Course Profile 1002MSC PDF

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Introduction to Biomedical Data Analysis 1002MSC - Tri 1 2021

Griffith University

TABLE OF CONTENTS 1. GENERAL COURSE INFORMATION .................................................................................................................................................. 2 1.1 COURSE DETAILS ...................................................................................................................................................................................................... 2 1.2 COURSE INTRODUCTION ........................................................................................................................................................................................... 2 1.3 COURSE STAFF .......................................................................................................................................................................................................... 3 1.4 TIMETABLE ................................................................................................................................................................................................................ 3 1.5 LECTURE CAPTURE.................................................................................................................................................................................................... 4 1.6 TECHNICAL SPECIFICATIONS ..................................................................................................................................................................................... 4 2. AIMS, OUTCOMES & GRADUATE ATTRIBUTES

2.1 COURSE AIMS ....................................................................................................................................................................................................... 4 2.2 LEARNING OUTCOMES .............................................................................................................................................................................................. 4 2.3. GRADUATE ATTRIBUTES ........................................................................................................................................................................................... 4 3. LEARNING RESOURCES

3.1 REQUIRED RESOURCES ....................................................................................................................................................................................................... 5 3.2 RECOMMENDED RESOURCES .................................................................................................................................................................................... 1 3.3 UNIVERSITY LEARNING RESOURCES.......................................................................................................................................................................... 1 3.4 LEARNING RESOURCES ............................................................................................................................................................................................. 5 3.5 OTHER LEARNING RESOURCES & INFORMATION ....................................................................................................................................................... 5 4. TEACHING & LEARNING ACTIVITIES

4.1 LEARNING ACTIVITIES ....................................................................................................................................................................................................... 6 4.2 OTHER TEACHING AND LEARNING ACTIVITIES INFORMATION ................................................................................................................................... 6 5. ASSESSMENT PLAN

5.1 ASSESSMENT SUMMARY ....................................................................................................................................................................................................... 6 5.2 ASSESSMENT DETAIL................................................................................................................................................................................................. 7 6. POLICIES & GUIDELINES .............................................................................................................................................................. 10 6.1 ASSESSMENT RELATED POLICIES AND GUIDELINES

UNIVERSITY POLICIES & GUIDELINES ...................................................................................................................................................................................................................................... 10 LEARNING SUMMARY ...................................................................................................................................................................... 11 LEARNING OUTCOMES .................................................................................................................................................................................................... 4 GRADUATE ATTRIBUTES .................................................................................................................................................................................................. 4

Printed: 21 April 2021, 12:04PM The published on-line version of the Course Profile is the authoritative version and by the publication of the Course Profile on-line the University deems the student has been notified of and read the course requirements.

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Introduction to Biomedical Data Analysis 1002MSC - Tri 1 2021

Griffith University

1. General Course Information 1.1 Course Details COURSE CODE

1002MSC

COURSE TITLE

Introduction to Biomedical Data Analysis

ACADEMIC ORGANISATION

MSC School of Medical Science

TRIMESTER

Trimester 1 2021

MODE

Mixed Mode

LEVEL

Undergraduate

LOCATION

Gold Coast, On Campus

CREDIT POINT VALUE

10

Course Description: This course provides students with fundamental statistical skills required in many health study areas and in careers as health professionals. Students will explore foundational statistical principles and practice the art of statistical data analysis and reporting of results. Assumed Knowledge: Maths B (or equivalent). Incompatible with 1014SCG.

Assumed Background: It is preferential for students to have a sound mathematical background. While Mathematics B (or equivalent) is not a pre-requisite, students that have not done Maths B (or any level of high school maths) in recent years, or those that are not confident in their maths skills, may need to pursue extra resources to assist with their study. Module 1 in the course is designed to cover key concepts necessary for students to know in this respect. Please contact the course convenor (details below) if any further support is needed in this respect. Some computer literacy is assumed, including the use of Windows and Microsoft Word. The library provides workshops to assist those students that need further support in this area - click here (look for the section about free online training) for more information.

1.2 Course Introduction In this course, the primary focus of the course will be to teach you the art of quantitative data analysis using statistical and computational methods - an important skill for any health professional to have. Statistical data analysis, and in particular that of Biostatistics, involves the planning of studies and collection of data, which can then be analysed to gain an understanding of health-related phenomena which you will work on or study. 1002MSC Introduction to Biomedical Data Analysis (IBDA) will involve five main components of learning: 1. Mini-Lectures Within each module, there will be a series of videos for you to watch online, through Learning@Griffith. These align with, and often further elaborate on the course lecture notes/slides. These lecture slides are provided online only. Students will be expected to review the online material prior to participating in tutorials. 2. Tutorials Attendance at and participation in tutorials is expected. Tutorials are held weekly from week 2-11, and usually cover material from the preceding week's designated mini lectures (see section 4.1 and 4.2 of this course profile). In tutorials, students will be able to engage with a tutor to ask questions related to a particular topic for that week or be provided with guidance/assistance to complete the tutorial tasks for that module. Tutorials are held online or in-person. Instructions for connecting to online tutorials will be provided through Learning@Griffith. 3. Statistical Analysis Software (SPSS) SPSS is one of several statistical software packages that are frequently used by researchers and statisticians. This course aims to provide students with an introduction to the use of a statistical software package, thereby providing a means for the practical application of theoretical principles presented through the online mini-lectures and tutorials. The skills learned in the SPSS Modules may prove to be essential in your careers as health professionals (that is, it is the "real-life" skill that is often most valuable in your future careers as health professionals engaging with statistics). 4. Teacher-Assisted Peer Support and Engagement This course is offered mixed-mode, so more learning and teaching will be conducted online. Unless you are enrolled into an oncampus tutorial (spaces limited due to COVID-19 restrictions), there may be minimal face-to-face contact between students and the teaching team. As such, it is very important to develop and participate in the online learning community provided and to

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Introduction to Biomedical Data Analysis 1002MSC - Tri 1 2021

Griffith University

encourage/support the learning of your peers at the same time. There will be opportunities given to students to participate or engage in community learning, such as through the course Facebook group and the course Microsoft Teams (MS Teams) site (you will automatically be added to this Teams site when you enrol). We know from past experiences, students greatly benefit from being engaged and an active participant in online learning community activities. 5. PASS Peer-assisted study sessions (PASS) are also available online. Information on what this program involves will be provided on Learning@Griffith, and the PASS leaders are also contactable via the course Facebook group and MS Teams.

TEACHER-STUDENT COMMUNICATION The primary means of communication between the teaching team and students will be via email, the course or Learning@Griffith Announcements. Consultations with the convenor are also available (online and in-person). Students are expected to check their student email and Learning@Griffith regularly (at least every 1-2 days) to ensure that they receive any important information.

Previous Student Feedback Summary of what students said about the course. •

• • • • • • •

1002MSC moved to a mixed-mode format in Semester 1 2016, with all learning & teaching done online, and only exams undertaken in-person. Since then, as a result of student feedback, in-person tutorials have been re-introduced and students really like having this regular in-person contact with the teaching team. The mini lectures were very useful to students, and they found them to be very clear, and efficient as the primary learning resource for the course. Students also really value the members of the teaching team, particularly their availability and approachability. The assessment was perceived to be fair and clear. Students suggested more feedback is required, so we now provide oneon-one feedback opportunities for every assessment item. Students that attended the online tutorials commented that they were really useful and “better than expected”, and liked the flexibility that came with attending online and then having the recording to review. In Trimester 3 2018, as a result of extensive consultation with students, the weighting of the assessment items were changed to better reflect students' perceived appropriate weighting across all assessment. From Trimester 3 2019, Module 1 (Foundational Maths) was offered as an optional module after student feedback about their experiences with this module. Finally, a key factor that students identified they struggled with, was personal time management and ensuring that they prioritised the online learning, amidst the busyness of the other courses they were undertaking. We try to work alongside students to encourage them to stay on track, and stay engaged, but it is also important for students to take ownership of their own time management, to ensure course activities, such as assessment or personal study, is done regularly and on time. Overall, the feedback was positive but indicated that there were some areas in need of improvement that should be addressed. This is continually done throughout the trimester and at the conclusion of/preparation for the trimester.

1.3 Course Staff Primary Convenor Mrs Nikki Fozzard

PHONE

5552 9270 (x29270)

EMAIL

[email protected]

CAMPUS

Gold Coast Campus

BUILDING

Health Science (G05)

ROOM

2.22

CONSULTATION

Consultation with Nikki is by appointment only. The best way to book such an appointment is by using the online appointment booking system (please see the Book a consultation section of Learning@Griffith for more information)

1.4 Timetable Timetables are available on the Programs and Courses website. NB: Details contained in this section of the course profile and section 4.1 Learning Activities are to be read in conjunction with the official class timetable. The published class timetable which is the authoritative source for timetabling information for all campuses can be located by clicking on the link above.

Additional Additional Timetable Timetable Information Information The lecture in week 1 is an orientation lecture that is considered compulsory. Information about how to attend this online session will be posted on Learning@Griffith and emailed to your student email account.

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Introduction to Biomedical Data Analysis 1002MSC - Tri 1 2021

Griffith University

Attendance at tutorials is expected. You should attend at least one tutorial online each week (from weeks 2-11). While there are no marks assigned to attendance and completion of tutorials, attendance will be recorded and this attendance and engagement information may be taken into consideration when assigning a final grade for the course.

Students should also take note that some exams may fall on a Saturday. Attendance, albeit online, at all exams is compulsory, so please ensure that you plan accordingly (e.g. take time off work if necessary, apply for deferred exam for religious reasons). Refer to Section 5 of this Course Profile for assessment dates.

1.5 Lecture Capture It is standard practice at Griffith University that lectures timetabled in lecture capture-enabled venues are recorded and made available to students on the relevant course site, in accordance with the University's Lecture Capture Policy. However there are no lectures offered with this course.

1.6 Technical Specifications Most learning resources are supplied online, through Learning@Griffith. In particular, the mini lectures are hosted on YouTube and are only streamable (not downloadable) from this site. Note that YouTube videos do not count against available student quota (but off-campus use will incur data usage; see your internet provider for more information). You will be unable to undertake this course without reliable and frequent internet access, capable of streaming/accessing the required learning materials. If you would like to, you can complete the Are you ready to study online? self-assessment tool.

2. Aims, Outcomes & Graduate Attributes 2.1 Course Aims Biostatistics forms a substantial and important part of scientific research, particularly as it relates to health. The purpose of this research is to collect and analyse data that relates to the phenomenon being studied; the purpose of statistics is to gain an understanding of what this data means. This understanding is the result of the correct application of statistical techniques, including that involved in the data collection, analysis, and application to the broader scientific context. 1002MSC will introduce students to the concepts and practices needed to achieve this level of analytical understanding and to be able to articulate that to their peers, scholars in their field as well as the wider community. The objectives of this course are to teach core skills in research methods and statistics, with a particular focus on their application to the health sciences. This is achieved through a logical progression from the fundamental mathematics you encounter in statistics and other disciplines in health sciences, up to more advanced statistical techniques. Students will thereafter be able to take a research scenario they wish to investigate, and with the skills they learned in this course, should be able to choose the most appropriate means of analysis and representation of their findings or investigate other means of analysis. The content, teaching methods and assessment of this course are specifically designed to ensure students achieve these aims.

2.2 Learning Outcomes After successfully completing this course you should be able to: 1.GENERAL STATISTICAL ANALYSIS SKILLS 1.1 Recognise different types of data and the appropriate methods to analyse them 1.2 Compute, by hand, selected calculations involved in a statistical analysis. 1.3 Interpret the output of statistical tests and translate these into comprehensible and succinct statements concerning relationships between or among data sets, while relating this back to the research question. 1.4 Articulate to others (including your colleagues, peers and supervisors) orally and in written form your understanding of concepts and solution processes. 2.SPSS/COMPUTER DATA ANALYSIS SKILLS 2.1 Confidently use statistics modelling software, SPSS, and develop the capacity to interpret the output. 2.2 Manage, summarise and present data (that is, create appropriate tables and figures)

2.3. Graduate Attributes For further details on the Griffith Graduate please click here Griffith University prepares influential graduates to be: • • • • • •

Knowledgeable and skilled, with critical judgement Effective communicators and collaborators Innovative, creative and entrepreneurial Socially responsible and engaged in their communities Culturally capable when working with First Australians Effective in culturally diverse and international environments This table demonstrates where each of the Griffith Graduate Attributes is taught, practised and assessed in this course. For further details on the Griffith Graduate Attributes please refer to The Griffith Graduate policy.

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Introduction to Biomedical Data Analysis 1002MSC - Tri 1 2021

Griffith University

University wide attributes GRADUATE ATTRIBUTE Knowledgeable and skilled, with critical judgement Effective communicators and collaborators Innovative, creative and entrepreneurial Socially responsible and engaged in their communities Effective in culturally diverse and international environments

TAUGHT •

PRACTISED • • • • •
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