EDUC 1011 - Reasoning with Numbers: Statistical Literacy

North Terrace Campus - Semester 1 - 2017

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.
    Assessment Mini-quizzes on statistics x 2; Statistics group presentation; Essay on application of statistics to real-life contexts
    Course Staff

    Course Coordinator: Dr Chad Habel

    Lecturer-in-charge/tutor: Fernando Marmolejo-Ramos
    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
    Please see MyUni for details. 
    Recommended Resources
    The lecturer will establish the recommended resources during weeks 1 and 2.

    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 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.

    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

    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:
    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
    See MyUni for details.
  • 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 


    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:  

    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

    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, 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: 

    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
    Please see MyUni for details.
    Please see MyUni for details.
    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
  • 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’s disciplinary procedures.

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