What predicts instructional quality and commitments to teaching: self-efficacy, pedagogical knowledge or integration of the two?

The aim of the paper is to identify different groups of in-service teachers based on their general pedagogical knowledge (GPK) and self-efficacy beliefs and to explore potential differences among these groups regarding their instructional quality and commitments to teaching. A sample of 161 in-service subject teachers (science, mathematics, or Estonian language) who taught in lower secondary schools in Estonia were included in the study. Data was collected with a GPK test and self-reported questionnaires on instructional quality and commitments to teaching in the context of an OECD Teacher Knowledge Survey. Based on the cluster analysis, three groups of in-service teachers were identified: "the over-confident" teachers with average self-efficacy and very low GPK, "the competent" teachers with high self-efficacy and GPK, and "the insecure" teachers with low self-efficacy and average GPK. These three types of teachers were different in terms of instructional quality and commitments to teaching. It seemed that teachers' self-efficacy beliefs are more important than GPK for instructional quality; however, GPK is more important for teachers' professional persistence illuminating their general sense of professional identity. Implications of these findings for teacher education and teacher retention will be discussed.

Applicability of the model of inclusive education in early childhood education: a case study.

Despite the development of policies and research supporting it, inclusion remains a challenge in contemporary education. We have developed a theoretical model for implementing inclusive education, thereby supporting early childhood education quality. It is necessary to establish the applicability of this model in order to apply it to improve the practices for adopting inclusive education. We conducted a case study, which showed that all levels and key characteristics of the theoretical model were also relevant in practice. However, as a result of the case study, the features describing the key characteristics were modified compared with the initial model. Additionally, the case study revealed that some of the features did not appear in practice. Those undetected features were mostly related to understanding the concept of inclusive education and the philosophy of inclusion. There appeared a need for a clearer understanding of inclusion on both the institutional and state level. The implementation of inclusive education does not in itself always increase inclusion or reduce exclusion. Therefore, when implementing inclusive education, it is necessary to think carefully about what is being done to allow all children to be meaningfully involved in the same classroom and by their teachers.

Data sharing practices and data availability upon request differ across scientific disciplines.

Data sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility. We evaluated data availability in research articles across nine disciplines in Nature and Science magazines and recorded corresponding authors' concerns, requests and reasons for declining data sharing. Although data sharing has improved in the last decade and particularly in recent years, data availability and willingness to share data still differ greatly among disciplines. We observed that statements of data availability upon (reasonable) request are inefficient and should not be allowed by journals. To improve data sharing at the time of manuscript acceptance, researchers should be better motivated to release their data with real benefits such as recognition, or bonus points in grant and job applications. We recommend that data management costs should be covered by funding agencies; publicly available research data ought to be included in the evaluation of applications; and surveillance of data sharing should be enforced by both academic publishers and funders. These cross-discipline survey data are available from the plutoF repository.

Understanding teacher design practices for digital inquiry-based science learning: the case of Go-Lab.

Designing and implementing online or digital learning material is a demanding task for teachers. This is even more the case when this material is used for more engaged forms of learning, such as inquiry learning. In this article, we give an informed account of Go-Lab, an ecosystem that supports teachers in creating Inquiry Learning Spaces (ILSs). These ILSs are built around STEM-related online laboratories. Within the Go-Lab ecosystem, teachers can combine these online laboratories with multimedia material and learning apps, which are small applications that support learners in their inquiry learning process. The Go-Lab ecosystem offers teachers ready-made structures, such as a standard inquiry cycle, alternative scenarios or complete ILSs that can be used as they are, but it also allows teachers to configure these structures to create personalized ILSs. For this article, we analyzed data on the design process and structure of 2414 ILSs that were (co)created by teachers and that our usage data suggest have been used in classrooms. Our data show that teachers prefer to start their design from empty templates instead of more domain-related elements, that the makeup of the design team (a single teacher, a group of collaborating teachers, or a mix of teachers and project members) influences key design process characteristics such as time spent designing the ILS and number of actions involved, that the characteristics of the resulting ILSs also depend on the type of design team and that ILSs that are openly shared (i.e., published in a public repository) have different characteristics than those that are kept private.

Supporting Self-Regulated Learning in Distance Learning Contexts at Higher Education Level: Systematic Literature Review.

Shifting learning to distant formats especially at the higher education level has been unprecedented during the past decade. Diverse digital learning media have been emerging which allow learner autonomy, and at the same time, require the ability of efficient regulation of various aspects of the learning process for sustainable academic progress. In this context, supporting students in self-regulated learning (SRL) in an optimal way becomes an important factor for their academic success. The present study attempts through a systematic review of 38 studies to provide an overview of the interventions identified as supporting all areas of SRL (metacognitive, cognitive, motivational and emotional), in its three phases (preparatory, performance, appraisal) in distance education environments at the higher education level. As the study results show, there are a number of SRL support interventions available with proven positive effect on SRL. However, their distribution has been found to be uneven. Whereas metacognition regulation and the performance phase of learning is vastly investigated, the emotion regulation, and the preparatory and appraisal phases of the SRL cycle are somewhat underexplored. As complex and multi-component as the process of SRL is, the combination of various interventions, and specific features, for more comprehensive support has also been found beneficial. Additionally, it has been revealed that the emotion regulation, in most cases, is closely related to motivation regulation, and similar interventions support these two. Future studies can further explore the efficiency and relevance of the identified interventions, taking closer look at the effects of various digital media, learner characteristics as well as different levels of education on learners' self-regulation needs.

Mining Educational Data to Predict Students' Performance through Procrastination Behavior.

A significant amount of research has indicated that students' procrastination tendencies are an important factor influencing the performance of students in online learning. It is, therefore, vital for educators to be aware of the presence of such behavior trends as students with lower procrastination tendencies usually achieve better than those with higher procrastination. In the present study, we propose a novel algorithm-using student's assignment submission behavior-to predict the performance of students with learning difficulties through procrastination behavior (called PPP). Unlike many existing works, PPP not only considers late or non-submissions, but also investigates students' behavioral patterns before the due date of assignments. PPP firstly builds feature vectors representing the submission behavior of students for each assignment, then applies a clustering method to the feature vectors for labelling students as a procrastinator, procrastination candidate, or non-procrastinator, and finally employs and compares several classification methods to best classify students. To evaluate the effectiveness of PPP, we use a course including 242 students from the University of Tartu in Estonia. The results reveal that PPP could successfully predict students' performance through their procrastination behaviors with an accuracy of 96%. Linear support vector machine appears to be the best classifier among others in terms of continuous features, and neural network in categorical features, where categorical features tend to perform slightly better than continuous. Finally, we found that the predictive power of all classification methods is lowered by an increment in class numbers formed by clustering.

Definitions and Conceptual Dimensions of Responsible Research and Innovation: A Literature Review.

The aim of this study is to provide a discussion on the definitions and conceptual dimensions of Responsible Research and Innovation based on findings from the literature. In the study, the outcomes of a literature review of 235 RRI-related articles were presented. The articles were selected from the EBSCO and Google Scholar databases regarding the definitions and dimensions of RRI. The results of the study indicated that while administrative definitions were widely quoted in the reviewed literature, they were not substantially further elaborated. Academic definitions were mostly derived from the institutional definitions; however, more empirical studies should be conducted in order to give a broader empirical basis to the development of the concept. In the current study, four distinct conceptual dimensions of RRI that appeared in the reviewed literature were brought out: inclusion, anticipation, responsiveness and reflexivity. Two emerging conceptual dimensions were also added: sustainability and care.