EDUC 1011 - Reasoning with Numbers: Statistical Literacy

North Terrace Campus - Semester 2 - 2022

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 Reasoning with Numbers: Statistical Literacy School of Education Semester 2 Undergraduate North Terrace Campus 3 Up to 3 hours per week N Available to University Preparatory Program or Wirltu Yarlu Preparatory Program students only Mini-quizzes on statistics x 2; Statistics group presentation; Essay on application of statistics to real-life contexts
Course Staff

Course Coordinator: Ms Amy Robinson

Lecturer and Tutor: Sarah James

Office: Level 6, Nexus10
Phone: 08 8313 0168

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.

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

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
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

No information currently available.

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

Assignments - 65 hours

Learning Activities Summary

Week 1: Introduction to the course and statistics

Week 2: Data, variables and analysis

Week 3: Data, variables and analysis

Week 4: Data, variables and analysis

Week 5: Normality

Week 6: TBC

Week 7: Hypothesis Testing

Week 8: Hypothesis Testing

MID SEMESTER BREAK - Two weeks

Week 9: Presentations

Week 10: Linear Regression

Week 11: Probability

Week 12: Probability

Weekly topics subject to change depending on cohort knowledge and skill set.

• 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
 Summary Formative Friday Week 5 25% 1,2,4 Presentation Summative Friday Week 9 25% 1,2,3,4,5 Report Summative Friday Week 13 40% 1,2,3,4,5 Active Participation Formative Ongoing - every two weeks 10% 1,2,4,5

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

Assessment Detail

No information currently available.

Submission

No information currently available.

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
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
• 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 studentâ€™s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

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