A Look at Online Teaching and Learning at UCF Charles D. Dziuban Patsy D. Moskal University of Central Florida

The University of Central Florida

UCF terminology for courses utilizing web instruction “ W eb” courses: delivered entirely over the Web, with no regular class meetings “ M ixed-mode” courses: some face-to-face instruction is replaced with web instruction so that on-campus time is reduced “ E nhanced” courses: delivered entirely in face-to-face mode, but with web enhancements

Levels of faculty development IDL 6543 Mixed-mode course to design, develop, & deliver M or W course ADL 5000 Online course modeling delivering existing M or W course Essentials Online course for faculty who want to supplement their F2F course without reducing any class time.

Growth of UCF online learning environments Enrollment Academic Year – Summer, Fall, Spring M+E courses W courses

The Evaluation

Principles that guide our evaluation Evaluation must be objective. Evaluation should conform to the culture of the institution. Uncollected data cannot be analyzed. Data do not equal information. Qualitative and quantitative approaches must complement each other. We must show an institutional impact. Our results may not be generalized beyond UCF.

Distributed Learning Impact Evaluation components Reactive behavior patterns Success rates Attitudes Demographic inertia Withdrawal rates Strategies for success Students Online programs Real-time surveys Writing project model Large online classes Faculty Modified instructional theories Student evaluation of instruction

Student Results

What we have found regarding online students The majority of students enrolled in fully online (W) courses are also enrolled in face-to-face courses, The distribution of students by ethnicity is approximately the same for all modalities, Fully online (W) courses consistently have more females than other modalities, On the average, students enrolled in W courses are oldest, followed by those in M sections then face-to-face.

Student satisfaction in fully online and mixed-mode courses 39% Fully online (N = 1,526) Mixed-mode (N = 485) 41% 11% 9% Very Satisfied UnsatisfiedSatisfied Neutral 38% 44% 9% Very Unsatisfied 3% 5% 1%

Success rates by modality Spring 01 through Spring 03 F2F MTotal N= 139,444 students W Percent

A segment model depicting success in course by college, gender, and modality 93.0% n=31, % n=20, % n=10, % n=62, % n=9, % n=2, % n=9, % N=1,242 Females Males F2F WM F2F,WM College of Health & Public Affairs

Withdrawal rates by modality Spring 01 through Spring 03 F2F MTotal N= 147,194 students W Percent

Student Behavior Types

Research on reactive behavior patterns Theory of William A. Long, University of Mississippi Ambivalence brings out behavior patterns Provides a lens for how “types” react to different teaching styles

Resources Personality Emotional maturity Sophistication level Level of intellect Educational level Character development

A description of Long behavior types Aggressive Independent high energy action-oriented not concerned with approval speaks out freely gets into confrontational situations Passive Independent low energy not concerned with approval prefers to work alone resists pressure from authority Aggressive Dependent high energy action-oriented concerned with approval rarely expresses negative feelings performs at or above ability Passive Dependent low energy concerned with approval highly sensitive to the feelings of others very compliant

A description of Long behavior traits Phobic exaggerated fears of things often feels anxious often sees the negative side doesn’t take risks Compulsive highly organized neat, methodical worker perfectionist strongly motivated to finish tasks Impulsive explosive quick-tempered acts without thinking frank short attention span Hysteric dramatic and emotional more social than academic artistic or creative tends to overreact

Distribution of Long types and traits for fully online students AI 21% PI 18% AD 54% PD 7% 51% 75% 26% 30% (N=1,437) (N=1,520)

Distribution of Long types and traits for mixed-mode students AI 17% PI 23% AD 52% PD 8% 54% 76% 23% 32% (N=472)

Distribution of Long types and traits for Composition I students AI 20% PI 23% AD 44% PD 14% 50% 53% 38% 40% (N=1,054)

Long types and traits for Web, mixed- mode, and general education students Web (N=1,533) Mixed-mode (N=491) Comp I (N=1,054) Aggressive Dependent 54%52%44% Passive Dependent 7%8%14% Compulsive74%76%53% Impulsive26%23%38% Types Traits

Faculty Results

A lot more time Time to develop course as compared with a comparable face-to-face section More work Equal to or less than W n=56 M N=43 Modality A little more time About the same A little less time A lot less time 2% 52% 21% 5% 77% 43%

2% A lot more time Time in weekly course administration activities as compared with a comparable face-to-face section More work Equal to or less than W n=55 M N=42 Modality A little more time About the same A little less time A lot less time 4% 43% 15% 19% 60% 38% 20%

30% A lot more time Time in weekly course delivery activities as compared with a comparable face-to-face section More work Equal to or less than W n=55 M N=42 Modality A little more time About the same A little less time A lot less time 20% 5% 28% 29% 9% 37% 13% 15%

Amount of interaction in Web classes compared to comparable F2F sections More interaction Equal to or less than W n=55 M N=40 Modality 13% 45% 16% 15% 62% 30% 2% 7% 8% 3% Increased Somewhat increased About the same Somewhat decreased Decreased

Quality of interaction in Web classes compared to comparable F2F sections Better interaction Equal to or less than W n=55 M N=43 Modality 22% 30% 33% 19% 35% 37% 9% 2% 14% Increased Somewhat increased About the same Somewhat decreased Decreased

Very satisfied Faculty satisfaction with their varying course modalities Positive Neutral or negative W n=55 M N=43 F2F N=64 Modality Satisfied Neutral Unsatisfied Very unsatisfied 6% 44% 5% 58% 5% 49% 38% 7%

Faculty willingness to teach Web courses in the future Positive Neutral or negative W n=71 M N=53 Modality 81% 16% 2% 69% 13% 10% 6% 4% Definitely Probably Probably not Definitely not

Relationships of faculty satisfaction with class interaction and workload (TAU-b) WM (n=53)(n=38) Amount of interaction.39**.34* Quality of interaction.43**.51** Time to develop Time to administer Time to deliver *p<.05; ** p<.01

Student Ratings

Student Ratings by Modality Very ModalityExcellentGoodGoodFairPoor F2F (N=628,623) E (N=6,632) M (N=11,450) W (N=5,435)

Facilitation of learning Communication of ideas Excellent Very Good Good Fair Poor Then... The probability of an overall rating of Excellent =.93 & The probability of an overall rating of Fair or Poor =.00 If... A decision rule based on student evaluation responses and the probability of faculty receiving an overall rating of Excellent

A comparison of excellent ratings by college unadjusted and adjusted for instructors satisfying Rule 1 CollegeUnadjusted %Adjusted % Arts & Sciences Business Education Engineering H&PA (N=441,758) (N=147,544)

A comparison of excellent ratings by course modality--unadjusted and adjusted for instructors satisfying Rule 1 F2F E M W ITV Course ModalityUnadjusted % Adjusted % N=709,285 N=235,745

Research Initiative for Teaching Effectiveness

The transition from online to face-to-face courses Distributed Learning Impact Evaluation Research Initiative for Teaching Effectiveness

Research Initiative for Teaching Effectiveness For more information contact: Dr. Chuck Dziuban (407) Dr. Patsy Moskal (407)