BIOMET 7000WT - Research Methodology and Experimentation

Waite Campus - Semester 2 - 2020

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.

  • General Course Information
    Course Details
    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 Biometry or Introductory Statistics
    Assessment Online discussions, written assessment, case-study analysis and design, workshop participation and course reflective journal
    Course Staff

    Course Coordinator: Dr Olena Kravchuk

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes

    On successful completion of this course students should be able to:

    1. Develop and demonstrate a sufficient understanding of the principles of multifactorial experiments in the context of wine science

    2. 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

    3. Develop and demonstrate skills in designing and analysing factorial experiments with Minitab 17 and preparing data analysis reports

    4. Develop and demonstrate skills in conducting multivariate analysis with Minitab 17 and preparing data analysis reports

    5. Develop and demonstrate individual learning strategies for efficiently participating in intensive workshops and self-learning prior and following the workshops

    6. 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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
  • 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 Summary
    Thecourse 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 Requirements
    The 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.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    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
    Assessment Detail

    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.

    Course Grading

    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.

  • Student Feedback

    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 ( 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.

  • Student Support
  • Policies & Guidelines
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