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EDUC 1011 - Reasoning with Numbers: Statistical Literacy

North Terrace Campus - Semester 1 - 2025

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 data for academic purposes. Students will be introduced to some basic statistical concepts such as summarising data, statistical distributions, basic hypothesis testing, and probability. Assessment will consist of an online quiz, a summary of a statistical paper, and a research activity where students analyses data and then present their analysis. This course will be useful for students entering courses where mathematical and statistical skills are necessary, such as Psychology, Health Sciences, or Business and Commerce disciplines. This course is compulsory for University Preparatory Program students wishing to undertaken studies in Nursing or Health Sciences.

  • 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 Available to University Preparatory Program or Wirltu Yarlu UPP students only
    Assessment Quizzes, minor essay, major essay
    Course Staff

    Course Coordinator: Ms Amy Robinson

    Lecturer and Tutor: Dr Fernando Marmolejo-Ramos

    Course Timetable

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

  • 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)

    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.

    1-5

    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.

    2, 3, 4, 5

    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.

    3

    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.

    3

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    5
  • Learning Resources
    Required Resources
    Please see MyUni for details. 
    Recommended Resources
    Moore. D.S, McCabe. G and Craig.B.A 2014 Introduction to the Practice of Statistics, 8th Edition, W.H.Freeman and Company. 

    Online Learning
    See MyUni for details.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course will entail 1 hour of lectures per week and 2 hours of tutorials per week. Students are expected to discuss key concepts presented in the course. Most importantly, students are expected to relate the concepts discussed in the lectures and tutorials to everyday life situations and applications.
    Workload

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

    1 hour lecture per week - 12 hours

    Preparation for lectures - 12 hours

    2 hour tutorial per week - 24 hours

    Preparation for tutorials - 12 hours



    Learning Activities Summary

    Schedule
    Topic
    Week 1 Introduction to the course and statistics 
    Week 2 Data, Variables and Analyses 
    Week 3 Data, Variables and Analyses
    Week 4 Data, Variables and Analyses
    Week 5 Normality
    Week 6 TBC
    Mid-semester break
    Week 7 Hypothesis Testing
    Week 8 Hypothesis Testing
    Week 9 Presentations
    Week 10 Linear Regression
    Week 11 Probability
    Week 12 Probability
    Week 13 This week may be used to catch up on missed content due to public holidays. This will be confirmed by your tutor.
    For clarification on which dates correspond to which weeks, please visit:
    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).

  • 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 Weighting Learning Outcome
    Minor Essay 20% 1,2,4
    In-Class Quiz 35% 1,2,3,4,5
    Critical Analysis Report 45% 1,2,3,4,5
    Participation Activity TBC 1,2,3,4,5


    For clarification on which dates correspond to which weeks, please visit:  

    National Association of Enabling Educators (NAEEA) Common Learning Outcomes (2019)

    Skills: On completion of an Enabling course, a student will demonstrate:

    cognitive skills to understand, analyse, synthesise and critically evaluate information;
    information skills to find, retrieve and analyse information for use in academic
    contexts;
    communication practices to foster the exchange of knowledge and ideas within an
    academic context;
    academic literacy skills fostering the written communication of ideas, theories and
    analysis; and
    independent learning skills

    Application of knowledge and skills: A student completing an Enabling course will
    demonstrate the application of knowledge and skills by: 

    engaging with the university learning and teaching environment in an ethically and
    contextually aware manner;
    applying independent learning techniques to achieve their learning outcomes;
    using their developing critical thinking skills and broadening knowledge in particular
    contexts; and
    adopting student practices that meet their institutions’ academic expectations.

    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: /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

    No information currently available.

    Submission

    No information currently available.

    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 .

  • 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
  • Fraud Awareness

    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鈥檚 disciplinary procedures.

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