Teaching Control Programming Using Programmable Automation Controllers

Introductory control programming was included as a required course for agricultural systems students. A programmable automation controller (PAC) was programmed with a flowchart paradigm to monitor and control applications. An ex post facto research design was used, with a questionnaire to obtain student feedback. The PAC instructional unit and student feedback are described. Ninety‐two percent of students agreed the PAC unit of the course helped improve their problem‐solving skills

Ilium 1986

The 1986 yearbook of Taylor University in Upland, Indiana.https://pillars.taylor.edu/yearbooks/1027/thumbnail.jp

Development of computer science online and preliminary validation of its efficacy as an instructional environment

CS Online was developed as an instructional environment to address many issues facing computer science education. One of these is the need to rekindle interest in introductory computer science. CS Online seeks to accomplish this by offering active learning experiences set in real-world contexts. The intended outcomes are increased interest in computer science as an academic discipline, increased enrollments in related courses, and increased achievement resulting from cognitive skills growth; The CS Online system generated data while 36 high school students solved programming problems, and questionnaires administered by the system were used to collect information about students\u27 self-regulatory skills and experience in math and computers. In addition, qualitative data analysis of source code submitted by students was conducted to determine how students progressed through the problem solving process and the common mistakes they made; The study revealed that students with differing levels of math and computer experience and self-regulatory skills were able to adequately complete programming problems using the system. The descriptive data on the 36 students indicated that students with high motivation seemed to outperform low motivation students in all performance measures in the study. Those who had high planning skills also seemed to outperform the low group in most of the performance measures. A similar pattern was observed in the students with high versus low math and computer skills. As the task difficulty increased, students with high planning skills seemed to require increasingly fewer attempts to complete exercises than those with lower planning skills. A qualitative analysis of problem solving revealed that students erred in syntax, logic, and then grammar---in that order. It was also shown that students spent considerable time re-running programs to observe output or to clean-up code; Although the findings suggest that in general motivation and planning seem to be important components of learning a programming language, the current descriptive findings should be interpreted with caution. Future studies with larger sample sizes are warranted. To examine effects of self-regulation on learning and performance, other relevant variables, such as existing computer language skills, may be included to control their effects on the performance; Additional findings suggest that the use of hints were helpful for students with lower math skills, computer skills, and motivation. Teachers can encourage the use of hints for those who need the extra help, but can discourage their use for the more highly skilled and motivated. The findings also suggest that, based on the types of mistakes students commonly made, instruction on debugging skills should be considered to reduce the number of syntax, logic, and grammar errors. Less time spent correcting errors becomes more time spent on problem solving. (Abstract shortened by UMI.)

Gestão de sistemas de informação: Relatório de disciplina contendo o programa, conteúdo e métodos de ensino

Este relatório apresenta a disciplina de Gestão de Sistemas de Informação, contendo o programa, o conteúdo e os métodos de ensino (de acordo com o número 2 do artigo 44º do Decreto Lei nº 448/79, de 13 de Novembro ratificado pela Lei Nº 19/80 de 16 de Julho), para a satisfação dos requisitos exigidos aos candidatos admitidos a concurso para provimento de lugar de Professor Associado, para o grupo disciplinar de Informática, para exercer funções no âmbito da disciplina de Sistemas de Informação. A Gestão de Sistemas de Informação é uma disciplina do plano de estudos dos Cursos de Mestrado e de Especialização em Sistemas de Informação, da Universidade do Minho. A disciplina de Gestão de Sistemas de Informação é descrita através das componentes usuais da especificação de unidades de ensino. Assim: É discutida e justificada a sua natureza bem como a finalidade que a justifica; São propostos os objectivos educacionais que pretende alcançar; São descritos os conteúdos programáticos que abarca; São justificadas e propostas as estratégias de ensino adoptadas; É proposto um plano de realização; São tecidas algumas considerações sobre a utilização de documentação de apoio e são apresentadas e comentadas as referências fundamentais; É justificado e proposto o modelo de avaliação a utilizar; São descritos os recursos necessários ao seu funcionamento. Para além de uma breve síntese da visão do autor sobre os aspectos fundamentais do domínio da Gestão de Sistemas de Informação, são ainda tecidas algumas considerações sobre os antecedentes e motivações desta disciplina, sobre o seu enquadramento no contexto profissional e da investigação, e sobre a sua inserção nos cursos onde irá ser leccionada

Bayesian Optimisation for Planning And Reinforcement Learning

This thesis addresses the problem of achieving efficient non-myopic decision making by explicitly balancing exploration and exploitation. Decision making, both in planning and reinforcement learning (RL), enables agents or robots to complete tasks by acting on their environments. Complexity arises when completing objectives requires sacrificing short-term performance in order to achieve better long-term performance. Decision making algorithms with this characteristic are known as non-myopic, and require long sequences of actions to be evaluated, thereby greatly increasing the search space size. Optimal behaviours need balance two key quantities: exploration and exploitation. Exploitation takes advantage of previously acquired information or high performing solutions, whereas exploration focuses on acquiring more informative data. The balance between these quantities is crucial in both RL and planning. This thesis brings the following contributions: Firstly, a reward function trading off exploration and exploitation of gradients for sequential planning is proposed. It is based on Bayesian optimisation (BO) and is combined to a non-myopic planner to achieve efficient spatial monitoring. Secondly, the algorithm is extended to continuous actions spaces, called continuous belief tree search (CBTS), and uses BO to dynamically sample actions within a tree search, balancing high-performing actions and novelty. Finally, the framework is extended to RL, for which a multi-objective methodology for explicit exploration and exploitation balance is proposed. The two objectives are modelled explicitly and balanced at a policy level, as in BO. This allows for online exploration strategies, as well as a data-efficient model-free RL algorithm achieving exploration by minimising the uncertainty of Q-values (EMU-Q). The proposed algorithms are evaluated on different simulated and real-world robotics problems, displaying superior performance in terms of sample efficiency and exploration