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’.

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)

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

Effects of problem-based learning: A meta-analysis.

This meta-analysis has two aims: (a) to address the main effects of problem based learning on two categories of outcomes: knowledge and skills; and (b) to address potential moderators of the effect of problem based learning. We selected 43 articles that met the criteria for inclusion: empirical studies on problem based learning in tertiary education conducted in real-life classrooms. The review reveals that there is a robust positive effect from PBL on the skills of students. This is shown by the vote count, as well as by the combined effect size. Also no single study reported negative effects. A tendency to negative results is discerned when considering the effect of PBL on the knowledge of students. The combined effect size is significantly negative. However, this result is strongly influenced by two studies and the vote count does not reach a significant level. It is concluded that the combined effect size for the effect on knowledge is non-robust. As possible moderators of PBL effects, methodological factors, expertise-level of students, retention period and type of assessment method were investigated. This moderator analysis shows that both for knowledge- and skills-related outcomes the expertise-level of the student is associated with the variation in effect sizes. Nevertheless, the results for skills give a consistent positive picture. For knowledge-related outcomes the results suggest that the differences encountered in the first and the second year disappear later on. A last remarkable finding related to the retention period is that students in PBL gained slightly less knowledge, but remember more of the acquired knowledge