114343 Healthy Workplaces Semester 2 2024

Java Python 114343

Healthy Workplaces

COURSE GUIDE

Semester 2, 2024

What is this course about?

Summary of the course

Welcome to 114343 Healthy Workplaces! In this course, we will explore the key elements that contribute to a healthy work environment. The course will begin with an overview of the evolution of work and establish a theoretical foundation by introducing various theories in workplace well-being literature. We will also delve into various psychosocial risk factors that can affect individual well-being and organisational performance, examine individual coping strategies to manage stress and enhance personal resilience, and discuss effective organisational interventions designed to promote a positive and supportive work environment. This content will be examined within the social, cultural, and legal contexts of both New Zealand and international literature.

Course student learning outcomes

•    Identify and explain relevant theories, principles, and factors required for creating and sustaining healthy workplaces.

•    Identify and analyse the impact of relevant factors on creating and sustaining healthy workplaces.

•    Identify and critically evaluate appropriate responses for managing psychosocial risk factors and outcomes in work contexts.

Relationship to other courses

This course is a progression from 114.241 (Principles of Human Resources Management),  114.254 (Employment Relations), and 114.240 (Organisational Behaviour) . This course is a subject source for the BBus major in Human Resource Management and Employment Relations. For someone not enrolled in this major, this course would help give you theoretical and applied knowledge of psychosocial risk factors and the identification and evaluation of appropriate strategies for creating and sustaining healthy workplaces.

Course role in development of programme learning goals

In addition to being a subject course for the HRMER major. Successful completion of this course serves as contributing evidence of having achieved Programme Learning Goal 7:   Graduates will have sound awareness of ethical, social, cultural and environmental implications of business practice.

How is this course assessed?

Formal Requirements to pass this course

There is no requirement to pass each individual piece of assessment. The only requirement is that your final grade (which is determined by aggregating your performance from all three assessment tasks) must be 50% or more.

NB. Due to the potential of unforeseen developments (for example, recent years have seen pandemic and weather event related disruption), please refer to the course Stream site for updated information regarding lectures, assessments, etc.  This information will be regularly updated if the situation changes.

The assessment at a glance

Assessment

Learning  Outcomes

Percentage Weighting

Due Dates

 

Online test

 

1

 

114343 Healthy Workplaces Semester 2, 2024 20%

16 August

(within a 24-hour

window)

Workbook

1, 2, 3

 

30%

22 September

Report

1, 2, 3

50%

25 October

Oral confirmation checks of assessment submissions may be used within the course, from time to time, for verification purposes. This will involve a conversation between the student and the instructor (in person or online) to discuss the student’s assignment submission. These conversations are usually 5-10 minutes long.

Communicating with each other

The primary means of communication, further to our interaction in the lectures, are the Stream forums. These can be found under the Communication Tools tab on Stream. Please use these forums to communicate with us, rather than contacting usvia email. This is because messages from individual student email accounts are sometimes misidentified by Massey email systems as spam, and filtered out – ensure we hear from you by communicating via the Stream  forums.

There are  several forums, each with a different purpose. They include:

News forum: This is a one-way forum from us to you. Look here for important updates about this course. Note that these announcements will automatically be sent to your registered email address. If you see an email with 114343 in the subject line, PLEASE READ IT - it will be important!

Student Discussion: Use this forum to chat with your classmates. This forum will not be monitored by staff.

Personal Communication : Use this to confidentially communicate with course staff about private  matters, such as ill health, personal issues, etc.

Course Information: (think of this as a “Student to Coordinator Public Questions forum”) .  Use

this to post general course-related questions and receive answers that are visible to all participants.

Other forums may include one or more Assessments Forum for queries about assessments.

If you  "subscribe" to a forum, new messages get emailed to you (this is automatic for the News Forum, but for the others, you need to subscribe yourself).

Communication expectations

We expect everyone in the Massey community to communicate courteously, appropriately, and constructively in all exchanges, as Massey's guidelines stipulate.

In terms of specific messages, it’s important to be clear from the outset both what I expect of you and what you can expect of me. Here is what you can reasonably expect of me:

•    responses to all discussion forum postings within 48 hours during the working week (Monday-Friday);

•    responses to any personal communication within 48 hours during the working week.

Within reason, I also have a couple of expectations of you:

•    use a meaningful subject line in your discussion postings;

•    use the Stream discussion forums appropriately;

•   support your colleagues in the course – that means encourage, help, and respect your fellow students         

基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究”展开,提出了一种结合数据驱动方法与Koopman算子理论的递归神经网络(RNN)模型线性化方法,旨在提升纳米定位系统的预测控制精度与动态响应能力。研究通过构建数据驱动的线性化模型,克服了传统非线性系统建模复杂、计算开销大的问题,并在Matlab平台上实现了完整的算法仿真与验证,展示了该方法在高精度定位控制中的有效性与实用性。; 适合人群:具备一定自动化、控制理论或机器学习背景的科研人员与工程技术人员,尤其是从事精密定位、智能控制、非线性系统建模与预测控制相关领域的研究生与研究人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能预测控制;②为复杂非线性系统的数据驱动建模与线性化提供新思路;③结合深度学习与经典控制理论,推动智能控制算法的实际落地。; 阅读建议:建议读者结合Matlab代码实现部分,深入理解Koopman算子与RNN结合的建模范式,重点关注数据预处理、模型训练与控制系统集成等关键环节,并可通过替换实际系统数据进行迁移验证,以掌握该方法的核心思想与工程应用技巧。
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