Preservice Special Education Teachers' Reflections on Their Developing Professional Awareness Via In-Context Learning

The highly complex demands of the profession often overwhelm new teachers. Burnout and attrition are known consequences of inadequate preparation, burdening the education system with costly turnover and schools with disproportionate numbers of inexperienced teachers, including special educators. Many teacher preparation programs embed field experiences as a formative aspect of professional development with the intention of exposing candidates to the realities and demands of the profession. Little is known, however, about the ways in which preservice teachers’ professional awareness develops via in-context learning through field-based experiences. Using an exploratory, qualitative approach we analyzed end of course reflections of 31 special education preservice teachers for insights into the development of their professional awareness (professional roles and responsibilities) following a strategically aligned field experience. Findings suggest that participants expressed a growing understanding of professional practices, teacher dispositions, and professional relationships. A discussion of the findings and implications for teacher preparation are presented.     

Pedagogical possibilities for unruly bodies

Project Re?Vision uses disability arts to disrupt stereotypical understandings of disability and difference that create barriers to healthcare. In this paper, we examine how digital stories produced through Re?Vision disrupt biopedagogies by working as body-becoming pedagogies to create non-didactic possibilities for living in/with difference. We engage in meaning making about eight stories made by women and trans people living with disabilities and differences, with our interpretations guided by the following considerations: what these stories ‘teach’ about new ways of living with disability; how these stories resist neoliberalism through their production of new possibilities for living; how digital stories wrestle with representing disability in a culture in which disabled bodies are on display or hidden away; how vulnerability and receptivity become ‘conditions of possibility’ for the embodiments represented in digital stories; and how curatorial practice allows disability-identified artists to explore possibilities of ‘looking back’ at ableist gazes.     

Optimization of Maximal Rate of Heart Rate Increase Assessment in Runners

Purpose: Correlations between fatigue-induced changes in exercise performance and maximal rate of heart rate (HR) increase (rHRI) may be affected by exercise intensity during assessment. This study evaluated the sensitivity of rHRI for tracking performance when assessed at varying exercise intensities. Method: Performance (time to complete a 5-km treadmill time-trial [5TTT]) and rHRI were assessed in 15 male runners following 1 week of light training, 2 weeks of heavy training (HT), and a 10-day taper (T). Maximal rate of HR increase (measured in bpm·s^?1) was the first derivative maximum of a sigmoidal curve fit to HR data recorded during 5 min of running at 8 km·h^?1 (rHRI_8km·h^?1), and during subsequent transition to 13 km·h^?1 (rHRI_{8–13km·h^?1}) for a further 5 min. Results: Time to complete a 5-km treadmill time-trial was likely slower following HT (effect size ± 90% confidence interval = 0.16 ± 0.06), and almost certainly faster following T (–0.34 ± 0.08). Maximal rate of HR increase during 5 min of running at 8 km·h^?1 and rHRI_{8–13km·h^?1} were unchanged following HT and likely increased following T (0.77 ± 0.45 and 0.66 ± 0.62, respectively). A moderate within-individual correlation was found between 5TTT and rHRI_8km·h^?1 (r value ± 90% confidence interval = –.35 ± .32). However, in a subgroup of athletes (n = 7) who were almost certainly slower to complete the 5TTT (4.22 ± 0.88), larger correlations were found between the 5TTT and rHRI_8km·h^?1 (r = –.84 ± .22) and rHRI_{8–13km·h^?1} (r = –.52 ± .41). Steady-state HR during rHRI assessment in this group was very likely greater than in the faster subgroup (≥ 1.34 ± 0.86). Conclusion(s): The 5TTT performance was tracked by both rHRI_8km·h^?1 and rHRI_{8–13km·h^?1}. Correlations between rHRI and performance were stronger in a subgroup of athletes who exhibited a slower 5TTT. Individualized workloads during rHRI assessment may be required to account for varying levels of physical conditioning.     

The Use and Effectiveness of Palmtop Computers in Education

This paper reports how palmtop computers were used in an English secondary school over the course of a year. In particular, it concentrates on the perceived effectiveness of such computers in an educational setting, and on how teachers' and students' knowledge and use of "content-free" applications increased over that time. The benefits and potential problems with this new technology are discussed along with the consequences it has for classroom and school organisation.     

Girls and boys and practical science

A critical examination of the evidence from the literature suggests that there are more similarities than differences between the performances of girls and boys on practical laboratory tasks, notwithstanding well‐established, sex‐related differences in areas of interest, science‐relevant experience and confidence. This premise is supported by an analysis, with differentiation by sex, of the Techniques for Assessment of Practical Skills in Science Project (TAPs)? data, gathered from 1122 Scottish pupils 15‐16 years old, considered by their teachers to be low achievers in science.

It is argued that, by neglecting the assessment of practical work, there has been a failure to capitalize on the generally favourable attitudes of pupils, particularly those of girls, to this component of science courses, and that a formative, diagnostic and criterion‐referenced approach to assessment, together with the development of a sensitive reporting system, has the potential to enhance teaching and learning. An assessment system with these characteristics was developed and the resulting profiles of achievement, compiled by the class teacher, were used for purposes of national certification. Novel features of the scheme may have contributed to the better performances ‘overall’ by the girls in the sample.     

Using causal models to bridge the divide between big data and educational theory

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.