Benefits - UBC CTLT

Using What You Have: Observational Data and the Scholarship of Teaching Catherine Finnegan Board of Regents of University System of Georgia Creating A More Educated Georgia Agenda Introductions and Definitions Sources of Data in CMS Study Examples Engagement Retention

Instruction University System of Georgia 35 public colleges and universities 4 Research Universities, 15 Regional/State Universities 4 State Colleges

12 Associate Colleges 253,552 students 9,553 full-time faculty Office of Information and Instructional Technologies Supports and coordinates the delivery of innovative technology resources, services, and solutions. Establishes a communications conduit among executive management for the university system about information and instructional technology.

Advanced Learning Technologies Provides academic enterprise systems and services for USG institutions. Fosters the development and implementation of collaborative online degree programs and training materials. Conducts research and evaluations to influence policy making, instructional practice and technology development.

Technology Use in Courses 80% 1994 70% 1995 60% 1996 1997

50% 1998 40% 1999 2000 30% 2001 20%

2002 10% 2003 0% 2004 E-mail Internet Resources

Webpages for Course Adapted from Campus Computing Study,2002-2004. Rising Use of IT in Instruction Percentage of courses using course management tools, by sector, 2000-2004 50% 45% 40% 35% 30% 25%

20% 15% 10% 5% 0% Public Univ. Private Univ. Public 4-Yr. College Private 4-Yr. College

Community Colleges Adapted from Campus Computing Study,2002-2004. USG Faculty Use of CMS 2005 Nearly half (46.3%) of all USG faculty currently use a CMS in their instruction. Almost two-thirds of users have increased their usage over time. Over two-thirds of users believe that a CMS has provided important advantages in improving student engagement in learning.

Over two-fifths of non-users would use a CMS if their issues were addressed. What CMS was Used For 90.6% enhanced their face-toface instruction 43.8% deliver fully on-line instruction 43.8% deliver hybrid courses * Based on 46.3% of respondents who were currently using a CMS. CMS and Student Engagement Increased amount of contact with their

students (55.6%) Increased student engagement with the course materials (63.5%) Allowed for inclusion of more interactive activities in their class (54.2%) Allowed them to accommodate more diverse learning styles (67.6%) * Based on 46.3% of respondents who were currently using a CMS. Evaluation Measures the effectiveness of an ongoing program in achieving its objectives Aims at program improvement

through a modification of current operations Two types of evaluations: Project Program Assessment Systematic collection, review, and use of information about educational programs undertaken for the purpose of improving learning and development Two types of audience: Accreditation Accountability

Scholarship of Teaching Sustained inquiry into teaching practices and students learning in ways that allow other educators to build on ones findings Directed toward other instructors in ones field and beyond Now Tell Me What you are interested in learning about your teaching practices and your students learning?

What projects are you now conducting? What data are you using to investigate? CMS In Scholarship of Teaching Content Surveys E-learning System e-Portfolio

SIS Student Online Activity LOGON RE-READ LECTURE NOTES REPLY to MESSAGE READ MESSAGE CREATE NEW

MESSAGE LOGOFF Emergence of a New Data Set = Large Data Set How is this data different from other inputs to pedagogical research? Its what the students actually did Compared to self-reporting

It captures the steps of the process Rather than the outcome alone Its quantitative Its easy to collect this data across a large number of students. How can CMS data be used? See patterns and trends Tell a story that explains the results Identify areas of improvement and targeted change Evaluate impact of changes

Patterns of Movement in Courses Comparison of Withdrawing, Non-Successful and Successful Student Access Logs 160 140 120 100 80 60 Page 40 Accessed 20 0

Access Order Withdrawer Non Successful Successful New evidence for? Course level inquiry Cross course and programmatic research College-wide policy review Typical Sources of Data

Student course evaluations and surveys Content analysis Grade distributions Interviews Portfolio review CMS Data Sources Individual, course, group and institutional activity reports Assessment reports Survey reports Discussions Assignments Content analysis

Advantages of CMS Data Data captured automatically as students interact with software Reports available at each level (course, group, institution) Time parameters of reports allow more timely and granular review Consistency of data across time and course Instructor control of tools Disadvantages of CMS Data

Only reports actions doesnt explain them Access to data based on role Canned report data limited Data collection dependent on proper formatting of content and assessment Activity Data Reports Available to Instructors Summary of Activity Tool Usage Components Usage Content File Usage Entry and Exit Pages

Student Tracking Entry Into Reports and Tracking Available from TEACH only List of Available Reports Date and time parameters can be set. Summary of Activity Reports

Provides a general overview of student and auditor activity Information contained Total number of sessions Average session length Average sessions/day by weekday by weekend Most active day

Least active day Most active hour of day Least active hour of day Example Summary of Activity Report Tool Usage Reports Provides an overview of how often tools are used Tools available

Assessments Assignments Bookmarks

Calendar Chat/Whiteboard Content File Discussions Mail Media Library Notes PowerLinks Proxy Tool SCORM Module Organizer Page URL Information contained

Total number of sessions for each tool Average time per session Total time for all tool sessions Percent time for each tool compared with total time Example Tool Usage Report

Component Usage Reports Provides an overview of how often students use each component of a course Component which component student has accessed Visits total number of times student has visited a component Average time/visit average time students spend per visit

Total time total amount of time students spent for all components Percent of total visits relates time spent in a given component compared to total time spent for all components Example Component Usage Report Entry and Exit Page Reports Provides an overview of pages used most frequently for course entry and exit

Page Name which page student entered or exited Tool Used which tool was used to enter or exit Page Usage total number of times student entered or exited from the page Percent of Total Usage relates the number of times a page is used to enter or exit to total number of entries or exits

Example Entry and Exit Page Reports Content Usage Reports Provides an overview of the content files viewed by students Content file the content file that students have accessed Sessions the total number of content file sessions

Percent of Total Sessions relates the number of content file sessions to the total number of sessions for all content files Content File Usage Report Content File Usage Graph Student Tracking Reports

Provides an overview of student activities in the course, displaying both general and detailed statistics First Access Last Access Sessions Total Time Mail Read Messages Sent Messages Discussion Read Messages Sent Messages

Calendar Chat and Whiteboard Assessments Assignments URL Media Library

Content Files Aggregate Student Tracking Individual Student Tracking Data from Quizzes and Surveys Performance Displays student scores for quiz submissions Item Statistics Displays performance statistics for individual

questions. Compares the performance of selected students with the entire class Summary Statistics Compares all students results in one table Class Statistics Displays class performance for individual questions Performance Displays student scores for quiz and survey submissions

Item Statistics Displays performance statistics for individual questions. Item Statistics Displays performance statistics for individual questions. Compares the performance of selected students with the entire class Summary Statistics Compares all students results in one table

Class Statistics Displays class performance for individual questions Additional Data Sources Discussions and Mail Assignments Course Evaluations and Surveys Student Information Systems Now Tell Me Considering the projects that you outlined earlier,

What data found in a CMS might be used to investigate your theories? How would you collect this data? Would you triangulate this data with other sources? Typical Statistical Methods Frequency Distributions and Trends Measures of Central Tendency ANOVA Regression

Want to play with some data? Go to http://www.statcrunch.com Create an account Upload data file: ExampleData.xls Run Summary Statistics Studies on Student Persistence and Achievement Creating A More Educated Georgia Research Setting:

eCore Fully online, collaboratively developed, core curriculum courses offered jointly by institutions in the University System of Georgia. Supported by University System. Courses include the humanities, social sciences, mathematics, and sciences. Over 25 courses and 2000 enrollments in Spring semester http://www.gactr.uga.edu/ecore/ Underlyling Problem: Student Retention Overall Course Retention: Fall 2000-Spring 2003

Withdraw and Complete 100% 15 80% 69 82 121 84 85

144 204 60% Percent 40% 43 145 220

266 Fall 2001 Spring 2002 128 245 294 631

20% 0% Fall 2000 Spring 2001 Summer 2001 Term Summer 2002

Fall 2002 Spring 2003 Findings from Four studies Predicting Student Retention & Withdrawal Tracking Student Behavior & Achievement Online Examining Student Persistence and Satisfaction Perspectives and Activities of Faculty Teaching Online

Study 1: Predicting Student Retention & Withdrawal Purpose: to investigate student withdrawal and retention in eCore courses. How well can a students group membership (completion & withdrawal) be predicted? A two group Predictive Discriminant Analysis (PDA) is used to predict students withdrawals and completions in online courses. Authors: Morris, Wu, Finnegan (2005). Variables Two grouping variables - student completers

- student withdrawers Nine predictor variables - gender, age, verbal ability, math ability, current credit hours, high school GPA, institutional GPA, locus of control and financial aid. Model A: Two-group PDA Predictive Model, Spring 2002 Age Withdraw

Gender Inst Cum Cr HR Inst Cum GPA HS GPA SATVerbal SAT-Math Grouping Variable Complete Model A : Findings The most important predictors in

Model A are - high school GPA - mathematic ability (SAT-math) Model A, prediction with 62.8% accuracy Model B: Two-group PDA Predictive Model, Fall 2002 Withdraw FA Grouping Variable

Locus Complete Model B : Findings Financial aid showed significant differences between the responses of withdrawers and completers (x2=4.84, df=1, p<.05). Completers were more likely to receive financial aid that withdrawers. Locus of control has significant differences between the responses of withdrawer and completer(X2= 4.205, df= 1, p<.05).

Completers were more likely to have internal motivation than withdrawers. Model B predicted with 74.5% accuracy Study 1: Summary Students withdraw for a variety of reasons. Primary instructional reasons for withdrawing included too much work in the online course, preferred the classroom environment, and disliked online instruction. High school grade point average and

mathematics SAT were related to retention in the online courses. Students who completed courses were more likely to have received financial aid. Students who completed courses were more likely to have a higher internal locus of control. Study 2: Tracking Student Behavior & Achievement Online Purpose: to examine student behavior by tracking what students do online and how long they spend on each activity. Data: analyzed student access tracking

logs. Coded over 300,000 student activities. Frequency: number of times student did a behavior Duration: time spent on the behavior Authors: Morris, Finnegan, Wu (2005) Research Questions What are the differences and similarities between completers and withdrawers in various measures of student behavior online? How accurately can achievement be predicted from student

participation measures in online learning courses? Variables (n=8) Frequency and Duration of viewing course content viewing discussions creating new discussion posts responding to discussion posts Over 400 students and 13 sections of 3 courses Frequency of Learning Activities

Content Pages Viewed 500 400 300 200 100 Average Over Term 0 Withdrawer English NonSuccessful Successful

Geology History Discussion Posts Viewed 1600 1400 1200 1000 800 600 400 200 Over Term

Average 0 Withdrawer English NonSuccessful Successful Geology History Frequency of Learning Activities Original Posts Created

Follow-up Posts Created 100 50 80 40 60 30 40

20 10 Average Over Term 0 Withdrawer English NonSuccessful Successful Geology History

20 Average Over Term 0 Withdrawer English NonSuccessful Successful Geology History Duration of Learning Activities

Total Time Spent During Term Viewing Content Viewing Discussions Creating Original Posts Creating

Follow-up Posts Average Overall Time Per Week Withdrawers n=137 10 hours 2.6 hours

3 hours <1 hour <1 hour <1 hour NonSuccessful Completers n=72 18 hours 9 hours

6 hours <1 hour <1 hour 1.2 hours Successful Completers n=214 54 hours

19 hours 19 hours 1 hour 1.5 hours 3.75 hours N=423 Findings: Completers & Withdrawers Completers had more frequent activity and spent more time on task on all 4 measures than unsuccessful completers and

withdrawers. Withdrawers spent significantly less time and had less frequent activity than completers on all 4 measures (p>.001). Expected. Significant differences in participation also existed between successful and unsuccessful completers. Multiple Regression Model for Impact of Participation on Achievement Successful and Non-Successful Completers n = 286

Findings: Successful and Unsuccessful Completers The participation model explained 31% of the variability in achievement. 3 of 8 variables were significant at the p.<.05 level and good predictors of successful completion (achievement/grades). # of content pages viewed # of discussion posts viewed Seconds viewing discussions Summary: Study 2

Time-on-task matters; withdrawers did engage significantly in number or duration of activities at the online site. Successful completers engaged significantly with the online course: Going repeatedly to content pages (frequency) Going repeatedly to discussion posts (frequency) Spending significant time reading discussion posts (duration) Study 3: Understanding Student Persistence and Satisfaction

Purpose: To investigate issues that affect course completion, course withdrawals and satisfaction with online courses. Survey (n=505, response 22%) Indepth Interviews 8 withdrawers 8 completers Authors: Boop, Morris, Finnegan (2005) Successful completers Felt membership in the course. Understood course layout, expectations, assignments. Faculty feedback was important.

Clarity about course was important. Used words indicating drive and persistence to succeed. Could overcome course-related problems. Withdrawers/ Unsuccessful Students Spoke of being lost & confused in the course. Needed more direction & help from faculty to understand the course goals, expectations, assignments & design. Needed more explicit help with discussions and understanding involvement. Needed more managerial and navigational

help. Study 4: Perspectives and Activities of Faculty Teaching Online Purpose: To explore the activities and perspectives of faculty teaching online Interviews (n=13) Analysis of archived courses (10) Authors: Morris, Xu, Finnegan (2005) Classification of Faculty Roles Classification of Faculty Roles (N=10)

500 400 300 200 100 Number of Postings 0 N N N N Pedagogical

E E Social E Managerial E) E E

Summary: Study 4 Novice instructors are far less engaged with students online. Experienced faculty posted with a ratio of 1:6 --faculty to student posts Experienced faculty interchanged pedagogical, managerial, and social roles online Students in courses with experienced faculty engaged more often in discussions Faculty visibility is important to student participation. Novice faculty need extensive assistance to understand online instruction.

Best Practices: Students Students should be advised that for online courses Time on task matters for successful achievement; Online courses may be activity and time intensive; requires pro-active, engaged students; Will not be easier for academically marginal students; Students should directly (and as needed) seek instructor help to understand course structure and course-related objects and objectives

Best Practices: Faculty 1 Faculty should Understand Low participation early in the term as an indicator for withdrawal or unsuccessful completion. Should monitor/track all students early in the course term to see lags in participation Understand the role of student expectations & attitudes in persistence Should understand the role of Locus of Control in Withdrawing and Unsuccessful completion Best Practices: Faculty 2 Should engage managerial functions to

explain course layout, assignments expectations (may be more important than pedagogical function at times) Understand that course layout and instructions are not necessarily intuitive to the students Should seek to understand previous academic preparation of students and make adjustments accordingly Comparing Student Performance to Programmatic Learning Outcomes

Link graded activities within courses to eCore common student learning outcomes Determine achievement of learning outcomes based on trends in grades Identify additional means of documenting student achievement of learning outcomes

Benefits of CMS Data New quantitative evidence Complements survey, grades, and portfolio data Very detailed information about engagement and learning process Reduce burden on faculty and staff Automatically collects evidence Leverages tools already in use Opportunities for Studies Increase awareness of data sources available to study

pedagogy and outcomes Encourage systematic analysis of existing data for pedagogical improvement Identify additional data elements within CMS and other data sources Challenges for Studies Use of CMS not widespread nor extensive Essential tools not used (i.e., gradebook) Siloed data sources (Greens ERP Turtle)

Conclusions Data collected in CMS and other systems can be used to inform the scholarship of teaching Systematic and ongoing New sources of data offer opportunities to study perennial questions from different perspectives. Thank You! Catherine Finnegan [email protected]

Presentations and Citations Available at: http://alt.usg.edu

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