Poly Sci October14 2008

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    Poly Sci October14 2008 - Presentation Transcript

    1. An Online Course Design Introduction to Economic Analysis Steven C. Myers [email_address] Department of Economics - The University of Akron Presented to the Department of Political Science October 14, 2008 Online at http://gozips.uakron.edu/~myers/online And http://learnecon.blogspot.com
    2. Today
      • Origin
      • Online Design
      • Research – Online vs. Face-to-face
      • Tour around Springboard
      • Other software
    3. Origin
      • College of Business, MBA online
      • Faculty
      • General Studies Advisory Committee
      • E244 – Intorduction to Economic Analysis
    4. Its not the same
      • Face-to-face
      • Lecture
      • Ask questions / lead discussion
      • Draw graphs and equations
      • Show slides
      • 3 big tests
      • Internet / online
      • Can’t lecture
      • Can’t talk to them or discuss
      • Can’t draw
      • Can show slides if prepared
      • 3 big tests invites academic dishonesty and a lack of control.
    5. Design of the online course
      • Built in modules
      • Modules completed in order
      • Competency based testing
      • Use student feedback
        • For student learning enhancement
        • For modifying & improving the course
    6. Course Design
      • DL requires planning for contingencies
      • 14 Content Modules
      • Active Learning vs. Passive Learning
    7. Pre-class and Module 1: Preparing Students to Learn
      • Email them
      • http://gozips.uakron.edu/~myers/online/
        • Is Distance Learning for Me?
        • VARK – testing learning styles
        • Syllabus
    8. Module 1: Course Introduction
    9. Content Modules 2-15
      • Module Introduction & Objectives
      • Chapter Introductions
      • Content
      • Supportive Materials
      • Assessment of Learning - Quizzes on objectives with multiple trials
      • Evaluation
    10.  
    11. 2 Research & Writing Assignments
      • Practice and experience in reflecting on a topic in the current economy.
      • Practice in analysis of economic trends.
      • Gaining of confidence about talking about the economy.
      • Ability to know and use the resources of economic commentary, prior analysis and data.
    12. Introductions & Content
      • Mostly Passive
      • Learner Centered
      • Students progress without intervention
      • Micro – Modules 2-8
      • Macro – Modules 9-15
    13. Graded Assessment
      • Competency based
      • Everyone strives to get a perfect 10 (Mastery)
      • Three attempts, 15 min. time limit
      • Questions a mix of (1) Recognition, (2) Conceptual, & (3) Analytic ~70% C&A
      • Random intervention by Professor
    14. Role of Module Evaluation
      • Planned intervention
      • Forced contact
      • Focus on the learning
      • Professorial encouragement
      • Decreases dropout rates
      • Process repeats
    15. Module Evaluation A Classroom Assessment Technique from Angelo and Cross (1993); tested by Chizmar and Ostrosky (1998)
      • What comments do you have on this module and your experience in completing it?
      • What main point have you learned that you did not fully understand before?
      • What questions … Include any points that still remain muddy or unclear. Do consider posing the muddy points to your fellow students in the discussions.
      • What recommendations do you have for us as we continue to change and enhance the course?
    16. Student Comments / online
      • “ The fact that this course was completely internet based, had no bearing on the level of knowledge I gained from the course. I think I learned as much, or even more, than I would have in a traditional classroom. I contribute this most to the way the instructor organized the course.”
    17. Do On-Line Students in a Mastery Based Principles Course Analyze, Synthesize And Evaluate Better Than Face-To-Face Students? (Myers and Nelson)
      • (1) Does the mode of delivery (face-to-face or web-based) have an influence on learning outcomes? 
      • (2) Are students in an online environment as likely to do as well as in face-to-face classes? Will they be able to equal the complex problem solving of the face-to-face students?
      • (3) Will web-based students develop more favorable attitudes towards economics than the attitudes developed by students in the face-to-face class?
      • (4) Do student myths about economics affect their learning outcomes and attitudes towards economics?
      • http://gozips.uakron.edu/%7Emyers/online/ASSA2004/ASSA_2004_Internet_Paper_myers_nelson.pdf
    18. Comparison
      • Face-to-face
        • Offered by Michael Nelson
        • Regular class times
        • Lecture and active learning collaborative techniques
        • Better than the norm of 83% “chalk and talk” (Becker and Watts)
        • Some minor web-enhancements such as online gradebook
      • Online
        • Offered by Myers (F02) / Nelson (S03)
        • No face-to-face meetings
        • Mastery & competency based learning
        • High professor-student interaction
        • No student to student interaction
        • Full use of the WebCT environment
    19. Student Characteristics
      • Online Students
        • More likely to be
        • To be older
        • To be non-white
        • To be female
        • To have taken more credits
        • To be decided in their major
        • More favorably disposed to economics
      • Face-to-face Students
        • More likely
        • To be male
        • To be a freshman
        • To be undecided in their major
        • To spend less time gathering business and economic news.
    20. The online course design has influenced student success
      • ‘ Grade’ on pre-test status
      ‘ Grade’ on final examination status Fall 2002 and Spring 2003 WD and Inc ignored 86% 14% 0% 0% Face-to-face Course 83% 17% 0% 0% Online Course F C/D B A 13% 61% 16% 2% Face-to-face Course 18% 36% 40% 5% Online Course F C/D B A
    21. The online course design has influenced student success
      • Grades Distribution
      MC Learning Assessment Breakdown / Online 2-15 Total 9-15 Macro 2-8 Micro Modules 69% 19% 51% 31% 894 71% 26% 45% 29% 331 68% 15% 54% 32% 563 %C&A Analytical Conceptual Recognition Total 10% 40% 37% 8% Face-to-face Course 10% 0% 39% 33% Online Course F C/D B A
    22. Research design Student’s success =f( student’s initial endowments , awareness of the economy, attitudes about economics, course modality, student characteristics) Pre-test scores total on recall and recognition on simple application problems on complex problems GPA Previous attempt of course
    23. Research design Student’s success =f( student’s initial endowments, awareness of the economy , attitudes about economics, course modality, student characteristics) Survey of economic facts 20 questions…on trends and facts in the economy. 2 questions: How does the student collect business and economic news. How many hours per week does the student spend collecting news
    24. Research design Student’s success =f( student’s initial endowments, awareness of the economy, attitudes about economics , course modality, student characteristics)
      • Three Survey Questions:
      • Level of student report of being interested in economics.
      • Level of student report of liking this economics course.
      • Level of student report about likelihood of taking more economics courses.
      5=strongly agree 4=agree 3=indifferent/no opinion 2=disagree 1=strongly disagree
    25. Research design Student’s success =f( student’s initial endowments, awareness of the economy, attitudes about economics, course modality, student characteristics ) Online or face-to-face Major Level, e.g., freshman Credits completed Age Gender Ethnicity
    26. Dependent Variable: Student Success Outcome Measures
      • examscore on first 6 chapters
            • In class exam for face-to-face class
            • Average of first three module quizzes for online class
      • Final examination
            • ( FE, FE_recog, FE_apply, FE_complex )
            • First 3 or 4 levels of Bloom’s Taxonomy
      • Writing assignment
            • writing2
            • 4 th to 6 th level of Bloom
      • Regular Grades in class
    27. Results
      • Students’ Initial Endowments
        • Higher pretest and higher GPA are positive on examscore, writing2 and less so on FE.
        • Higher complex pretest scores and higher GPA are positive for writing2 and FE_Complex
    28. Results
      • Economic Awareness & Attitudes
        • “ Survey” is weakly positive for examscore and writing2, but has no effect on FE.
        • No combination of “News gathering” is ever significant.
        • “ Attitudes” are strongly significant for examscore and writing2, but no effect on FE
    29. Results
      • Student characteristics:
        • Those with an undeclared or undecided major do much worse than engineering majors for examscore and FE.
        • Older students do worse on examscore, no effect on writing2 and strongly positive on FE
        • Females do better in writing2 (strongly significance), but do worse on FE (almost significant)
    30. Results
      • Online Students:
      • Do much better on examscore
      • Show no significance difference in scoring on analytical and complex reasoning tasks.
          • Insignificant coefficients in writing2
          • Insignificant and small coefficients in
            • FE
            • FE_recog
            • FE_Apply
            • FE_complex
      • There is some evidence of a possible interaction with females
          • Weak suggestion in the data that
            • Men in online classes do better and
            • Women in online classes do worse all things equal
    31. Springboard + software
      • Springboard
      • Virtual Office Hours
        • Elluminate.com
        • Adobe Connect
      • Slidecasting (at sldeshare.net)
      • RecordPad (www.nch.com.au/recordpad)

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