Frontline Learning Research (E-Journal - EARLI, European Association for Research on Learning)
Not a member yet
256 research outputs found
Sort by
Responsibility in the School Context - Development and Validation of a Heuristic Framework
Get PDFExisting research has identified feelings of responsibility as having major motivational implications for a person’s actions. A person identifying as being responsible for a certain task will perceive themselves as self-determined and thus invest considerable effort in the task. Despite being coneptualised as an individual’s sense of internal obligation, responsibility in everyday contexts is often attributed by and to other people. Different perspectives on responsibility may, however, not always overlap, especially in the school context where tasks and liabilities often remain ill-defined. This paper thus presents a framework of responsibility in the school context which assumes teachers, students and parents to share a certain number of microsystems which may (indirectly) influence one another. In order to test the usefulness of the proposed framework, a series of studies were conducted collecting data on teachers’, students’ and parents’ views of their own and one another’s responsibility in the school context. 4339 statements were assigned to categories representing different parts of the framework and reveal its usefulness for describing the complexity of responsibility attributions and its influences in the school context. Findings show the framework will be helpful to embrace existing research and develop questions for further research that address central educational issues such as student and teacher motivation, teacher burnout as well as prerequisites for students’ high or low achievement
Using New Models to Analyze Complex Regularities of the World
Get PDFThis commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is a risk that we model perfect nonsense perfectly. Our second concern is related to the relevance of comparing multilayer perceptron neural networks and linear discriminant analysis classification accuracy indices. We find this problematic, as it is like comparing apples and oranges. It would have been easier to interpret the model and the variable (group) importance’s if the authors would have compared MLP to some discriminative classifier, such as group lasso logistic regression. Finally, we conclude our commentary with a discussion about the predictive properties of the adopted data modeling approach.
Unfolding perspectives on networked professional learning: Exploring ties and time
Get PDFNetworked learning and learning networks are commonplace concepts in most contemporary discourse on learning in the 21st century. This special issue provides a collection of studies that address the need for a growing body of empirical work to extent the limited understanding of the use and benefits of networks in relation to learning and professional development. In this article we attempt to offer a synthesis of the studies presented in this special issue and reflect on their findings. The studies in this issue present a rich combination of networked professional learning research addressing issues related to the composition and structure of learning networks, their content and activities, showing how multi-faceted research in the field of networked learning really is. Based on the findings and methods used in the articles in this issue, we articulate some recommendations for further research. The recommendations are focused on the need for advanced multi-level analysis to understand the complexity of learning ties, the need for employing a multi-method research approach to triangulate and contextualize findings, the need to conduct process and time-based analysis and finally the need to further develop a theory and toolkit for applying Social Network Analysis in the context of networked learning.
Conceptual representations for transfer: A case study tracing back and looking forward
Get PDFA primary goal of instruction is to prepare learners to transfer their knowledge and skills to new contexts, but how far this transfer goes is an open question. In the research reported here, we seek to explain a case of transfer through examining the processes by which a conceptual representation used to reason about complex systems was transferred from one natural system (an aquarium ecosystem) to another natural system (human cells and body systems). In this case study, a teacher was motivated to generalize her understanding of the Structure, Behaviour, and Function (SBF) conceptual representation to modify her classroom instruction and teaching materials for another system. This case of transfer was unexpected and required that we trace back through the video and artefacts collected over several years of this teacher enacting a technology-rich classroom unit organized around this conceptual representation. We provide evidence of transfer using three data sources: (1) artefacts that the teacher created (2) in-depth semi-structured interview data with the teacher about how her understanding of the representation changed over time and (3) video data over multiple years, covering units on the aquatic ecosystem and the new system that the teacher applied the SBF representation to, the cell and body. Borrowing from interactive ethnography, we traced backward from where the teacher showed transfer to understand how she got there. The use of the actor-oriented transfer and preparation for future learning perspectives provided lenses for understanding transfer. Results of this study suggest that identifying similarities under the lens of SBF and using it as a conceptual tool are some primary factors that may have supported transfer
Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks
Get PDFoai: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
Longer bars for bigger numbers? children’s usage and understanding of graphical representations of algebraic problems
Get PDFIn Singapore, primary school students are taught to use bar diagrams to represent known and unknown values in algebraic word problems. However, little is known about students’ understanding of these graphical representations. We investigated whether students use and think of the bar diagrams in a concrete or a more abstract fashion. We also examined whether usage and understanding varied with grade. Secondary 2 (N = 68, Mage = 13.9 years) and Primary 5 students (N = 110, Mage = 11.1 years) were administered a production task in which they drew bar diagrams of algebraic word problems with operands of varying magnitude. In the validation task, they were presented with different bar diagrams for the same word problems and were asked to ascertain, and give explanations regarding the accuracy of the diagrams. The Küchemann algebra test was administered to the Secondary 2 students. Students from both grades drew longer bars to represent larger numbers. In contrast, findings from the validation task showed a more abstract appreciation for how the bar diagrams can be used. Primary 5 students who showed more abstract appreciations in the validation task were less likely to use the bar diagrams in a concrete fashion in the production task. Performance on the Küchemann algebra test was unrelated to performance on the production task or the validation task. The findings are discussed in terms of a production deficit, with students exhibiting a more sophisticated understanding of bar diagrams than is demonstrated by their usage
Web-based progress monitoring in first grade mathematics
Get PDFThe purpose of our research was to examine a web-based tool for mathematics progress monitoring in first grade. The newly developed assessment tool uses several robust indicators and curriculum-based measures forming three competences (Basic Precursors, Advanced Precursors, and Computation) to determine comprehensive early numeracy skills in regular education. 373 students completed a total of eight online tests every two or three weeks. Results indicate that delayed alternate-form reliability was adequate (rM = .78). Repeated measures analyses with post hoc comparisons were used to ascertain the sensitivity to assess learning growth. All three competences showed linear growth rates that were significant over time, but only Computation and overall scores produced dependable increases from test to test. Predictive validity was determined using two standardised school achievement tests (end of first grade, end of second grade). Results indicate high predictive validity of the first four online tests (rM = .67, rM = .66 for 6 months and 18 months prediction). Correlations with teacher ratings of their students' skills confirmed this pattern. Results from student and teacher questionnaires indicate that the students were able to conduct the tests independently and that a three-week interval was adequate for regular‑education use. Teachers stated to use the progress monitoring results diversely for classroom purposes. We conclude that the use of a web-based assessment setting with diverse measures is beneficial with respect to psychometric properties and feasibility for frequent use in regular education
Focusing on doctoral students’ experiences of engagement in the thesis work
Get PDFWhile doctoral students’ reasons for attrition and negative experiences have been explored for a long time, little is known about their engagement in their doctoral process. This study aimed at filling the gap in the doctoral education literature by exploring the nature of students’ engagement in doctoral work. Altogether, 21 behavioural sciences doctoral students from one top-level research community were interviewed. The interview data were qualitatively content analysed. The students described their engagement in terms of experiences of dedication, efficiency and sometimes absorption. The sources of their engagement were typically increased sense of competence and relatedness. They less often reported strengthened sense of autonomy and contribution as the sources. In addition, three qualitatively different experiences of engagement in doctoral work, adaptive engagement, agentic engagement and work-life inspired engagement were identified from the students’ descriptions. Further, there was a variation among the students in terms of what experiences of engagement they emphasized in different phases of their doctoral studies. Our results suggest that rather than being a singular entity doctoral student engagement in the doctoral work varies
Modelling for Prediction vs. Modelling for Understanding
Get PDFMusso et al. (2013) predict students’ academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can mainly be used for exploratory modelling. Moreover, the output generated from ANNs cannot be fully translated into a meaningful set of rules because they store information about input-output relations in a complex, distributed, and implicit way. These problems hamper systematic theory-building as well as communication and justification of model predictions in practical contexts. Modern-day regression techniques, including (Bayesian) structural equation models, have advantages similar to those of ANNs but without the drawbacks. They are able to handle numerous variables, non-linear effects, multi-way interactions, and incomplete data. Thus, researchers in the learning sciences should prefer more theory-driven and parsimonious modelling techniques over ANNs whenever possible
Team entitativity and teacher teams in schools: Towards a typology
Get PDFIn 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’.