The problem of this study was an analysis of results obtained by a computer-assisted instructional approach and a lecture-demonstration instructional approach of teaching mathematical concepts in the area of office machines at the community college level. The purposes of this study were as follows: (1) to determine which method, the lecture-demonstration or computer assisted instruction, will produce the better mathematical skill in office machines; (2) to determine the effectiveness of computer-assisted instruction as compared to the lecture demonstration approach on the student’s attitude toward office machines, as measured by the Purdue attitude scale; (3) to compare the correlation between attitude and achievement for the computer-assisted instruction group and the lecture-demonstration group; and (4) to compare the correlation between attitude and achievement for high-ability students and low-ability students, based on scores obtained from the Otis-Lennon Mental Ability Test. The findings in this study indicate that there were no significant differences in mathematical achievement, attitude scores, or the correlation between attitude and achievement between students in the computer-assisted group and students in the lecture-demonstration group. The following conclusions were formulated from an analysis of this study. 1. Based on the mathematical achievement scores and the statistical analysis presented in this study, it is concluded that there are no demonstrated differences between the computer-assisted instructional approach and the lecture demonstration approach for teaching applied mathematical concepts to business machines students. 2. Based on the attitude scores presented in this study, students in both groups appear to have a favorable attitude toward the business machines course. Attitude scores for students in the computer-assisted group were not significantly different from scores for students in the lecture-demonstration group. 3. The integration of business machines and applied mathematics, in addition to developing speed and accuracy on electronic calculators and adding machines, produces increased achievement in mathematics.

The intent of this thesis is to examine white, rural women of the South who were directly affected by home demonstration between 1920 - 1950 and to discuss their roles as producers and consumers in the expanding market economy. Home demonstration, a three-tiered bureaucratic agency that provided domestic education and production techniques to Southern women, played a major role in guiding women toward the expanding market economy. Agents often had to temper their programs in order to compromise with the women they served to accommodate rural restrictions on capital, capability, and confidence. By integrating rural women into a more modernized, less isolated, and more urbanized environment, home demonstration hoped to improve the lives of women through its focus on sanitation, nutrition, and efficiency within household production.

No description available.

The purpose of the present study was to determine the comparative effectiveness of the educational sound film and the demonstration method in teaching the quick-mix method of cake making.

The purpose of this study was to analyze how two major online newspapers, Aftonbladet.se & Expressen.se, have framed and portrayed the actions of the environmental organization Återställ Våtmarker in their reporting. Additionally, the study also examines whether the portrayal of the organization has changed over time. A qualitative content analysis was conducted on 23 articles. The analysis was based on framing and agenda setting theory and included both text and image analysis. The results of the study showed that initially in the first half of 2022 Återställ Våtmarker was framed as a “peaceful” organization, where the conflicts and drama arose due to public reactions and actions. This however changed in the later half of 2022, Återställ Våtmarker began to be framed and portrayed as an organization who was seeking conflicts and drama. The articles and their reporting began to indicate that their primary focus was to highlight the demonstrations and actions as “annoying” and “disruptive” to the public. But also how “chaotic” scenes and “turmoil” constantly arose due to Återställ Våtmarker and their demonstrations. However, it can be argued that the reporting was a textual overdramatization as there was a clear discrepancy between the text and the images in the reporting, Thus, it can be speculated that the articles overdramatized the text to attract more readers, which in turn leads to more revenue for the newspaper.

Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly because they are expected to learn a task from scratch merely through an agent's own experience. In this thesis, we show that learning from scratch is a limiting factor for the learning performance, and that when prior knowledge is available RL agents can learn a task faster. We evaluate relevant previous work and our own algorithms in various experiments. Our first contribution is the first implementation and evaluation of an existing interactive RL algorithm in a real-world domain with a humanoid robot. Interactive RL was evaluated in a simulated domain which motivated us for evaluating its practicality on a robot. Our evaluation shows that guidance reduces learning time, and that its positive effects increase with state space size. A natural follow up question after our first evaluation was, how do some other previous works compare to interactive RL. Our second contribution is an analysis of a user study, where na"ive human teachers demonstrated a real-world object catching with a humanoid robot. We present the first comparison of several previous works in a common real-world domain with a user study. One conclusion of the user study was the high potential of RL despite poor usability due to slow learning rate. As an effort to improve the learning efficiency of RL learners, our third contribution is a novel human-agent knowledge transfer algorithm. Using demonstrations from three teachers with varying expertise in a simulated domain, we show that regardless of the skill level, human demonstrations can improve the asymptotic performance of an RL agent. As an alternative approach for encoding human knowledge in RL, we investigated the use of reward shaping. Our final contributions are Static Inverse Reinforcement Learning Shaping and Dynamic Inverse Reinforcement Learning Shaping algorithms that use human demonstrations for recovering a shaping reward function. Our experiments in simulated domains show that our approach outperforms the state-of-the-art in cumulative reward, learning rate and asymptotic performance. Overall we show that human demonstrators with varying skills can help RL agents to learn tasks more efficiently.

I denna studie försöker vi finna svar på hur en praktisk lärardemonstration kan utföras på ett pedagogiskt vis. Vi undersöker även övergången från den stund som domineras av läraren till den stund som utmärks av individuellt elevarbete. Skolämnet som studien utgår ifrån är slöjd men vi tror att de utmaningar och resultat som framkommer kan appliceras även på andra skolämnen. Från svar på enkäter riktade till elever och intervjuer utförda med både lärare och elever framkommer att ett flertal aspekter spelar in för ett framgångsrikt lärande knutet till  demonstrationer. Lärarens kapacitet och förmåga att både förbereda och framföra något är viktig. Samtidigt spelar lärarens kontakt med och kunskap om, både grupp och individ, roll. Återkoppling till elever bör ske med fokus på det som utförts och som eleven kan påverka. Även faktorer av mer praktiska slag påverkar, som vid vilken tid på dagen lektionen pågår och om eleverna då är pigga eller ofokuserade. Att få demonstrationen och det efterkommande elevarbetet att vara en lärorik stund istället för något tråkigt är en utmanande, komplex och rolig del av läraryrket. Att lyckas med det kan vara avgörande för elevernas förståelse och lust att engagera sig i ämnet. / In this study we try to find answers on how a practical demonstration, performed by a teacher, can be conducted in both effective and pedagogic ways. We also explore the transition from a teacher lead period to a period defined by practical work and understanding performed by pupils. The subject studied is the swedish sloyd, but we think the challenges are mainly the same in any school subject. Through research on literature in areas such as rhetorics, leadership and pedagogy we contrast different aspects on the subject with surveys conducted on pupils and interviews with both pupils and teachers. From empirics we conclude that different aspects play important roles in succeeding with bringing a sloyd demonstration into individual pupil knowledge. The teacher’s capacity in preparation and enthralling an audience as well as his or her empathy and knowledge of the mental constitution of the class all play a part. It is also important how well the teacher gives individualized feedback, when pupils start working or no longer do work due to various reasons. Practical factors such as hour of the day and whether the pupils are alert or hungry also do contribute.  Bringing a demonstration from a dull experience into engaged pupil work is a challenging, complex and enjoyable part of a lesson. It may be crucial to the pupil’s future effort making and understanding.

<p>Robots have become increasingly common in various sectors, such as manufacturing, healthcare, and service industries. With the growing demand for automation and the expectation for interactive and assistive capabilities, robots must learn to adapt to unpredictable environments like humans can. This necessitates the development of learning methods that can effectively enable robots to collaborate with humans, learn from them, and provide guidance. Human experts commonly teach their collaborators to perform tasks via a few demonstrations, often followed by episodes of coaching that refine the trainee’s performance during practice. Adopting a similar approach that facilitates interactions to teaching robots is highly intuitive and enables task experts to teach the robots directly. Learning from Demonstration (LfD) is a popular method for robots to learn tasks by observing human demonstrations. However, for contact-rich tasks such as cleaning, cutting, or writing, LfD alone is insufficient to achieve a good performance. Further, LfD methods are developed to achieve observed goals while ignoring actions to maximize efficiency. By contrast, we recognize that leveraging human social learning strategies of practice and coaching in conjunction enables learning tasks with improved performance and efficacy. To address the deficiencies of learning from demonstration, we propose a Coaching by Demonstration (CbD) framework that integrates LfD-based practice with sparse coaching interactions from a human expert.</p> <p><br></p> <p>The LfD-based practice in CbD was implemented as an end-to-end off-policy reinforcement learning (RL) agent with the action space and rewards inferred from the demonstration. By modeling the reward as a similarity network trained on expert demonstrations, we eliminate the need for designing task-specific engineered rewards. Representation learning was leveraged to create a novel state feature that captures interaction markers necessary for performing contact-rich skills. This LfD-based practice was combined with coaching, where the human expert can improve or correct the objectives through a series of interactions. The dynamics of interaction in coaching are formalized using a partially observable Markov decision process. The robot aims to learn the true objectives by observing the corrective feedback from the human expert. We provide an approximate solution by reducing this to a policy parameter update using KL divergence between the RL policy and a Gaussian approximation based on coaching. The proposed framework was evaluated on a dataset of 10 contact-rich tasks from the assembly (peg-insertion), service (cleaning, writing, peeling), and medical domains (cricothyroidotomy, sonography). Compared to baselines of behavioral cloning and reinforcement learning algorithms, CbD demonstrates improved performance and efficiency.</p> <p><br></p> <p>During the learning process, the demonstrations and coaching feedback imbue the robot with expert knowledge of the task. To leverage this expertise, we develop a reverse coaching model where the robot can leverage knowledge from demonstrations and coaching corrections to provide guided feedback to human trainees to improve their performance. Providing feedback adapted to individual trainees' "style" is vital to coaching. To this end, we have proposed representing style as objectives in the task null space. Unsupervised clustering of the null-space trajectories using Gaussian mixture models allows the robot to learn different styles of executing the same skill. Given the coaching corrections and style clusters database, a style-conditioned RL agent was developed to provide feedback to human trainees by coaching their execution using virtual fixtures. The reverse coaching model was evaluated on two tasks, a simulated incision and obstacle avoidance through a haptic teleoperation interface. The model improves human trainees’ accuracy and completion time compared to a baseline without corrective feedback. Thus, by taking advantage of different human-social learning strategies, human-robot collaboration can be realized in human-centric environments. </p> <p><br></p>

This thesis analyzes the kinds of verbal and nonverbal signals elites manifest to show leadership qualities. Launching from Max Weber’s conceptual framework of charisma as a power term and Harold Lasswell’s study of propaganda, this study takes a multidisciplinary approach to studying political leadership with elements of communication methodology and an ontological basis in evolutionary psychology. The study’s goal is to offer a framework for defining and evaluating the diverse signal patterns employed by political elites in three real-life situations. These are the Malta Summit, the 1992 Virginia Presidential Debate, and the 2012 South Carolina Republican Presidential Primary. The cases were chosen because they display a diverse set of signal variations during different types of interactions. The three case studies are evaluated by measuring frequency and patterns of occurrence of the five different interaction constructs (indicator of interest, indicator of disinterest, demonstration of high value, demonstration of low value, and compliance testing) to explain different interaction patterns. A simple frequency distribution of the different signals during a given interaction is used to display the empirical findings and to compare patterns across the case studies. This study reveals that the presence of DLV (demonstration of low value) signals weaken an elite’s position in relation to other elites and the public while the presence of DHV (demonstration of high value) signals strengthen an elite’s position. It is largely the presence, absence, and frequency of these two signals that determines who conveys leadership qualities effectively regardless of leadership style. Studying the signaling patterns of political elites would allow scholars to understand better the kinds of signal patterns and signal frequencies that are used in different types of leadership styles and norm ranges for signals including for political elites belonging to different cultures and subcultures

Typescript (photocopy). / Digitized by Kansas Correctional Industries