EDUC 1011 - Reasoning with Numbers: Statistical Literacy
North Terrace Campus - Semester 1 - 2014
General Course Information
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 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 Coordinator: Dr Chad Habel
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesUpon the successful completion of this course, students should be able to:
- Discuss and apply basic concepts which are essential in statistics, including variance, probability, significance, and others;
- Apply statistical knowledge to academic and everyday life;
- Work cooperatively with others;
- Analyse a specific dataset in response to a question in order to form well-supported conclusions;
- 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) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1, 2, 4 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1, 2, 4 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2, 4 Skills of a high order in interpersonal understanding, teamwork and communication. 3 A proficiency in the appropriate use of contemporary technologies. 5 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 2, 3 A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 4 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 1, 2, 4
- Selected readings as included in Course Guide and distributed in class
- Students are welcomed to bring their own laptops to class
Recommended ResourcesThe lecturer will establish the recommended resources during weeks 1 and 2.
The website “causeweb”, among others, is a recommended resource to visualise statistical concepts: https://www.causeweb.org/resources/
Examples and readings will be extracted from:
Ott, R. L., & Longnecker, M. (2010). An introduction to statistical methods and data analysis. Brooks/Cole, Cengage Learning.
Minium, E. W., King. B. M., & Bear, G. (1993). Statistical reasoning in Psychology and Education (3rd Ed.). Toronto: John Wiley & sons, Inc.
Upton, G., & Cook, I. (2006). Oxford dictionary of statistics. Oxford: Oxford University Press.
Aron, A., & Aron, A. E. (2003). Statistics for Psychology (3rd Ed.). N.J.: Prentice Hall.
Please note that “turnitin” will be used for students to learn more effectively about referencing and citation conventions at university; it is recommended that students familiarise themselves with this. Please visit: http://www.adelaide.edu.au/clpd/plagiarism/students/turnitin/
It is also strongly recommend that students view the following video: http://www.adelaide.edu.au/myuni/onlinelearning/learningmodules/avoidingPlagiarism/player.html
Learning & Teaching Activities
Learning & Teaching ModesThis 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 2 hours consultation per week (x12) 24 hours 3 hours online research per week (x12) 36 hours 5 hours reading and independent study per week (x12) 60 hours Total 156 hours
Learning Activities Summary
Week Lecture topic Activities/workshop Resources 1 Presentation of the course The “course guidelines” will be discussed to make sure all is clear. Also a conversation in relation to students’ expectations will be generated. No exercises for this session. Just questions about the course 2 Introduction to the study of statistics Pt. I [Why study statistics? (opening reflection)] Discussion of a fresh and new approach to the relevance of statistics in academic and everyday life. Exercises from O&L will be worked out Ott & Longnecker (2010), Chapter 1 3 Introduction to the study of statistics Pt. II [some controversial issues in statistics!] A short passage titled “are statistics necessary?” will be read and discussed in class. Sub-groups will defend their arguments. Exercises from M,K,&B will be worked out Minium, King & Bear (1993), Ch 1 (focus page 9) 4 Preliminary concepts [checking the nuts and bolts of statistics] Exercises from M,K,&B will be worked out in class Minium, King & Bear (1993), Ch 2 5 Gathering data Pt. I Some data will start to be collected by the students (e.g., cups of coffee drunk during the semester break). Exercises from O&L will be worked out in class Ott & Longnecker (2010), Ch 2 (pp. 16-35) 6 Gathering data Pt. II A second data collection will be done (e.g., cups of water drunk in week 7) Exercises from O&L will be worked out in class Ott & Longnecker (2010), Ch 2 (pp. 35-48) 7 Exploring data Pt. I [Interpreting graphs and charts] Students will start using different graphical methods (e.g., histograms, boxplots, etc.) to represent the data collected in previous weeks.Exercises from O&L will be worked out in class Ott & Longnecker (2010), Ch 3 (pp. 56-85) 8 Exploring data Pt. II [How can I check it whether my data is ok? About something called EDA] Students will have a closer look at their data sets in order to spot skewness and potential maverick observations.A short passage titled “Misleading graphs” will be read and discussed in class. Sub-groups will defend their argumentsExercises from O&L will be worked out in class Ott & Longnecker (2010), Ch3 (pp. 85-116)
Reading = p.22-23 in Aron & Aron (2003)
9 Data and chance Pt. I [on the relationship between statistics and probability] Exercises from O&L will be worked out in class Ott & Longnecker (2010), Ch 4 (pp. 141-157) 10 Data and chance Pt. II [sample space and other matters] Student presentations 11 Data and chance Pt. III [some further things about probability worth knowing] Exercises from O&L will be worked out in class Ott & Longnecker (2010), Ch 4 (pp. 157-203) 12 Why study statistics? (closing reflection) [After all of what we’ve seen… what did we learn about stats?] Concluding remarks on the relevance of statistics in academic and everyday life No exercises for this session. Just questions about the course and final thoughts/comments
For clarification on which dates correspond to which weeks, please visit: http://www.adelaide.edu.au/student/dates/
Note: selected exercises on the topic being presented will be worked out in class as part of the workshop. Additionally, selected short articles on the topic being presented will be read and discussed in class as part of the workshop and lecture. Students will be encouraged to generate examples and exercises based on the examples and the readings provided.The readings for week 2 onwards will be uploaded in MyUni, at the latest, 3 days before the lecture.
Specific Course RequirementsTo 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 ExperienceThe 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.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Week Type Assessment Weighting Due 3 Formative Mini-quiz 10% 5pm Friday of week 3 7 Formative Mini-quiz 10% 5pm Friday of week 7 10 Summative Oral presentation 30% During week 10 tutorial Swot
Summative Essay 50% 5pm Friday of Swot vac week
For clarification on which dates correspond to which weeks, please visit: http://www.adelaide.edu.au/student/dates/
Assessment Related RequirementsStudents must attempt all assessment tasks to pass this course. 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 188.8.131.52, 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 DetailStatistics Mini-Quizzes
Questions will be sampled from published resources [see 'Recommended resources']. Mini-Quizzes will occur in weeks 3 and 7 and answers should be submitted via MyUni by 5pm on Friday in those weeks.
Each Mini-Quiz will consist of 3 short questions sampled from selected resources (see below) and will be answered using a specific piece of information (SPI question type), a selection from multiple choices , an extended piece of information, or open-ended question. Multiple Choice Questions will be similar to those questions/exercises worked out during the lecture/workshop sessions and will be extracted from topics discussed in previous lectures/workshops.
For example: “Find the mean of the following values: 5, 0, 7, 3, 6, and 3” is a Specific Piece of Information question type. “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 and D) 0” is a Multiple Choice question type. “What does the mean of a data set tell us about the data?” is an Open-Ended question type.
Statistics group oral presentation
15% of the mark goes towards its powerpoint/oral presentation and 15% for a 200 word summary of it and that should be included in the slides. This activity is worth a total of 30%.
This presentation will be given in class and the powerpoint file should be submitted the day before the presentation via MyUni. All the members in the group will receive the same mark for the content of the slides and the 200-word summary, but will be marked differently based on their individual oral presentation.
The slides should have the following parts (in this order):
1. Slide 1 = title of the presentation and the name of the members of the group
2. Slide 2 = 200-word summary of the presentation
3. Slide 3 = outline of the presentation
4. Slides 4 – onwards = necessary number slides to present the topic of the presentation
5. Last slide = references used
Time for each presentation = approx. 10 minutes + approx. 5 mins for questions
The presentation will revolve around a statistical concept selected by the members of each group but groups should have different topics to present (i.e., the same topic cannot be addressed by two or more groups). Although the statistical concepts selected by the students are those discussed during the lectures/workshops, it is the students’ task to find further information about the concepts (i.e., the readings given for the lectures and the PowerPoint slides should be seen as platforms for further research).
Some essential aspects to bear in mind for the presentations are: i) to provide definitions of the concept being presented, ii) to provide examples in relation to the concept, and iii) to provide a clear link between the concept addressed and everyday life situations. For instance, let’s suppose the topic of my presentation is “on the use graphical techniques to represent data” (this would be the title of my talk and therefore will appear in slide 1). An approx. 200-word summary of my talk would appear in slide 2. Let’s further assume that my session will cover three parts: i) a definition of graphical techniques in statistics and a brief description of some the techniques available out there, ii) examples of how the techniques display data, and iii) why these techniques are important to understand information given in newspapers and media (this would be the outline of my session and it should appear in slide 3). The subsequent slides will be devoted to each of the parts of my talk and the last slide would show the references I consulted.
Aside from 'graphical representations', some possible topics for presentations could include:
* Variance, dispersion, and standard deviation
* Statistical significance
* Different types of averagea (mean, median and mode)
Final individual essay
In this essay, a selected statistical concept is discussed in relation to its applicability to real life scenarios. Data collected in previous sessions can be used for this essay. This will be a 750-word essay that should include Figures and/or Tables to explain concepts. Word count excludes Figures and Tables. The essay should be double spaced using Arial 12 font.
The essay should have the following parts and that should be labelled as such (except the Introduction):
1. Intro [I] (in this section a general background of the topic is given along with the outline of the essay. Give a title to the essay and add the student name and ID as a footnote by the title. Include the word count in the first page at the end of the footnote);
2. Body [B] (the definition and theoretical implications of the concept are presented);
3. Discussion [D] (the applicability of the statistical concept is presented using real life examples. Figures and/or Tables must be used);
4. Conclusion [C] (the core conclusion of the essay is presented in one paragraph);
5. References [R] (the references consulted to write up the essay, using Harvard format)
Recommendation = assuming the essay in its totality is 100% in amount of words, allocate 10% of the word count for [I], 25% of the word count for [B], 60% of the word count for [D], and 5% of the word count for [C].
The essay will revolve around a statistical concept selected by the student and the data collected in previous sessions can be used to exemplify the topic of the essay. For this task, however, it is not compulsory that students have different topics for their essays (i.e., the same topic can be addressed by two or more individuals). Although it might be the case that two or more students work on the same topic, their essays have to be different in relation to the wording used, the ideas presented, and the examples displayed, i.e., students should be quite mindful of potential collusion. Your essay must be completely your own work.
Although the statistical concept selected by the student is one of those discussed during the lectures/workshops, it is the student’s task to find further information about the concept (i.e., the readings given for the lectures and the PowerPoint slides should be seen as platforms for further research). For the citation of references, use the Harvard referencing system since it is the most common at the University of Adelaide.
For instance, let’s suppose the topic of my essay is “on the use graphical techniques to represent data” (this would be the title of my essay and therefore will appear on page 1). I’ll put a footnote by the title of my essay and my name, my student ID, and the word count of my essay will appear in that footnote. In the Introduction I’ll provide a gentle and general background of my topic and wrap it up with an outline of my essay (e.g., “The topic of this essay will be addressed in three core parts: i) a definition of graphical techniques in statistics and a brief description of some the techniques available out there, ii) examples of how the techniques display data, and iii) why these techniques are important to understand information given in newspapers and media. The last section will provide some final conclusions”). The subsequent pages will be devoted to cover each of the parts mentioned in the outline and each part will have a heading (e.g., I’d title the first part of my essay something like “Classic and current graphical techniques”). In the discussion section (which I’d label “Discussion”), I’ll elaborate on why graphical techniques are important to understand information given in media (which is the part iii) mentioned in my outline) and I’d make use of Tables and/or Figures to make my point clearer. Then, in a section I’d label “conclusions” I’d type a final paragraph presenting some final thoughts on the topic. The last page of my essay will be labelled “References” and I’d put in it all the articles, book chapters, and other sources I read to write up my essay.
Students are strongly encouraged to use the university library in order to find references that will be used to flesh out the understanding of the topics presented in the lectures/workshops. In particular, using the library would be extremely helpful for the group presentation and the final essay assignments.
SubmissionAll assignments will be electronically submitted via MyUni, except for tests and in-class assessments.
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: email@example.com);
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. Hard copy submissions made after 5.00pm on a Friday will be assumed to have been submitted on the next business day and will be penalised 5% per day for every day including weekend days and public holidays. This penalty may be increased where the assignment is to be completed in a period of less than a week.
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.
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.
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