665 Session11-data analysis-s13


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LIS 665 Teaching Information Technology Literacy
Dr. Diane Nahl
Spring 2013
University of Hawaii
LIS Program

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  • LIS 665 Teaching Information Technology Literacy Joe Murphy, Mobile Literacy: http://prezi.com/u9urpn3pw9xo/cil2010-mobile-literacy/
  • LIS 665 Teaching Information Technology Literacy
  • LIS 665 Teaching Information Technology Literacy
  • 665 Session11-data analysis-s13

    1. 1. Student Learning Outcomes:Data Analysis & Interpretation Student Learning Gains and Input for Revisions to Instructional Design •Dr. Diane Nahl Spring 2013 •LIS 665 Teaching Information Technology Literacy University of Hawaii LIS Program
    2. 2. Instructional Design Stages a. Needs Assessment: What is and what is needed? b. Define Goals & Objectives: What will they do and learn? c. Select Formats, Methods & Materials: How is best? d. Devise Test & Evaluation Procedures: Prototyping it. e. Construct & Teach Prototype: Performing it. f. Evaluate & Analyze Outcomes: What did they get? g. Revise & Recycle Steps: What will be different next time?LIS 665 Teaching Information Technology Literacy 2 Nahl 2013
    3. 3. Blended Librarians • Embedded librarianship (2004 Barbara Dewey) • Online librarianship • Cybrarian • Collaboration and partnerships with teaching faculty, students, and institutional or community events and initiatives. • Often immersed within ICT environments • Long-term involvementLIS 665 Teaching Information Technology Literacy 3 Nahl 2013
    4. 4. Learning Fundamentals • Learning is about actively making connections: • Biological: establishing neural networks, activating emotion receptors, and developing muscle memory • Conceptual: connecting ideas, making meaning, and problem solving • Experiential: interacting with self, others, and the socio-technical information environmentLIS 665 Teaching Information Technology Literacy 4 Nahl 2013
    5. 5. Learning Fundamentals • Learning is a developmental, cumulative process involving the whole person. • Learning is shared within a social process. • Learning that is constructivist, active and immersive is longer lasting. • Learning requires frequent feedback. • Learning often takes longer than teaching. • Learning is sometimes instantaneous.LIS 665 Teaching Information Technology Literacy 5 Nahl 2013
    6. 6. Holistic Instructional Design for the Three-fold Self • Teach the Heart • [A] Affective learning • Teach the Mind • [C] Cognitive learning • Teach the Body • [S] Sensorimotor learningLIS 665 Teaching Information Technology Literacy 6 Nahl 2013
    7. 7. Worksheet Data Analysis • Radcliff et al. Ch 11: Which types of scoring guides will you use on your outcomes data that measured performance? • In your case, forms, worksheets, and discussion posts were used to gather responses in active learning exercises. • How will you score the responses on the worksheets? How will you assign levels of completeness or correctness? • To answer the questions above complete the data analysis exercise Steps 1 through 8 on the following slides by April 4 (2 weeks).LIS 665 Teaching Information Technology Literacy 7 Nahl 2013
    8. 8. Worksheet Data Analysis a. First Step: Make a copy and then examine one of the online worksheets from an activity and read all of the responses from every student or team/group on that worksheet. a. Assign an ID code to each student directly on the worksheet and use that ID code in the spreadsheet. (Work only on copies of student worksheets) b. Second Step: Set up your scoring criteria for correct, partial, and incorrect or blank responses. a. You may score multi-part questions separately (e.g., Q1a, Q1b, etc. b. You may use various approaches given in this chapter but you must assign numerical scores for each level of correctness or ideal to each worksheet response/answer.LIS 665 Teaching Information Technology Literacy 8 Nahl 2013
    9. 9. Worksheet Data Analysis c. Third Step: Read each worksheet again and score the individual question item responses. Score all of the worksheets for that exercise. d. Fourth Step: Use the spreadsheet (shared last week) to record the individual question scores (so you know how everyone did on each question as in the Hillyer article Fig. 7.2). a. This requires an ID column and showing each student’s data in a row (e.g., Student 1, Student 2, etc.) and each question or part of a question in columns, then enter the student scores in the appropriate column. b. For clarity add a meaningful content word or phrase to the question number (e.g., Q4 Search Terms; Q5 Boolean) on the spreadsheet to easily show what the scores relate.LIS 665 Teaching Information Technology Literacy 9 Nahl 2013
    10. 10. Worksheet Data Analysis e. Fifth Step: Sum the columns and compute the average score for each question. [total of all scores on that question/total number of students]  f. Sixth Step: Sum the rows and compute the average for each student and convert to a percentage. a. [(total of all scores on every question by each student/maximum possible score) x 100] e. Seventh Step: Sum the total scores for all students to get a global score for the entire class on that worksheet, compute the average and convert to a percentage. a. [(global total of all questions for all students/maximum possible score) x 100]LIS 665 Teaching Information Technology Literacy 10 Nahl 2013
    11. 11. Worksheet Data Analysis h. Eighth Step: Examine the spreadsheet and look for patterns, e.g., a. What is the range of scores on each question (highest and lowest scores)? b. Which question was most difficult for most students? Which question was easiest for most students? c. If you graded each student based on the maximum possible, what letter grade would each receive (90-100% A; 80-89% B; 70- 79% C; 60-69% D; 50-59% F)? What letter grade would the entire class receive on this worksheet? d. What other patterns do you see in your data? These findings will be part of the Assessment Report write-up.LIS 665 Teaching Information Technology Literacy 11 Nahl 2013
    12. 12. Data Analysis Exercise: Content Analysis Method Ch 13 Radcliff et al. • Examine open-ended data from your session by reading all of the responses, may use Psych GG comments posted within 2 weeks after the session. Follow Content Analysis steps 1-7 + 8: • Select an open-ended narrative data instrument (minute writing reflection exercise, open-ended worksheet responses or session evaluation, or other type) and read the entire set of responses. • Begin with Step 1 Determine What Questions You Want to Answer and articulate what you intend to analyze and why. • In Step 2 determine what content you will extract and count for the analysis. • In Step 3 define what you will count as an occurrence • Necessary to be very specific and concrete to avoid ambiguityLIS 665 Teaching Information Technology Literacy 12 Nahl 2013
    13. 13. Data Analysis Exercise: Content Analysis Method • In Step 4 Create a Coding Sheet to organize data extraction (Google document spreadsheet) • In Step 5 Pre-test the coding sheet. Each Team member codes the same section individually, then compare your work • If necessary, discuss the differences and standardize the coding definitions to obtain nearly complete agreement in coding • Agreement among raters should be 95% or higherLIS 665 Teaching Information Technology Literacy 13 Nahl 2013
    14. 14. Data Analysis Exercise: Content Analysis Method • In Step 6 after achieving high agreement in coding, take separate portions of narrative and code it individually. • Compare coding at intervals to ensure agreement remains high • In Step 7 Use the spreadsheet to enter and then analyze (compute or summarize) the coded data. • It is best if you code independently and maintain individual spreadsheets, or separate sheets in one workbook. • Compare your individual results and compute the percent overlap (>94%) • Repeat steps 1-7 to analyze the rest of your open-ended data as well as the PSYCH GG comments posted after your sessionLIS 665 Teaching Information Technology Literacy 14 Nahl 2013
    15. 15. Data Analysis Exercise: Content Analysis Method • In Step 8 (not numbered in text) you will turn your spreadsheet data into information • Compute descriptive statistics of totals (frequencies), averages (the mean), and percentages • Clusters of terms within a concept, categories and their members • Compute individual student statistics and clusters (encouraged for this project) • Compute inferential statistics about the population (not required for this project) • Make a table or graphic chart to display results together. • Give the table a meaningful title.LIS 665 Teaching Information Technology Literacy 15 Nahl 2013
    16. 16. In Two Weeks • Instruction Unit Parts I & II due April 4 • Guest presentation: Sean Thibadeaux, Reference and instruction librarian, Hawaii Pacific University • Ch 8 • CTSB report • Mackey & Jacobson • Bring numerical data in tables and/or graphic charts from your session for data analysisLIS 665 Teaching Information Technology Literacy 16 Nahl 2013