Group, team, or something in between? Conceptualising and measuring team entitativity

The main aim of this study includes bridging the gap between strict team and broader group research by describing the distinction between strict teams and mere collections of individuals as the degree of team entitativity or teamness. The concept of entitativity is derived from social psychology research and further developed and integrated in team research. Based upon the entitativity concept and the core team definitions, the defining features shaping teams’ degree of entitativity are determined: shared goals and responsibilities; cohesion (task cohesion and identification); and interdependence (task and outcome). In a next step, a questionnaire is developed to empirically grasp these features. The questionnaire is tested in two waves of data collection (N1=1320; N2=731). Based upon a combination of Classical Test Theory analyses (exploratory and confirmatory factor analyses) and Item Response Theory analyses the questionnaire is developed. The final questionnaire consists of three factors: shared goals and cohesion, task interdependence, and outcome interdependence. Further psychometric analyses include the investigation of validity, longitudinal measurement invariance, and test-retest reliability. This manuscript describes frontline research by: (1) developing a novel conceptualisation bridging groups and teams based upon two research traditions (social psychology and team research) and (2) combining two methodological traditions regarding questionnaire development and validation (Classical Test Theory and Item Response Theory)

Team entitativity and teacher teams in schools: Towards a typology

In this article we summarise research that discusses ‘teacher teams’. The central question guiding this study is ‘What types of teacher teams are there in schools and can they rightfully be called ‘teams’ or are they merely groups?’. We attempted to answer this question by searching literature on teacher teams and comparing what these articles present as being teacher teams. We attempt to further grasp the concept of teacher teams by creating a typology for defining different types of teacher teams. Overall, the literature pertaining to teacher teams appeared to be characterised by a considerable amount of haziness and teacher ‘teams’ mostly do not seem to be proper ‘teams’ when bearing the criteria of a team as defined by Cohen and Bailey (1997) in mind. The proposed typology, characterising the groups of teachers by their task, whether they are disciplinary or interdisciplinary, whether they are situated within or cross grades en by their temporal duration, seems to be a useful framework to further clarify different sorts of teacher ‘teams’.

Modelling for understanding AND for prediction/classification - the power of neural networks in research

Two articles, Edelsbrunner and, Schneider (2013), and Nokelainen and Silander (2014) comment on Musso, Kyndt, Cascallar, and Dochy (2013). Several relevant issues are raised and some important clarifications are made in response to both commentaries. Predictive systems based on artificial neural networks continue to be the focus of current research and several advances have improved the model building and the interpretation of the resulting neural network models. What is needed is the courage and open-mindedness to actually explore new paths and rigorously apply new methodologies which can perhaps, sometimes unexpectedly, provide new conceptualisations and tools for theoretical advancement and practical applied research. This is particularly true in the fields of educational science and social sciences, where the complexity of the problems to be solved requires the exploration of proven methods and new methods, the latter usually not among the common arsenal of tools of neither practitioners nor researchers in these fields. This response will enrich the understanding of the predictive systems methodology proposed by the authors and clarify the application of the procedure, as well as give a perspective on its place among other predictive approaches

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

oai:flr.journals.publicknowledgeproject.org:article/13Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students’ outcomes. Some authors have suggested that traditional statistical methods do not always yield accurate predictions and/or classifications (Everson, 1995; Garson, 1998). This paper explores a relatively new methodological approach for the field of learning and education, but which is widely used in other areas, such as computational sciences, engineering and economics. This study uses cognitive and non-cognitive measures of students, together with background information, in order to design predictive models of student performance using artificial neural networks (ANN). These predictions of performance constitute a true predictive classification of academic performance over time, a year in advance of the actual observed measure of academic performance. A total sample of 864 university students of both genders, ages ranging between 18 and 25 was used. Three neural network models were developed. Two of the models (identifying the top 33% and the lowest 33% groups, respectively) were able to reach 100% correct identification of all students in each of the two groups. The third model (identifying low, mid and high performance levels) reached precisions from 87% to 100% for the three groups. Analyses also explored the predicted outcomes at an individual level, and their correlations with the observed results, as a continuous variable for the whole group of students. Results demonstrate the greater accuracy of the ANN compared to traditional methods such as discriminant analyses.  In addition, the ANN provided information on those predictors that best explained the different levels of expected performance. Thus, results have allowed the identification of the specific influence of each pattern of variables on different levels of academic performance, providing a better understanding of the variables with the greatest impact on individual learning processes, and of those factors that best explain these processes for different academic levels

Bridging gaps: a systematic literature review of brokerage in educational change

Bridging gaps between educational stakeholders at the classroom, school, and system levels is essential to achieve sustainable change in primary and secondary education. However, transferring knowledge or building capacity within this network of loosely coupled stakeholders is demanding. The brokerage concept holds promise for studying these complex patterns of interaction, as it refers to how specific actors (brokers) link loosely coupled or disconnected individuals (brokering). However, different research traditions, in terms of theoretical frameworks and methodological approaches, and various stakeholders examined in their role as bridge builders make understanding the role of brokers, brokering, and brokerage in changing educational practice challenging. Therefore, the purpose of this study is to provide an overview of the current literature on these concepts in educational change research. In a systematic literature review based on 42 studies, we analyzed each study’s theoretical assumptions, methodological approach, scope in terms of stakeholders involved, and empirical findings. First, the literature review revealed that research on educational change refers to four different theoretical frameworks when focusing on brokers, brokering, or brokerage. Second, our results indicate that predominantly qualitative approaches have been applied. Third, using content network graphs, we identified teachers and principals as among the most frequently analyzed brokers. Fourth, four relevant aspects of the empirical findings are presented: brokers’ personal characteristics, conditions that enable brokering, successful brokering strategies, and outcomes of brokerage. Finally, we outline a future research agenda based on the empirical evidence base and shortcomings

Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.Fil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; BélgicaFil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Kyndt, Eva. Swinburne University Of Technology; Australia. Universiteit Antwerp; BélgicaFil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgic

The measurement of collaborative culture in secondary schools: An informal subgroup approach

Research on teacher collaboration underlines the importance of a collaborative culture for teachers’ functioning. However, while scholars usually regard collaborative culture as a school team characteristic, this study argues that subgroups may be more meaningful units of analysis to conceptualize and assess teachers’ perceptions of collaborative culture. Based on the assumption that collaborative culture is developed, expressed, and maintained in frequent work-related interactions, this study hypothesizes that collaborative culture is not homogenously spread over the school but rather varies between informal subgroups. Data from 760 Flemish teachers were examined using social network analysis and consensus analyses. The results provided evidence that perceptions on collaborative culture are more homogeneous within informal subgroups that are characterized by frequent interactions than the entire school team. This finding stresses the importance of assessing the meaningful unit of analysis for collective-level and socially-constructed concepts, such as collaborative culture. Moreover, the benefits and potential of a social network approach to identify (socially stable) subunits within the school team are illustrated

Development and Validation of an Instrument to Measure Work-Related Learning

This paper describes the development and validation of an instrument for measuring work-related learning, which can be applied in different occupational contexts. Based on a comprehensive literature review and group discussions among the authors, the instrument was carefully constructed and examined among a heterogeneous sample of Flemish employees (N = 3232). The dataset was randomly divided into two subsets. An exploratory factor analysis was conducted on the first dataset (n = 1616) to provide insight into the underlying structure of the instrument. The second subset of the data (n = 1616) was used to validate the retrieved structure by means of a confirmatory factor analysis and to investigate the internal consistencies, convergent and discriminant validity, and the measurement invariance across different groups. After six months, the instrument was retested among the same respondents to examine longitudinal measurement invariance and predictive validity. The results showed that three factors could be distinguished and confirmed, namely informal learning activities using personal sources, informal learning activities using environmental sources, and formal learning activities. The results regarding the reliability and validity of the instrument were satisfactory

Investigating students' approaches to learning. The role of perceived workload, perceived task complexity, working memory capacity, attention and motivation, and their predictive role of academic performance

During the past decade, a wide range of teaching methods that emphasise the independence of the student have been implemented and investigated under the common denomination of student-centred learning environments. The main and consistent goal of all these learning environments is fostering a deeper level of learning and understanding. Despite promising results, the specific conceptual origin of this doctoral research lies in the finding that student-centred learning environments do not always push students towards a deep approach to learning. These approaches to learning include the intentions of the students and the strategies they use to learn. A student with a surface approach to learning seeks to reproduce the course material. The most popular strategies among these students are rote learning and unreflective memorisation. A deep approach to learning is associated with an intrinsic interest in the subject. The students intention is to understand the material by making use of various strategies such as conceptual analysis, reflection, discussing the material, looking up additional information, among others, to that effect. When carried out thoroughly, this deep approach to learning generally results in higher quality learning outcomes. The inconclusiveness of results in the research on student centred learning environments has given rise to the question as to what specific factors influence the induction of a deep approach to learning. This doctoral dissertation comprises 6 articles: 1 literature review and 5 empirical studies based on two experiments. In addition, there is a general introduction and a conclusion which focuses on what we have learned from this research and its limitations. Finally, some suggestions are put forward for future research. The first chapter is a literature review: based on the scientific literature, specific encouraging or discouraging factors, suggested by research on the influence of student centred learning environments, were identified. Next the nature of the influence of these factors on students approaches to learning was investigated. This literature study revealed a lot of possible encouraging and discouraging factors that can be divided into three general categories: context, perceived context and individual characteristics. A selection of variables to study was carried out, based on the literature review, and the influence of five independent variables (perceived workload, perceived task complexity, motivation, working memory capacity and attention) on students approaches to learning were investigated. Chapter 2 to chapter 5 report on the different hypotheses investigated regarding the relationships of these selected variables with approaches to learning. Chapter 2 investigated the influence of perceived workload and task complexityon students approaches to learning. This study identified lack of information, as an aspect of task complexity, as a generally inhibiting factor when trying to induce a deep approach to learning. It also showed the differential influence of familiarity on approaches to learning under different workload and task complexity conditions. Chapter 3 focused on the direct and indirect influence of motivation on learning approaches. It showed that autonomous motivation influences the perception of a lack of information. Moreover, autonomous motivation does not only influence students approaches to learning in an indirect manner. It also has a direct positive influence on deep approaches to learning. Chapter 4 is an exploratory study which investigates if working memory capacity and attention relate to students approaches to learning. Results indicate that students with high cognitive capacities use less deep approaches but they also show lesser use of surface approaches to learning. In chapter 5 the methodology of the previous chapters was supplemented with a complementary person oriented approach, examining if different student profiles could be identified based on the motivation and working memory capacity of the student. It was further explored if these different profiles differed in their learning approaches and whether they experienced a different influence of workload in their learning approaches. This study concluded that students with a different profile differed in their approaches to learning and were influenced by workload in the same way. Chapter 6 examined, by means of a neural network analysis, which variables were important for predicting the best, weakest and middle group of students in terms of academic performance, and how well they could predict learning outcomes. The predictors that were included were students approaches to learning, their motivation and their cognitive capacities: working memory capacity and attention. Results showed that the academic performance of the top and bottom 20% were best predicted by only their cognitive capacities, while approaches to learning and motivation only contributed to the prediction of the middle group in addition to their cognitive skills.nrpages: 300status: publishe

Turning points during the life of student project teams: A qualitative study

In this qualitative study a more flexible alternative of conceptualising changes over time in teams is tested within student project teams. The conceptualisation uses turning points during the lifespan of a team to outline team development, based on work by Erbert, Mearns, & Dena (2005). Turning points are moments that made a significant difference during the course of the collaboration as a team. In this study, they are tracked by means of team interviews and reflection papers of team members. A method of coding was created to collect all information about the turning points, their causes and consequences. By means of a thorough analysis of these coded data an overview of their nature and their effects on the rest of the team process as perceived by the team members themselves is provided. Results show that the development paths of the three teams were differentiated in terms of turning points that occurred and, especially, in the order in which the turning points occurred. However four types of turning points (two at the task level en two at the interpersonal level) were remarkable due to their occurrence in all three project teams