Experiential Learning Activities in the Weed Science Classroom

2007 ◽  
Vol 21 (1) ◽  
pp. 255-261 ◽  
Author(s):  
Robert S. Gallagher ◽  
Edward C. Luschei ◽  
Eric Gallandt ◽  
Antonio DiTommaso
Keyword(s):  
Experiential Learning ◽  
Pest Management ◽  
Science Classroom ◽  
Effective Means ◽  
Learning Activities ◽  
Academic Disciplines ◽  
Learning Approaches ◽  
Weed Ecology ◽  
Weed Science ◽  
Wide Range

Considerable discussion has occurred among the weed science community regarding the potential benefits and limitations of integrated approaches to crop and pest management. This discussion also needs to occur in our weed science classrooms, where students from a wide range of academic disciplines are trained in the fundamentals of weed ecology and management. Although the inherent complexity of integrated crop and pest management can make this adaptation to our weed science courses challenging, the use of experiential learning techniques provides an effective means to promote understanding and retention of these concepts. This paper outlines several classroom activities based on the experiential learning approaches that have been implemented by the authors. The activities focus on (1) weed identification and natural history, (2) weed population processes, and (3) integrated management systems. For each activity, we offer our rationale for the exercise, an example of its implementation in the classroom setting, potential pitfalls, and student feedback regarding their perceptions of the activity's educational value. With this paper, we hope to provide examples that may be useful to other weed science educators wishing to incorporate more experiential learning activities into their courses and to initiate a dialogue between educators that can help our community improve and enliven weed science education.

Mobile Affordances and Learning Theories in Supporting and Enhancing Learning

2017 ◽  
Vol 9 (2) ◽  
pp. 61-73 ◽  
Author(s):  
Kathryn MacCallum ◽  
Stephanie Day ◽  
David Skelton ◽  
Michael Verhaart
Keyword(s):  
Experiential Learning ◽  
Mobile Learning ◽  
Learning Theory ◽  
Mobile Technology ◽  
Learning Activities ◽  
Learning Theories ◽  
Learning Approaches ◽  
Learning Practices ◽  
Support Students ◽  
Specific Learning

Mobile technology promises to enhance and better support students' learning. The exploration and adoption of appropriate pedagogies that enhance learning is crucial for the wider adoption of mobile learning. An increasing number of studies have started to address how existing learning theory can be used to underpin and better frame mobile learning activities. In particular, there are a number of learning theories that have been identified which particularly lend themselves to the specific affordances of mobile learning. This paper examines how mobile technology was incorporated within three different computing courses. These case studies explore how specific learning approaches (collaborative learning, connectivism and experiential learning) were adopted to frame the use of the technology within each course and how the affordances of mobile technology were harnessed to enhance and better support existing learning practices.

scholarly journals Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments

2020 ◽  
Vol 29 (02) ◽  
pp. 2040004
Author(s):  
Nikolaos Spatiotis ◽  
Isidoros Perikos ◽  
Iosif Mporas ◽  
Michael Paraskevas
Keyword(s):  
Sentiment Analysis ◽  
Learning Activities ◽  
Educational Systems ◽  
Training Procedure ◽  
Learning Approaches ◽  
Greek Language ◽  
Life Long Learning ◽  
Wide Range ◽  
Training Activities ◽  
Analysis System

Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of learning progress during the course or to improve future courses. Learners-generated content and their feedback and comments can provide indicative information about the educational procedures that they attended and the training activities that they participated in. Educational systems must possess mechanisms to analyze learners’ comments and automatically specify their opinions and attitude towards the courses and the learning activities that are offered to them. This paper describes a Greek language sentiment analysis system that analyzes texts written in Greek language and generates feature vectors which together with classification algorithms give us the opportunity to classify Greek texts based on the personal opinion and the degree of satisfaction expressed. The sentiment analysis module has been integrated into the hybrid educational systems of the Greek school network that offers life-long learning courses. The module offers a wide range of possibilities to lecturers, policymakers and educational institutes that participate in the training procedure and offers life-long learning courses, to understand how their learners perceive learning activities and specify what aspects of the learning activities they liked and disliked. The experimental study show quite interesting results regarding the performance of the sentiment analysis methodology and the specification of users’ opinions and satisfaction. The feature analysis demonstrates interesting findings regarding the characteristics that provide indicative information for opinion analysis and embeddings combined with deep learning approaches yield satisfactory results.

Fostering Collaborative Autonomy: The Roles of Cooperative and Collaborative Learning

Relay Journal ◽  
2018 ◽  
pp. 360-381
Author(s):  
Gordon Myskow ◽  
Phillip A. Bennett ◽  
Hisako Yoshimura ◽  
Kyoko Gruendel ◽  
Takuto Marutani ◽  
...  
Keyword(s):  
Collaborative Learning ◽  
Cooperative Learning ◽  
Learner Autonomy ◽  
The Other ◽  
Learning Activities ◽  
Educational Context ◽  
Learning Approaches ◽  
Group Interactions ◽  
Collaborative Autonomy ◽  
Over Time

The distinction between Cooperative and Collaborative Learning approaches is not a clear one. Some use the terms interchangeably while others consider Cooperative Learning to be a type of Collaborative Learning. Still others clearly differentiate between them, characterizing Cooperative Learning as more highly structured in its procedures, involving a great deal of intervention by the teacher to plan and orchestrate group interactions. Collaborative Learning, on the other hand, presupposes some degree of learner autonomy－that groups can work effectively toward shared goals and monitor their own progress. This paper takes the view that the distinction between Cooperative and Collaborative Learning is a useful one and that both approaches can play valuable roles in fostering autonomous interaction. It argues that while Collaborative Learning formations may be the ultimate goal for teachers wishing to develop learner autonomy, Cooperative Learning is a valuable means for modeling the skills and abilities to help students get there. The discussion begins with an overview of the two approaches, focusing on their implementation in the Japanese educational context. It then presents seven highly structured Cooperative Learning activities and shows how they can be modified and extended over time to encourage more autonomous interaction.

The State versus the People

2020 ◽  
Author(s):  
Matthew Rendle
Keyword(s):  
French Revolution ◽  
Effective Means ◽  
State Control ◽  
Scarce Resources ◽  
Soviet State ◽  
The People ◽  
Wide Range ◽  
Complex Picture ◽  
The Military

This book provides the first detailed account of the role of revolutionary justice in the early Soviet state. Law has often been dismissed by historians as either unimportant after the October Revolution amid the violence and chaos of civil war or even, in the absence of written codes and independent judges, little more than another means of violence. This is particularly true of the most revolutionary aspect of the new justice system, revolutionary tribunals—courts inspired by the French Revolution and established to target counter-revolutionary enemies. This book paints a more complex picture. The Bolsheviks invested a great deal of effort and scarce resources into building an extensive system of tribunals that spread across the country, including into the military and the transport network. At their peak, hundreds of tribunals heard hundreds of thousands of cases every year. Not all ended in harsh sentences: some were dismissed through lack of evidence; others given a wide range of sentences; others still suspended sentences; and instances of early release and amnesty were common. This book, therefore, argues that law played a distinct and multifaceted role for the Bolsheviks. Tribunals stood at the intersection between law and violence, offering various advantages to the Bolsheviks, not least strengthening state control, providing a more effective means of educating the population on counter-revolution, and enabling a more flexible approach to the state’s enemies. All of this adds to our understanding of the early Soviet state and, ultimately, of how the Bolsheviks held on to power.

Career Development Theory: An Integrated Analysis

2020 ◽  
Author(s):  
Julia Yates
Keyword(s):  
Career Choice ◽  
Subject Matter ◽  
Academic Disciplines ◽  
Development Theory ◽  
Integrated Analysis ◽  
Theoretical Frameworks ◽  
Wide Range ◽  
Comprehensive Picture ◽  
The Subject ◽  
Career Practice

Career theories are developed to help make sense of the complexity of career choice and development. The intricacy of the subject matter is such that career theories most often focus on one or two aspects of the phenomenon. As such, the challenges of integrating the theories with each other, and integrating them within career practice, are not insignificant. In this chapter, an overview of the theoretical landscape is offered that illustrates how the theories align with each other to build up a comprehensive picture of career choice and development. The chapter introduces a wide range of theoretical frameworks, spanning seven decades and numerous academic disciplines, and discusses the most well-known theorists alongside less familiar names. The chapter is structured around four concepts: identity, environment, career learning, and psychological career resources. Suggestions are offered for the incorporation of theories in career practice.

scholarly journals Mechatronics: Experiential Learning and the Stimulation of Thinking Skills

2021 ◽  
Vol 11 (2) ◽  
pp. 46
Author(s):  
Maki K. Habib ◽  
Fusaomi Nagata ◽  
Keigo Watanabe
Keyword(s):  
Critical Thinking ◽  
Experiential Learning ◽  
Learning Process ◽  
Laboratory Experiments ◽  
Thinking Skills ◽  
Project Based Learning ◽  
Critical Thinking Skills ◽  
Learning Approach ◽  
Learning Approaches ◽  
Experimental Systems

The development of experiential learning methodologies is gaining attention, due to its contributions to enhancing education quality. It focuses on developing competencies, and build-up added values, such as creative and critical thinking skills, with the aim of improving the quality of learning. The interdisciplinary mechatronics field accommodates a coherent interactive concurrent design process that facilitates innovation and develops the desired skills by adopting experiential learning approaches. This educational learning process is motivated by implementation, assessment, and reflections. This requires synergizing cognition, perception, and behavior with experience sharing and evaluation. Furthermore, it is supported by knowledge accumulation. The learning process with active student’s engagement (participation and investigation) is integrated with experimental systems that are developed to facilitate experiential learning supported by properly designed lectures, laboratory experiments, and integrated with course projects. This paper aims to enhance education, learning quality, and contribute to the learning process, while stimulating creative and critical thinking skills. The paper has adopted a student-centered learning approach and focuses on developing training tools to improve the hands-on experience and integrate it with project-based learning. The developed experimental systems have their learning indicators where students acquire knowledge and learn the target skills through involvement in the process. This is inspired by collaborative knowledge sharing, brainstorming, and interactive discussions. The learning outcomes from lectures and laboratory experiments are synergized with the project-based learning approach to yield the desired promising results and exhibit the value of learning. The effectiveness of the developed experimental systems along with the adopted project-based learning approach is demonstrated and evaluated during laboratory sessions supporting different courses at Sanyo-Onoda City University, Yamaguchi, Japan, and at the American University in Cairo.

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.

Exploring business doctoral students attitudes, training, and use of classroom experiential learning activities

2021 ◽  
Vol 19 (2) ◽  
pp. 100493
Author(s):  
Andrew A. Bennett ◽  
Kevin D. Lo ◽  
Adam Pervez ◽  
Terry A. Nelson ◽  
Kenneth Mullane ◽  
...  
Keyword(s):  
Experiential Learning ◽  
Doctoral Students ◽  
Learning Activities ◽  
Students Attitudes

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

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