widely researched topic, but the problem is far from solved and most of the work focuses on predicting holistic scores or grades.To allow for a more fine‐grained and analytic assessment of learners' knowledge, systems which go beyond predicting simple grades are required.To guarantee that models are stable and make their predictions for the correct reasons, methods for explaining the models are required. What this papers adds?: A core topic in physics education is the concept of energy.We implement and evaluate multiple systems based on natural language processing technology for assessing learners' conceptual knowledge about energy physics using transformer language models as well as feature‐based approaches.The systems assess students' knowledge about various forms of energy, indicators for the same and the transformation of energy from form into another.As our systems are based on machine learning methodology, we introduce a novel German short answer dataset for training them to detect the respective knowledge elements within students' free‐text responses.We evaluate the performance of these systems using this dataset as well as the well‐established SciEntsBand‐3‐Way dataset and manage to achieve, to our best knowledge, new state‐of‐the‐art results for the latter.Moreover, we apply methodology for explaining model predictions to assess whether predictions are carried out for the correct reasons. Implications for practice and/or policy: It is indeed possible to assess constructed responses for the demonstrated knowledge about energy physics in an analytic fashion using natural language processing.Transformer language models can outperform more specialized feature‐based approaches for this task in terms of predictive and descriptive accuracy.Co‐occurrences of different concepts within the same responses can lead models to learn undesired shortcuts which make them unstable. [ABSTRACT FROM AUTHOR]

Like many other countries, Germany has defined goals to reduce its CO2-emissions following the Paris Agreement of the 21st Conference of the Parties (COP). The first successes in decarbonizing the electricity sector were already achieved under the German Energiewende. However, further steps in this direction, also concerning the heat and transport sectors, have stalled. This paper describes three possible pathways for the transformation of the German energy system until 2050. The scenarios take into account current climate politics on a global, European, and German level and also include different demand projections, technological trends and resource prices. The model includes the sectors power, heat, and transportation and works on a Federal State level. For the analysis, the linear cost-optimizing Global Energy System Model (GENeSYS-MOD) is used to calculate the cost-efficient paths and technology mixes. We find that a reduction of CO2 of more than 80% in the less ambitious scenario can be welfare enhancing compared to a scenario without any climate mitigating policies. Even higher decarbonization rates of 95% are feasible and needed to comply with international climate targets, yet related to high effort in transforming the subsector of process heat. The different pathways depicted in this paper render chances and risks of transforming the German energy system under various external influences. [ABSTRACT FROM AUTHOR]

Abstract Participation processes can improve environmental decision-making. However, proving the impact of participation processes – i.e. decision-making involving diverse actors from civil society, business, state, and administration – is challenging. This paper therefore examines participation processes by applying proxy measures that are assumed to strongly correlate with improved environmental decision-making. Such indicators include the perception of the process as being fair and legitimate, the final outcomes being considered effective and efficient, satisfaction with one's own engagement and the development of trust, and conflict resolution. Participation process characteristics such as participation format and facilitation are assumed to have an impact on these indicators. This study examined participation processes in relation to the German energy transformation (Energiewende), asking respondents about their experience of participation, their perception of the process, and also about intermediate outcomes such as trust building and conflict resolution. An online survey was conducted, resulting in 516 valid responses. The results show that participation processes related to the Energiewende are often perceived as fair and legitimate. Moreover, the findings provide a better understanding of the relationships between participation process characteristics and desirable intermediate outcomes, such as trust and conflict resolution, as well as normative process factors. The intensity of a participation process – i.e. whether it is dialogic or a written consultation process – had no significant influence either on normative process factors, such as procedural fairness, or on trust or conflict resolution. Instead, a trustworthy moderator and the integration of all affected interests are important participation process characteristics that could determine the success of the decision-making process. Highlights • The results of an online Survey (N = 516) are presented. • Participation processes related to the German energy transformation are positively perceived. • Participatory approaches can enhance trust building. • Participation has limited capacity to resolve conflicts. • Participation intensity is less important than participants diversity and facilitation. [ABSTRACT FROM AUTHOR]

Organizational interactions in fields, including their antecedents and consequences, remain under-researched, in particular with regard to relational distance and transformative skills. Through a comparative study of the German and Japanese wind power sectors, we explore the importance of distance among organizational actors and the development of skills. While in the case of Germany a radical increase in wind energy generation can be witnessed, the situation in the field of Japanese wind power remains largely unchanged. We show how different degrees of distance among organizational actors in these two countries result in the different development of skills that stimulate transformation in the field of energy generation. More precisely, we illustrate the pivotal role of distant challengers with their transformative skills for the successful conversion of already established field structures. Our study contributes to field theory by elaborating on the understanding of the evolution of relational distance, thereby grasping the dynamic interplay between the diversity of actors and their skill formation within a certain strategic action field. [ABSTRACT FROM AUTHOR]

The energy transformation is changing the structure of the energy sector in Europe and Germany. In this paper the current structure of the energy sector is analysed both empirically as well as theoretically. Therefore, the authors have developed the business model framework for the energy transformation (BMFE). The framework is a synthesis of classical business model designs. An exhaustive survey of existing business models based on primary data collection and a literature review leads to 638 business models. Finally, 69 prototypical business models of the energy sector are the result of the classification of these business models. The information of the business models within the BMFE is applied to show the growing importance of value creation networks in energy industry. The work represents the current status of the business models in the energy sector. [ABSTRACT FROM AUTHOR]