BIOMET 7000WT - Research Methodology and Experimentation
Waite Campus - Semester 2 - 2022
General Course Information
Course Code BIOMET 7000WT Course Research Methodology and Experimentation Coordinating Unit School of Agriculture, Food and Wine Term Semester 2 Level Postgraduate Coursework Location/s Waite Campus Units 3 Contact 5 days during the mid-Semester break Available for Study Abroad and Exchange Y Prerequisites Completed degree in Agricultural Science, Viticulture & Oenology or Science Assumed Knowledge Introductory Statistics Course Description Multifactorial and multiple-response experiments are typical in the research and practice in Viticulture & Oenology. This course introduces the principles of factorial, response surface and mixture designs, as well as the principles of multivariate unsupervised (principle component analysis) and supervised (clustering and discrimination) modelling in the context of wine science. The theory component is matched with research publications from relevant areas, case-studies and the Minitab software functionality. Minitab 17 will be used extensively in the course. The course is taught in a small-group setting with the enrolment not exceeding 15 students, which guarantees an individual learning and teaching approach. The course will be presented in intensive workshop mode and supported with online discussions and assessments.
Course Coordinator: Dr Olena Kravchuk
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
On successful completion of this course students should be able to:
Develop and demonstrate a sufficient understanding of the principles of multifactorial experiments in the context of wine science
Develop and demonstrate an understanding of the basic principles of multivariate analysis underlying the principle component analysis and linear discriminant methods in the context of wine science
Develop and demonstrate skills in designing and analysing factorial experiments with Minitab 17 and preparing data analysis reports
Develop and demonstrate skills in conducting multivariate analysis with Minitab 17 and preparing data analysis reports
Develop and demonstrate individual learning strategies for efficiently participating in intensive workshops and self-learning prior and following the workshops
Demonstrate skills in designing and analysing typical factorial experiments in Viticulture and Oenology and understanding experimental designs and analysis in research publications in the field.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Learning & Teaching Activities
Learning & Teaching Modes
Professional development in statistical and quantitative skills is required continuously in the workforce in Viticulture and Oenology as new methods and applications are developed in the industries and research. Graduate students are expected to possess efficient learning skills to be prepared for intensive workshop presentations by subject matter holders following and preceding with online learning and communication.
This course is presented in the mode typical for intensive workshop training in the industry. The intensive 5-day series of 6-hr workshop is preceded by 4 weeks of online preparation (with weekly face-to-face tutorials) and followed by 4 weeks of online discussions and assessment (with weekly face-to-face tutorials). A reflective journal is maintained by students to guide them with efficient time management in this setting.After the workshop series, students are searching for and presenting a relevant case-study they wish to investigate in detail, and this forms the basis of online discussions between a student and the instructor and the class.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.A student enrolled in a 3 unit course, such as this, should expect to spend, on average 12 hours per week on the studies required. This includes both the formal contact time required to the course, as well as non-contact time.
Learning Activities SummaryThecourse consists of 5 days of intensive workshops during the mid-semester break, preceded by 4 weekly meetings and followed by 4 weekly meetings to reinforce individual learning. Computer tutorials and assessment are implemented online.
Specific Course RequirementsThe course uses intensively Minitab 17 software. The software can be leased directly from Minitab on a semester loan to be installed on students’ computers or laptops.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Task Task Type Percentage of total assessment Hurdle Yes/No Learning Outcome Approximate timing of assessment Online pre-workshop tutorials Formative No 1,2 Week 8 Online post-workshop tutorials Formative No 3,4 Week 12 Participation in workshops Formative & Summative 25 Yes 5 Week 8 Online discussions of publications Summative 25 No Reflective journal Formative & Summative 15 No 5,6 Week 12 Case-study discussion and design Formative & Summative 20 Yes 1,2,3,4,6 Week 10-11 Course revision Summative 15 No 1-4 Week 13
Assessment Related Requirements
Assessment Item with hurdle % needed or requirement to meet hurdle Is additional assessment available if student does not meet hurdle requirement? Yes or No Details of additional assessment, if available Participation in the workshop 100%
Individual sessions after the workshops (in exceptional circumstances only) Case-study discussion and design 50% Yes Critical review of published experiments
Online pre- and post-workshop tutorials (0%) – students are going through a set of assigned reading and computer exercise to facilitate and reinforce their interest in the subject
Participation in the workshops (25%) – in a series of 5 weekly workshops, consisting of 3 hrs of problem-solving and 3 hrs of software practice, students demonstrate their understanding in a set of assigned questions; active participation in workshops helps students develop their own strategy for successful learning in intensive teaching
Online discussions of publications (25%) – students are comprehensively reading research studies in their field highlighting the relevance of the course and demonstrating typical experimentation studies; the discussions facilitate students’ skills in critical reading of research literature
Reflective journal (15%) – students are writing 300 words each week after the workshop series to reflect on the material they have learned and their ways of dealing with the learning.
Case-study discussion (10%) – students identify and present a research question suitable for being addressed with the type of experiments and data analysis introduced in the course. In the discussion, students present an argument for the importance and relevance of this study to their understanding of the course material
Case-study design (10%) – students are designing their research proposal in Minitab 17, and either running the analysis, if there are experimental results available, or running a dummy analysis of computer-generated results, similar to what can be experienced in real life. The emphasis is on the understanding of the principles of design and the outcome of statistical models underlying the design and on the preparation of a high-quality report.
Course revision (15%) – students are working on a set of problems to revise the material covered in the course.
If an extension is not applied for, or not granted then a penalty for late submission will apply. A penalty of 10% of the value of the assignment for each calendar day that the assignment is late (i.e. weekends count as 2 days), up to a maximum of 50% of the available marks will be applied. This means that an assignment that is 5 days late or more without an approved extension can only receive a maximum of 50% of the marks available for that assignment.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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