Quarterly Review of Distance Education

Name: Quarterly Review of Distance Education | Volume 23 #2
Brand: IAP - Information Age Publishing
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Volume 23 #2

Michael Simonson Anymir Orellana(Editor)

IAP - Information Age Publishing

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Published on 1. December 2022

91 pages

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Front Cover
Editors and Editorial Board
Table of Contents
Statement of Purpose
Quarterly Review of Distance Education
"Research That Guides Practice"
Volume 23, Number 2, 2022
ARTICLES
RESEARH BRIEFS
COLUMN
Quarterly Review of Distance Education Editors and Editorial Board
Anymir Orellana Editor
Vanaja Nethi Assistant Editor
Department Editors
Chris Luchs and Kae Novak
Farah Bennani and Kae Novak
International and Cultural Perspective
Farah Bennani and Kae Novak
Editorial Board
A Case Study in How Different Teaching Methods Affect Different Student Demographics Across a University
Douglas R. Moodie
Kennesaw State University
Little research compares hybrid to online and face-to-face (F2F) teaching. Nearly all this research assumes no difference in the students' demographics entering F2F, hybrid, or online sections of a course. This study used all the data from 5 years ...
Introduction
Research Questions
1. Are there any differences (both with demographics and previous academic achievement) in the students using the different teaching methods across a university?
2. Do students from different demographics and in different parts of the university have different student final grade outcomes in different methods?
LITERATURE REVIEW
Online to F2F Comparisons
No Examination of Student Demographics
Examination Included Student Demographics
Hybrid Comparisons
Studies That Did Not Examine Student Demographics
Studies That Looked at Student Experience
Studies That Examined Student Demographics
Analysis
The Data Set
1. An arbitrary random number instead of student name. The researcher deleted this column from the working database as not useful.
2. Final course grade in letters. Letter grades were converted to numbers: A = 4, B = 3, C = 2, D = 1, F = 0.
3. The previous overall university GPA of the student at the start of the course was missing for some students who were just entering KSU. Previous GPA varied from zero to four. Starting transfer and first- year students would have no previous GPA. T...
4. Age varied from 14 to 75. The study removed all those under 18, a small number, for institutional review board reasons.
5. The analysis converted teaching method [online (OL), hybrid (Hy), or all-in-person (F2F)] to zero-one variables. That is online is [1, 0, 0], hybrid is [0, 1, 0], and F2F is [0, 0, 1] for columns online, hybrid, and F2F.
6. Term-Fall, Spring, or Summer. Some analyses used one for summer and zero for fall or spring because the summer term is a different length (2, 4, 6, or 8 weeks, rather than 15 weeks).
7. Calendar year.
8. Course name, consisting of discipline and number.
9. College.
10. Academic department home of the course. I deleted extraneous courses offered through non-KSU-only entities.
11. Course number. The first digit of course number gave the course level (1 = freshman, 2 = sophomore, 3 = junior, or 4 = senior)
12. Sex of student. This researcher converted this to male = 1, and female = 0.
13. Reported ethnicity converted an ethnicity of Alien, Asian, Black, Hispanic, Multiethnic, and White to zero or one variable. For example, Alien was [1, 0, 0, 0, 0, 0] for columns Alien, Asian, Black, Hispanic, Multiethnic, and White. Other ethnici...
14. The analysis did not use Instructor ID, which was an assigned random number.
15. Previous number of F2F courses taken.
16. Previous number of hybrid courses taken.
17. Previous number of online courses taken.
Basic Characteristics of the Dataset
Table 1
Properties of All Variables With Mean and Standard Deviation or Percentage of Total Dataset (N = 939,917)
6E+08
18
33295961
3463181
1.7E+08
17609256
9.614
7.7E+08
17609274
Statistical Analysis
Table 2
Partial Correlation Results of All Variables With Course Final Grade
Table 3
Regression on Course Grade Using all Predictors Without Previous GPA
939911
16
8370061
523128
396008
939895
1241605
1.3210
939911
9611666
0.010
0
0.011
0
0.010
0
0.002
0
0.008
8.69E-05
0.009
0
0.010
0.129811
0.009
6.4E-42
0.012
1.29E-21
0.010
9.63E-43
0.004
1.8E-232
0.000
1.1E-15
0.000
3E-100
0.000
6.9E-185
0.001
1.73E-17
0.001
0
Table 4
Results of Regression of Course Grade With Only Previous GPA Including Zero Previous GPA
0.944
0.892
1.053
798860
1
7308591
7308591
6596406
798859
885108.3
1.108
798860
8193699
0.9548
0.00037
2568.3
0
Table 5
Mean Data for All Students by Method
741246
49709
148962
939917
Analysis of Method Effect
Table 6
T Test for All Students by Method Comparisons
-0.044
-0.029
-0.015
0.100
-0.137
0.237
699536
592283
107253
46919
206958
71836
-7.680
-8.341
-2.321
8.1E-15
3.69E-17
0.0102
1.62E-14
7.39E-17
0.0203
2.959
3.003
2.988
1.363
1.263
1.500
741245
41709
148962
Analysis Using Previous GPA
Analysis Using Previous GPA and Sex
Analysis Using Previous GPA and Ethnicity
Analysis for Sex
Analysis by Ethnicity
Analysis by Ethnicity and Sex
Analysis by Course Level
Analysis for Academic Term
Analysis by Year
Analysis by Age
Analysis by College
Analysis by Department
Table 7
This Table Needs a Title
Table 7
(Continued)
Table 8
Grade Data for All Students by Department and Method
2.881
2.873
3.435
2.955
Hybrid
2.878
2.878
3.200
3.185
3.547
3.228
Hybrid
2.761
2.756
2.723
2.807
F2F
3.057
3.844
2.594
2.559
3.240
2.368
Hybrid
3.046
3.064
2.975
2.777
F2F
3.022
3.016
3.137
2.956
Hybrid
3.029
3.029
2.899
2.929
2.438
2.859
F2F
3.353
3.348
3.623
3.731
Online
2.985
2.988
3.376
2.897
Hybrid
3.028
3.028
3.559
3.609
3.216
3.581
3.605
3.537
3.299
Online
2.882
2.844
3.481
3.161
Hybrid
3.074
3.076
3.030
2.902
2.917
2.920
2.762
Hybrid
3.071
3.108
3.011
2.965
F2F
2.969
3.258
3.630
3.473
Hybrid
3.220
3.304
2.759
2.554
F2F
3.053
3.119
3.401
2.603
Hybrid
2.996
2.944
2.876
3.046
Online
3.367
3.367
2.850
2.800
2.923
2.998
Online
3.213
3.264
3.305
3.055
Hybrid
2.746
2.770
3.104
2.589
Hybrid
3.811
3.809
2.953
F2F
3.480
3.529
3.461
3.424
F2F
3.269
3.363
3.265
3.180
Hybrid
Table 8
(Continued)
3.108
3.270
3.039
2.961
F2F
3.214
3.077
3.474
3.227
Hybrid
3.668
3.668
3.226
3.233
3.358
3.183
Hybrid
3.265
3.264
3.587
3.220
Hybrid
3.032
3.091
3.622
2.950
Hybrid
2.407
2.413
2.464
2.304
Hybrid
3.344
3.349
2.985
3.395
Online
3.367
3.429
2.782
3.057
F2F
3.515
3.443
3.921
3.936
Online
3.033
3.040
2.668
2.904
2.893
2.999
2.791
Hybrid
3.126
3.126
3.645
3.674
3.485
3.444
F2F
3.474
3.551
3.312
3.155
F2F
2.966
2.997
3.071
2.896
Hybrid
2.984
2.981
3.220
2.982
Hybrid
2.881
2.923
2.918
2.523
F2F
3.097
3.152
3.108
2.960
F2F
3.280
3.420
3.125
2.806
F2F
2.974
2.959
3.163
2.988
Hybrid
3.126
3.166
3.190
3.010
Hybrid
Table 9
Number of Best Methods for Course Grades by Departments
14
20
6
10
50
Conclusions
Detailed Results for Course Final Grade
Method
Previous GPA
Sex
Ethnicity
Ethnicity and Sex
Course Level
Course Term
Year
Age
College
Department
Limitations of the Study
1. The use of a previous GPA to represent the academic ability of an incoming student is a convenient assumption. However, that is how most students rate their learning.
2. The use of course final grade to represent learning from a course is a common approximation of learning.
3. This analysis did not consider other factors like how many online or hybrid courses the student had done before the course, how many online or hybrid courses the student took simultaneously, whether the student was only taking online courses, or m...
4. In the main study, hybrid sections were only a small proportion of the total data.
5. The study did not examine differences between instructors. However, many instructors grade harder than others do for the same course, and hybrid teaching instructors may grade higher than those only teaching other methods.
6. The data for this case study comes from one university, and other universities and colleges may show completely different patterns.
Discussion
Answers to Research Questions
Further Points
1. Students manage their time better to meet the course deadlines.
2. Students get professor and classmate face time.
3. Everyone is equal in online discussions.
4. The learning does not stop when students leave the classroom.
5. It can provide deeper and more effective learning.
IMPLICATIONS
FUTURE WORK
References
Using Important-Performance Analysis to Guide Instructional Design Decisions for E-Service Learning
Sheri Conklin
University of North Carolina Wilmington
Designing experiential learning activities requires an instructor to think about learning outcomes. Using importance-performance analysis (IPA) can assist with the instructional design of the activities for implementing service-learning in distance e...
Introduction
Designing for Online Service-Learning
Methods
Participants
Importance-Performance SELEB Survey
Table 1
Factors and Associated Variables of the SELEB Scale
Table 2
Mean Comparison of the SELEB Factors
4.58
4.58
4.52
4.33
3.83
3.81
3.89
4.22
Service-Learning Project Description
Results
Table 3
Paired t-Test Important-Performance of SELEB Factors
0.00
1.00
0
1.64
.139
.37
.096
.926
.035
-1.66
.135
.40
Table 4
T-Test Importance-Performance of SELEB Items
A
4.67
4.67
B
4.67
4.67
C
4.44
4.56
D
4.11
4.22
E
4.00
3.67
F
4.67
4.44
G
3.44
3.67
H
3.78
3.67
I
4.00
3.67
J
3.78
4.33
K
4.44
4.44
L
4.33
4.33
M
4.56
4.22
N
4.67
4.67
O
4.11
4.00
DISCUSSION
LIMITATIONS
References
Can Educators Prevent a "Wild West" Scenario in Giving Online Exams?
Clement Chen
The University of Michigan-Flint
Keith T. Jones, Mark Lawrence, and Jill M. Simpson
University of North Alabama
This article discusses the necessity of using technology for exam proctoring in an era of increasing growth in online course delivery. The article discusses a recent generational trend affecting the rationalization leg of the "fraud triangle" gen...
Introduction
Respondus Lockdown Browser
Honorlock
Examity
What Else Can We Do?
Discussion/Conclusions
References
Faculty Perceived Barriers of Online Education at a Midwestern University in Ohio
Juenethia Tooson Fisher
Department of Housing and Community Development, City of Toledo
Berhane Teclehaimanot
The University of Toledo
This study extended research conducted by Lloyd et al. (2012) and investigated faculty perceived (interpersonal, institutional, training/technology, and cost/benefit) barriers to online education. Statistical analysis revealed three major items: (1) ...
Introduction
Statement of Problem
Purpose of the Study
Methodology
Research Questions
1. Is there a significant difference in faculty perceived institutional barriers for online education based on full-time status?
2. Is there a significant difference between faculty-perceived institutional barriers for online education and years of online teaching?
3. Is there a significant relationship between faculty-perceived institutional barriers to online education and age?
4. Is there a significant difference between faculty-perceived interpersonal barriers and gender?
5. Is there a significant difference in faculty perceived technology barriers and previous online courses related to online teaching?
Instrument
Data Collection
Data Analysis
Results
Table 1
Demographic Characteristics of Respondents
39
33.9
58
50.4
9
7.8
9
7.8
78
67.8
24
20.9
13
11.3
22
19.1
18
15.7
14
12.2
23
20.0
1
0.9
28
24.3
9
7.8
40
34.8
69
60.0
6
5.2
13
11.3
47
40.9
47
40.9
8
7.0
39
33.9
28
24.3
14
12.2
24
20.9
10
8.7
Table 2
Independent Samples t Test by Institutional Barriers Full and Part Time
26.8
5.5
26.7
4.3
0.11
&.05
Table 3
ANOVA Institutional Barriers Based on Years of Teaching Online
28.1
4.9
28.6
4.6
26
3.5
22.7
5.4
8.19
0.001
Table 4
Pearson Correlation by Institutional Barriers Based on Age
-
-0.15
-
Table 5
Independent Samples t Test by Interpersonal Barriers and Gender
14.2
3.5
14
3.7
0.83
&.05
Table 6
Independent Samples t Test by Technology Barriers and Previous Courses
4.1
1.4
4.3
1.2
-0.69
&.05
Conclusions
References
USING KNOWLEDGE-BUILDING DISCOURSE eXplorer TOOL FOR ANALYZING INTERNATIONAL STUDENTS' REFLECTIONS ON ACADEMIC DISCOURSE
Chynar Amanova
Northern Illinois University
Current technologies for qualitative data analysis treat all types of data analysis as a homogeneous category, and for this reason, the value of other technologies for a discourse analysis of transcripts is not well examined. Therefore, the current s...
Introduction
Knowledge Building Process and Potential of Technologies for Discourse Analysis
Theoretical Framework
PURPOSE OF THE STUDY
Methodology
DATA COLLECTION
Data Analysis Using Knowledge Building Discourse eXplorer
DATA ANALYSIS PROCESS
Discussion
References
Virtual Field Placements
Tom Brady and Mary Ella Taylor (Moore)
University of Mississippi
In this article, the authors discuss virtual teaching and how it affects teacher candidates and education program providers. This is significant not only because of the opportunistic nature of virtual teaching experiences, but also because the 21st c...
Background
VIRTUAL TEACHING EXPERIENCE
DATA COLLECTION
Initial Data Collection
Second Data Collection
MODIFICATIONS
Ramifications
Ramifications on Teacher Candidates
Ramifications on Education Program Providers
Observations That Guide Practice- Based on Research
Miscues, Oversights, Errors, and Blunders of the Distance Learning Instructor: What Not to Do and How to Correct Them
Errol Craig Sull
Purdue Global University
Conference Calendar
ICDETM 2022: International Conference on Distance Education and Teaching Methodologies
2022 6th International Conference on Education and E-Learning
ICEDEIET 2022: International Conference on Education, Distance Education, Instructional and Educational Technology
ICELOET 2023: International Conference on E-Learning and Online Education Technologies
National Future of Education Technology Conference
ICELDL 2023: International Conference on E-Learning and Distance Learning
Instructional Technology Council 2023 Annual Conference-eLearning
Digital Learning Annual Conference
16th International Conference on E-learning and Innovative Pedagogies
ICOLDE 2023: International Conference on Open Learning and Distance Education
Author Biographical Data
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Quarterly Review of Distance Education

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