EDUC 1011 - Reasoning with Numbers: Statistical Literacy

North Terrace Campus - Semester 1 - 2016

This course covers broad quantitative skills in the context of academic reasoning and argumentation: it aims to make students literate in the use of numbers and the basic analysis of primary data for academic purposes. It will be useful for students entering courses where applied numeracy skills are necessary, such as Psychology, Health Sciences, or Business and Commerce disciplines. Students will be introduced to some basic statistical concepts such as averages (mean, median and mode), variance, distribution, and probability. All learning takes place in a practical context, and all concepts are given a strong grounding in real-life examples and hands-on activities. This course is compulsory for University Preparatory Program students wishing to undertaken studies in Nursing or Health Sciences. This course is offered to all students who wish to gain a basic grasp of statistical skills and will relate these skills to their personal and academic experiences, i.e., students will be able to interpret material presented in publications delivered in several formats (e.g., via TV, Internet, newspapers, academic papers, etc). Assessment will consist of a self-directed research activity where students collect data and undertake some simple analysis of that data, and then present their analysis with some preliminary findings.

  • General Course Information
    Course Details
    Course Code EDUC 1011
    Course Reasoning with Numbers: Statistical Literacy
    Coordinating Unit School of Education
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange N
    Restrictions This class is only open for students in the University Preparatory Program or Wilto Yerlo Preparatory Program.
    Course Description This course covers broad quantitative skills in the context of academic reasoning and argumentation: it aims to make students literate in the use of numbers and the basic analysis of primary data for academic purposes. It will be useful for students entering courses where applied numeracy skills are necessary, such as Psychology, Health Sciences, or Business and Commerce disciplines. Students will be introduced to some basic statistical concepts such as averages (mean, median and mode), variance, distribution, and probability. All learning takes place in a practical context, and all concepts are given a strong grounding in real-life examples and hands-on activities. This course is compulsory for University Preparatory Program students wishing to undertaken studies in Nursing or Health Sciences.

    This course is offered to all students who wish to gain a basic grasp of statistical skills and will relate these skills to their personal and academic experiences, i.e., students will be able to interpret material presented in publications delivered in several formats (e.g., via TV, Internet, newspapers, academic papers, etc). Assessment will consist of a self-directed research activity where students collect data and undertake some simple analysis of that data, and then present their analysis with some preliminary findings.
    Course Staff

    Course Coordinator: Dr Chad Habel

    Lecturer-in-charge/tutor: Lucy Andrew
    Email: lucy.andrew@adelaide.edu.au 
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    Upon the successful completion of this course, students should be able to:

    1. Discuss and apply basic concepts which are essential in statistics, including variance, probability, significance, and others;
    2. Apply statistical knowledge to academic and everyday life;
    3. Work cooperatively with others;
    4. Analyse a specific dataset in response to a question in order to form well-supported conclusions;
    5. Utilise technology to assist in the analysis and application of statistical knowledge.
    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)
    1, 2, 4, 5
    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
    1, 2, 3, 4, 5
    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
    1, 3, 4
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    2, 3, 4, 5
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • Able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
    3, 4
    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
    2, 3
  • Learning Resources
    Required Resources
    Students will need to sign up to Khan Academy to receive homework questions. You can sign up using a Google or
    Facebook account, or by using an email address. Students will be given the opportunity to sign up to Khan Academy during the first
    tutorial https://www.khanacademy.org/signup?continue=
    Recommended Resources
    The lecturer will establish the recommended resources during weeks 1 and 2.


    Online Learning
    MyUni and Khan Academy (see above) will be essential portals for your learning in this course. It is very important that you are familiar with both these environments and use them effectively to support your learning.

    Important information may be emailed to your student email account, so it is essential that you check your student email regularly.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course will entail 1 hour of lectures per week and 2 hours of workshops per week. Students are expected to discuss key issues and concepts presented in the course. Most importantly, students are expected to relate the concepts discussed in the lecture/workshop to everyday life situations and applications. The student will be responsible for the non-contact activities which will include, but are not limited to, reading and studying.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    1 hour lecture per week (x12) 12 hours
    2 hour tutorial per week (x12) 24 hours
    4 hours online research per week (x12) 48 hours
    6 hours reading and independent study per week (x12) 72 hours
    Total 156 hours
    Learning Activities Summary

    Schedule
    Topic Notes
    Week 1 Introduction: Why use statistics? Descriptive vs. Inferential 5-6pm tutorial in Computer Lab
    Week 2 Central tendency, range, frequency, distribution
    Week 3 Variation; box and whisker plots Test: descriptive v inferential stats, central tendency, range, max, min, frequency
    Week 4 Graphical representations of data
    Week 5 Distribution of data Present data to the class. 5 minutes per group.
    Week 6 No lecture, no tutorial Data collection
    Mid-semester break Data collection
    Week 7 Gathering Data I
    Week 8 Gathering Data II Test: variation, interpreting graphs, distribution of data, box and whisker plots
    Week 9 Data and Chance I
    Week 10 Data and Chance II
    Week 11 Exploring Data I Test: Data and Chance (probability)
    Week 12 Exploring Data II
    Week 13 Exploring Data III Assignment questions will be answered in the tutorial
    For clarification on which dates correspond to which weeks, please visit: http://www.adelaide.edu.au/student/dates/
    Specific Course Requirements
    To pass this course, students must attend at least 75% of tutorials; in cases of absence for medical or compassionate reasons, documentation must be provided and students must still attend at least 50% of
    classes. If students fail to attend the minimum required number of tutorials, they will be considered to have not completed an assignment (see below).

    Small Group Discovery Experience
    The University of Adelaide has committed to a pedagogical approach termed the “Small Group Discovery Experience”, indicating that the SHDE will be a core component in a credit-bearing course of every undergraduate program, and that it will be part of every first-year level from 2014. Since the UPP is not an award-based program, it is not strictly required to include an SGDE in the UPP.

    However, since the UPP is designed to prepare students for first-year study, and the SGDE will be a core component of all first-year study, it is important for the UPP to provide some preparation in Small Group Discovery. These should be of a scaffolded, preparatory nature as befits each course within the program, and the philosophy and program objectives of the UPP. The Program has been designed to include  preparation for small group work and research activity in many of its courses.

    More specifically, this course aims to prepare students for their small-group discovery experience by providing subject expertise in statistics and giving students the opportunity to develop an inquiry-based project to gather and analyse some data in order to draw conclusions.

  • 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 Due Weighting Learning Outcome
    Attendance and Participation Formative 

    Ongoing

    5% 1, 2, 3, 5
    In-class mini-quizzes x3  Formative In class (tutorial), Week 3, Week 8, Week 11 30% (3 x 10% each) 1, 4
    Group presentations Formative In class (tutorial), Week 5 5% 1, 2, 3
    Graphing Assignment Summative Friday, first week of mid-semester break 25% 2, 4, 5
    Data Assignment Summative 5pm Friday, Week 13 35% 1, 2, 4, 5

    For clarification on which dates correspond to which weeks, please visit: http://www.adelaide.edu.au/student/dates/  

    Assessment Related Requirements
    Students must attempt all assessment tasks to pass this course. If students miss a mini-quiz they will receive a mark of zero, unless they receive an extension on medical or compassionate grounds as per the University's Modified Arrangment of Coursework Assessment (MACA) policy.

    Since the University Preparatory Program is designed to prepare students for success at University, completing and submitting all assignments is central to the intended learning outcomes of the program and each course within it. Often, at least attempting and submitting assignments in the face of difficulty or adversity is enough for success at University and the UPP encourages this resilience by employing this policy in select courses. Please note that the absolute last date for the submission of assignments in Semester 1 is the end of Swot Vac week, which is one week after the final assignment is due.

    If a student fails to submit all assessment tasks and would otherwise have received a grade greater than 45, they will be given a nominal grade of 45 (Fail) for that course in that semester. This will permit them to undertake additional assessment (formerly called academic supplementary assessment) at the Course Coordinator’s discretion, as per policy at http://www.adelaide.edu.au/student/exams/supps.html

    It is not necessary to apply for additional assessment; this assessment will usually consist of the missed pieces of assessment, but the course coordinator may require more. As per policy 9.1.3.3, if the student
    passes the additional assessment to the Course Coordinator’s satisfaction, the maximum grade they can get for the course is 50 (Pass). If a student’s raw grade is below 45, regardless of whether all tasks have been attempted, this score will stand unless exceptional, documented circumstances apply as per the University’s Modified Arrangements for Coursework Assessment: https://www.adelaide.edu.au/student/exams/mod_arrange.html 

    Substantial non-engagement in this course (evidenced by repeated non-attendance at tutorials and failure to submit assessments) may result in students being withdrawn from the University Preparatory Program and being required to apply for reinstatement if they wish to continue.

    Assessment Detail
    Attendance and Participation

    Your lecturer/tutor will mark you on your participation in group and class discussions during tutorials. This mark is not based on 'correct' answers but a willingness to participate. Remember that being vocal in class is not the only way to demonstrate participation: asking questions after class or via email/MyUni, doing pre-readings, active listening and note-taking, and actively discussing with other students are all ways of showing that you are participating in the class.

    Mini Quizzes

    Three Mini Quizzes (MQs) will be conducted at the beginning of the tutorial in weeks 3, 8 and 11.

    The MQ will consist of 3-5 short questions; either a specific piece of information (SPI question type), a selection from multiple choices (MC question type), an extended piece of information, or open-ended question (OE question type). MQ questions will be similar to those questions/exercises worked out during the lecture/workshop sessions and will be based on topics discussed in previous lectures/workshops.

    SPI example: Find the mean of the following values: 5, 0, 7, 3, 6, and 3

    MC example: Which of the following answers is the mean of the following values: 5, 0, 7, 3, 6, and 3?
    A) 4
    B) 7
    C) 2.52
    D) 0

    OE example: What does the mean of a data set tell us about the data?

    Group Presentation

    Choose a data set (or your tutor will give you a data set). Use statistics to describe your data with specific reference ot the concepts and terms introduced in class. Graph your data (you may need to create more than one graph) and explain the meaning of the data to the class. What kinds of interpretations might your draw from the data? How might the data be used as evidence to support some kind of academic argument?

    Graphing Assignment (500 words)

    Compare and contrast 3 different graphical techniques for representing data. Use one data set and graph the data 3 different ways. What are the advantages/disadvantages of each type of graph? Which is the best graph to represent this data? Why?

    The assignment must contain the following sections;

    Introduction: Describe the data in terms of central tendency and variation. What is the population or sample? How was the data collected?
    Body: Produce 3 types of graphs and discuss the advantages and disadvantages of each graph.
    Conclusion: What can you conclude from the data? Which graph represents the data in the best way?

    Data Assignment (700 words)

    Choose a data set (or your tutor will give you a data set). What is the question you are trying to answer with your data? i.e. Are house prices in SA increasing at the same rate as house prices in NSW and Tasmania? How have Australians’ attitudes to gay marriage changed over the past 20 years? How does smoking effect the risk of heart attack in people over 50? Try to present your data in a way that support a sepcific conclusion/answer to your question. In this assignment, you should:

    • Briefly describe how the data was collected
    • Use statistics to describe your data (central tendency and variation)
    • Graph the data in a way that best represents the data
    • Interpret the data – what it the data telling you? You may want to calculate percentages, probability, frequency etc. You may need to include extra data to help interpret your original data set.
    • Make conclusions – What can you conclude from your data? What can you NOT conclude i.e. identify a possible misinterpretation of the data by someone who doesn’t know as much as you about statistics. Clearly state what extra data you need to make relevant conclusions.
    Submission
    All assignments will be electronically submitted via MyUni, except for tests and group presentations.

    Students may be granted extensions to assignments on medical or compassionate grounds; documentation to support these ground will be required. Requests for extension must be made before the due date; requests for extension submitted after the due date will not be considered. All extension requests must be submitted to the Course Coordinator (Chad Habel: chad.habel@adelaide.edu.au);
    any extensions granted by the lecturer or tutor will not be considered valid.

    All extension requests will be administered according to the Modified Arrangements for Coursework Assessment Policy: http://www.adelaide.edu.au/policies/3303/

    For a concise information sheet on this policy, please visit http://www.adelaide.edu.au/student/exams/pdfs/maca_medical_compassionate_info.pdf
     
    Penalties for Late Submission

    Unless the Course Profile states otherwise when an assessment is submitted after the due date, and without an extension, 5% of the total mark possible will be deducted for every 24 hours or part thereof that it is late, including each day on a weekend. For example, an essay that is submitted after the due date and time but within the first 24 hour period, and that has been graded at 63%, will have 5% deducted, for a final grade of 58%. An essay that is more than 24 hours late will lose 10%, etc. 

    This course aims to return assessed work within 2 weeks of its submission, although this cannot be guaranteed. The resubmission of assignments is not possible for this course, except in exceptional circumstances as approved by the Course Coordinator.

    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 (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.

  • Student Support
  • Policies & Guidelines
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    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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