We Teach who We Are: Creativity in the Lives and Practices of Accomplished Teachers

2015 ◽  
Vol 117 (7) ◽  
pp. 1-46 ◽  
Author(s):  
Danah Henriksen ◽  
Punya Mishra
Keyword(s):  
Research Design ◽  
Real World ◽  
Teaching Practices ◽  
Classroom Practices ◽  
High Stakes Testing ◽  
Learning Approaches ◽  
Creative People ◽  
Exceptional Teachers ◽  
Excellent Teachers ◽  
Accomplished Teachers

Background/Context There is a strong sense in education that creativity should be nurtured in classroom settings, yet there is little understanding of how effective and creative teachers function. Existing research has recognized that successful/creative people in any discipline use creative avocations to enhance their professional thinking. Root-Bernstein demonstrated a strong connection between the professional and personal-life creativity of highly accomplished scientists, which has been applied to other disciplines. Until now, however, this phenomenon has not been applied to exemplary teachers. This study focuses on a broader picture of how exceptional teachers use creativity in the classroom. Purpose/Objective This study documents the ways in which successful, award-winning teachers function creatively in their classrooms. It investigates their beliefs about creativity in teaching—what “creativity” means, and how skilled teachers instantiate it in classroom practices. Finally, this research examined the teachers’ personal creativity (in terms of creative pursuits, hobbies, and habits of mind) and the practical ways this translates into teaching. Research Design A qualitative research design was used for in-depth interviews with highly accomplished teachers. Detailed interview data was gathered from eight recent National Teacher of the Year award winners/finalists, to investigate creative classroom practices and beliefs about creativity among exceptional teachers across varied teaching contexts. Qualitative coding of phenomenological research described important themes arising from the creative practices and beliefs of the participant teachers. Findings Findings reveal how excellent teachers actively cultivate a creative mindset. Results show how excellent teachers are highly creative in their personal and professional lives, and that they actively transfer creative tendencies from their outside avocations/interests into their teaching practices. This study describes common themes in creative teaching, including intellectual risk taking, real-world learning approaches, and cross-disciplinary teaching practices. Conclusions/Recommendations Current U.S. educational policy, with its emphasis on high-stakes testing and scripted, “teacher-proof” curricula, have impeded creativity in teaching and learning. Based on the findings of this study, suggestions for curricula include the incorporation of teachers’ unique personal creative interests in lessons, along with infusion of the arts and music across varied disciplinary content. Teacher education programs and professional development courses should include a focus on both real-world, cross-disciplinary lesson planning, while administrators and policymakers should support opportunities for teachers to take creative and/or intellectual risks in their work.

Qualitative Case Study Research Design

2015 ◽  
Vol 25 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Jennifer Tetnowski
Keyword(s):  
Research Design ◽  
Real World ◽  
Qualitative Case Study ◽  
Case Study Research ◽  
Study Research

Qualitative case study research can be a valuable tool for answering complex, real-world questions. This method is often misunderstood or neglected due to a lack of understanding by researchers and reviewers. This tutorial defines the characteristics of qualitative case study research and its application to a broader understanding of stuttering that cannot be defined through other methodologies. This article will describe ways that data can be collected and analyzed.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

scholarly journals Reinforcement Learning Approaches in Social Robotics

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1292
Author(s):  
Neziha Akalin ◽  
Amy Loutfi
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Social Robotics ◽  
Research Field ◽  
Social Robots ◽  
Learning Approaches ◽  
Reward Function ◽  
Optimal Behavior ◽  
Learning Challenges ◽  
Starting Point

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

Can Teachers’ Self-Reported Efficacy, Concerns, and Attitudes Toward Inclusion Scores Predict Their Actual Inclusive Classroom Practices?

2015 ◽  
Vol 40 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Umesh Sharma ◽  
Laura Sokal
Keyword(s):  
Teaching Practices ◽  
Teacher Educators ◽  
Classroom Practices ◽  
Inclusive Classrooms ◽  
Attitudes Toward Inclusion ◽  
Inclusive Classroom ◽  
Significant Relationships ◽  
Positive Attitudes ◽  
Classroom Behaviour ◽  
Inclusive Teaching

This research was undertaken to determine if significant relationships exist between teachers’ self-reported attitudes, concerns, and efficacy to teach in inclusive classrooms and their actual classroom behaviour in Winnipeg, Canada. Five teachers completed 3 scales measuring their attitudes to inclusion, their level of concerns about teaching in inclusive classrooms, and their level of efficacy for teaching in inclusive classrooms. They were observed using a newly developed scale to measure their inclusive teaching practices. Each teacher was observed from 3 to 5 hours on different occasions. Data were analysed using 1-tailed Spearman correlations. Results indicated that teachers who were highly inclusive in their classroom practices tended to have significantly lower degrees of concerns and positive attitudes to inclusion. Implications of the research for policymakers, future researchers, and teacher educators are discussed.

scholarly journals Implications of Dialogic Teaching: Practices of and Reflections from English Language Teachers

2021 ◽  
Vol 2 (1) ◽  
pp. 375-381
Author(s):  
Dr. Liaqat Iqbal ◽  
Sahibzada Aurangzeb ◽  
Farooq Shah
Keyword(s):  
Teaching Practices ◽  
English Language ◽  
Classroom Practices ◽  
Language Teachers ◽  
Local Context ◽  
Dialogic Teaching ◽  
Social Entity ◽  
English Language Teachers ◽  
Negative Impacts ◽  
And Control

Researches often endorse discussion, dialogues, and other learning tasks for the promotion of fluency, critical thinking, reasoning, and ability to evaluate and justifying. Keeping in view the Pakistani context, especially, the local context, it is not clear what type of classroom practices prevail in the region and what reflections teachers have about the use of such practices. Taking Bakhtin's and Vygotsky's ideas of dialogism and learning as a social entity, the present study aimed at knowing the teaching practices of English language teachers from the perspective of dialogic teaching and also at exploring how do teachers reflect on such a teaching approach. For this purpose, English Language Centers of district Mardan were taken as data sources where twenty classrooms were observed for classroom practices and the concerned teachers were interviewed for their reflections. It was found that the teachers use of dialogic teaching having positive and negative impacts. The positive impacts of dialogic teaching include creativity, thinking ability, confidence building, and other social impacts. It has little negative impacts that include challenges for the teachers in terms of behavior problems and control of talks.

scholarly journals Good Practice in Inclusive Education: Participatory Reinterpretation of Already Existing Elaborate Classroom Practices Under a UDL Perspective

2021 ◽  
pp. 279-312
Author(s):  
Michelle Proyer ◽  
Gertraud Kremsner ◽  
Gottfried Biewer
Keyword(s):  
Learning Environments ◽  
Inclusive Education ◽  
Teaching Practices ◽  
Classroom Practices ◽  
Good Practice ◽  
School Development ◽  
Educational Practices ◽  
Teachers Perspectives ◽  
Design Of Learning Environments

AbstractThis chapter presents well-established educational practices implemented at a school in Vienna with two decades of experience in school development in the context of inclusion. It elaborates on how these existing teaching practices can be interpreted from a UDL perspective. Furthermore, this chapter aims to underline the importance of engaging with teachers’ perspectives in research efforts regarding the design of learning environments. Findings point to the advantages that the emphasized consideration of localized and societal backgrounds of students could add to the purposeful application of UDL.

Efficient inference for agent-based models of real-world phenomena

2021 ◽  
Author(s):  
Andreas Christ Sølvsten Jørgensen ◽  
Atiyo Ghosh ◽  
Marc Sturrock ◽  
Vahid Shahrezaei
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Parameter Space ◽  
Real World ◽  
Autonomous Agents ◽  
Stochastic Simulations ◽  
Model Parameters ◽  
Learning Approaches ◽  
Real World Applications ◽  
Real World Problems

AbstractThe modelling of many real-world problems relies on computationally heavy simulations. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue based on machine learning methods. One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumnavigate the need for Bayesian sampling schemes and directly estimate the posterior distribution. We focus on stochastic simulations that track autonomous agents and present two case studies of real-world applications: tumour growths and the spread of infectious diseases. We demonstrate that good accuracy in inference can be achieved with a relatively small number of simulations, making our machine learning approaches orders of magnitude faster than classical simulation-based methods that rely on sampling the parameter space. However, we find that while some methods generally produce more robust results than others, no algorithm offers a one-size-fits-all solution when attempting to infer model parameters from observations. Instead, one must choose the inference technique with the specific real-world application in mind. The stochastic nature of the considered real-world phenomena poses an additional challenge that can become insurmountable for some approaches. Overall, we find machine learning approaches that create direct inference machines to be promising for real-world applications. We present our findings as general guidelines for modelling practitioners.Author summaryComputer simulations play a vital role in modern science as they are commonly used to compare theory with observations. One can thus infer the properties of a observed system by comparing the data to the predicted behaviour in different scenarios. Each of these scenarios corresponds to a simulation with slightly different settings. However, since real-world problems are highly complex, the simulations often require extensive computational resources, making direct comparisons with data challenging, if not insurmountable. It is, therefore, necessary to resort to inference methods that mitigate this issue, but it is not clear-cut what path to choose for any specific research problem. In this paper, we provide general guidelines for how to make this choice. We do so by studying examples from oncology and epidemiology and by taking advantage of developments in machine learning. More specifically, we focus on simulations that track the behaviour of autonomous agents, such as single cells or individuals. We show that the best way forward is problem-dependent and highlight the methods that yield the most robust results across the different case studies. We demonstrate that these methods are highly promising and produce reliable results in a small fraction of the time required by classic approaches that rely on comparisons between data and individual simulations. Rather than relying on a single inference technique, we recommend employing several methods and selecting the most reliable based on predetermined criteria.

Sign in / Sign up

Export Citation Format

Share Document