Key Findings:   E-learning is comparable to traditional teacher-led classroom instruction in terms of effectiveness.  E-learning has specific features that may influence learning: content, immersion, interactivity, and communication.  Barriers to e-learning adoption include fraud and cheating, digital divides and their impact on low income and underprepared students, and cost issues

Bell, Bradford S

Federman, Jessica E

English

Key Findings:  E-learning is comparable to traditional teacher-led classroom instruction in terms of effectiveness.  E-learning has specific features that may influence learning: content, immersion, interactivity, and communication.  Barriers to e-learning adoption include fraud and cheating, digital divides and their impact on low income and underprepared students, and cost issues.No1_13_ResearchLink_Bell_elearning_FINAL.pdf: 1470 downloads, before Oct. 1, 2020

Bell, Bradford S.

Federman, Jessica E.

eCommons@Cornell

E-Learning Works - Exactly How Well Depends on its Unique Features and Barriers

DigitalCommons@ILR

Cornell University ILR School 
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CAHRS ResearchLink Center for Advanced Human Resource Studies (CAHRS) 
1-2013 
E-Learning Works - Exactly How Well Depends on its Unique 
Features and Barriers 
Bradford S. Bell 
Cornell University, bb92@cornell.edu 
Jessica E. Federman 
Cornell University 
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E-Learning Works - Exactly How Well Depends on its Unique Features and 
Barriers 
Abstract 
Key Findings: 
• E-learning is comparable to traditional teacher-led classroom instruction in terms of 
effectiveness. 
• E-learning has specific features that may influence learning: content, immersion, 
interactivity, and communication. 
• Barriers to e-learning adoption include fraud and cheating, digital divides and their impact 
on low income and underprepared students, and cost issues. 
Keywords 
electronic learning, e-learning, online learning, HR strategy 
Comments 
Recommended Citation 
Bell, B. & Federman. J. E. (2013, January). E-learning works - Exactly how well depends on its unique 
features and barriers (CAHRS ResearchLink No. 1). Ithaca, NY: Cornell University, ILR School. 
This article is available at DigitalCommons@ILR: https://digitalcommons.ilr.cornell.edu/cahrs_researchlink/24 
 E-learning is comparable to traditional teacher-led classroom instruction in terms 
of effectiveness. 
 E-learning has specific features that may influence learning: content, immersion, in-
teractivity, and communication. 
 Barriers to e-learning adoption include fraud and cheating, digital divides and their 
impact on low income and underprepared students, and cost issues. 
E-learning has grown at a considerable rate, and current projections show no slow-
down in the near future.  The National Center for Education Statistics estimates that 
between 2000 and 2008 the share of undergraduates enrolled in at least one online 
course grew from 8 percent to 20 percent (Radford 2011).  The Babson Survey Re-
search Group estimated that by the fall of 2010, 31 percent of all higher education stu-
dents were taking at least one online course (Allen & Seaman 2011). Further, they esti-
mated that between 2002 and 2010 online enrollments grew at a rate of 18.3 percent, 
compared with just over 2 percent for the overall postsecondary education student 
body. 
This study refers to e-learning as all forms of electronically-supported instruction, 
which range from videotaped lectures posted on the Internet; learning management 
systems such as Blackboard that distribute lecture notes, syllabi, and assignments;  
 
 
Key Findings  
 
 
 
E-LEARNING WORKS—EXACTLY HOW 
WELL DEPENDS ON ITS UNIQUE FEATURES 
AND BARRIERS         
CAHRS 
ResearchLink  
No. 1 
January 2013  
Topic: How Effective is E-Learning Relative to Traditional 
Learning? 
  
 
 
 
 
Page 2 E-LEARNING WORKS—EXACTLY HOW WELL DEPENDS ON ITS UNIQUE FEATURES AND BARRIERS         
 
 
 
193 Ives Hall 
Ithaca, NY 14853 
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cahrs@cornell.edu Month 
2012  
CAHRS 
ResearchLink  
No. XX  
Month 2012  
The Study Questions 
 How does the effectiveness of e-learning compare to more traditional forms 
of instruction? 
 What features of e-learning influence its effectiveness? 
 What barriers to e-learning exist? 
Two opposing perspectives among researchers regarding the effectiveness of e-
learning:  Richard Clark (2009) sees technology as a tool to be manipulated at the 
hands of instructional design, pedagogical approaches, and teacher practices, and 
believes it should have no impact on the learning success of the student.  Other 
researchers say that by being able to customize instruction to the learner and 
increase interactivity, e-learning can lead to better educational outcomes (Bailey 
et al 1994).  A majority of the general public, however, believe that online cours-
es offer less educational value than traditional classroom courses (Taylor et al 
2011). 
This study reviews four meta-analyses: one by Bernard and his colleagues (2004), 
two by Sitzmann and her colleagues (2006 and 2011), and one by Means of the 
U.S. Department of Education, Office of Planning, Evaluation, and Policy Develop-
ment (2010). 
 Bernard’s analysis examined 232 studies from 1985 to 2002 
online collaborative simulations; and interactive systems that use artificial intelli-
gence to deliver customized instruction (Bacow et al. 2012). 
This study reviews four meta-analyses that have been conducted on e-learning 
research.  By combining the results of multiple studies, researchers can find an 
overall estimate of a particular relationship.  Although the methodology of the 
studies included varies significantly, these meta-analyses provide the most com-
prehensive assessment of the effectiveness of e-learning relative to other delivery 
media. 
General Views of E-Learning Effectiveness  
Data Source  
Page 3 
 
 
 
193 Ives Hall 
Ithaca, NY 14853 
607-255-9358 
www.ilr.cornell.edu/cahrs 
cahrs@cornell.edu 
Month 2012  
CAHRS 
ResearchLink  
No. XX  
Month 2012  
 Sitzmann’s first analysis examined 96 published and unpublished studies 
 Sitzmann’s second analysis examined 55 reports 
 Means’ analysis examined 45 studies 
These meta-analyses show that e-learning is as effective as traditional classroom 
techniques. 
The question is not ‘does e-learning work,’ because it does.  The question is what 
features influence the effectiveness of e-learning? Kozlowski and Bell (2007) identi-
fied four categories: content (level of richness, as exemplified by a multimedia 
presentation), immersion (level of realism), interactivity (between learners, between 
learners and instructors, and between learners and simulated characters), and com-
munication (different communication channels as well as allowing learners to com-
municate synchronously in real time).  Research into these influences on e-learning 
will provide insights that will help curriculum planners choose which types of  
e-learning are appropriate for which courses. 
Possible barriers to effective e-learning include academic integrity (tests not per-
formed in a traditional classroom provide ample opportunities for cheating);  ‘digital 
divides’ which impact access to high-speed technology, as well as using it only for 
entertainment; academically underprepared students who may lack in such self-
regulatory skills as self-monitoring and self-evaluation; and the cost effectiveness of 
not only developing the content, but providing technology and support over the 
continuing use of the courses. 
 E-learning can be just as effective as traditional methods of learning, and in some 
situations, may be better.  It must be designed to meet the learning objectives. 
 Additional research into the features that may influence e-learning is needed.  
These areas include: content, immersion, interactivity, and communication. 
 Underprepared learners may struggle to flourish with an e-learning approach, 
and the cost of implementing and maintaining a high-end e-learning experience 
may be prohibitive. 
Specific Features That Influence E-Learning  
Specific Barriers That Influence E-Learning  
Takeaway 
Page 4 
 
 
 
193 Ives Hall 
Ithaca, NY 14853 
607-255-9358 
www.ilr.cornell.edu/cahrs 
cahrs@cornell.edu Month 
2012  
CAHRS 
ResearchLink  
No. XX  
Month 2012  Researchers  
This study was conducted by:  
 
 
 
 
 
 
 
 
 
 
Allen, E. and J. Seaman. Going the Distance: Online Education in the United 
 States,  2011, Report prepared for the Babson Survey Research Group 
 (Wellesley, Mass.: Babson Survey Research Group, November 2011). 
 
Bacow, L. S. and others. Barriers to Adoption of Online Learning Systems in 
 U.S. Higher Education, Report prepared for Ithaka S+R (New York: 
 Ithaka S+R, May 2012). 
 
Bailey, E. K. and M. Cotlar. “Teaching Via the Internet,” Communication Edu-
 cation 43 (1994): 184–93; Starr Roxanne Hiltz and Barry Wellman, 
 “Asynchronous Learning Networks as a Virtual Classroom,”  
 Communications of the ACM 40 (1997): 44–49; Robert B. Koz “Will 
 Media Influence Learning? Reframing the Debate,” Educational  
 Technology Research and Development 42, no. 2 (1994): 7–19; Shu-
 Sheng Liaw, “Designing the Hypermedia-Based Learning  
 Environment,” International Journal of Instructional Media 28 (2001):  
 43–46;  Patrick Sullivan, “Gender Differences and the Online Class
 room:  Male and Female College Students Evaluate their Experience
 es,” Community College Journal of Research and Practice 25 (2001): 
 805–18.  
 
Bradford Bell,  
Associate Professor,  
Human Resource  
Studies,  
ILR School,  
Cornell University 
References  
E-LEARNING WORKS—EXACTLY HOW WELL DEPENDS ON ITS UNIQUE FEATURES AND BARRIERS         
Jessica E.  
Federman 
Ph.D. student  
in Human Resource 
Studies  
ILR School,  
Cornell University 
 Bernard, R.M. and others. “How Does Distance Education Compare  
 with Classroom Instruction? A Meta-Analysis of the Empirical Literature,” 
 Review of Educational Research 74 (2004): 379–80. 
 
Clark, R. E. and others. “An Analysis of the Failure of Electronic Media and Discov-
 ery-Based Learning: Evidence for the Performance Benefits of Guided 
 Training Methods,” in Handbook of Training and Improving Workplace Perfor
 mance, volume 1: Instructional Design and Training Delivery, edited by Ken
 neth H. Silber and Wellesley R. Foshay (New York: John Wiley and Sons, 
 2009), p. 264. 
 
Kozlowski, S.W.J. and B.S. Bell. “A Theory-Based Approach for Designing Distrib-
 uted Learning Systems,” in Toward a Science of Distributed Learning, edited 
 by Stephen M. Fiore and Eduardo Salas (Washington: APA, 2007), pp. 15–-
 39 
 
Means, B. and others. Evaluation of Evidence-Based Practices in Online Learning: A Meta
 Analysis and Review of Online Learning Studies, report prepared for the U.S. 
 Department of Education, Office of Planning, Evaluation, and Policy Devel-
 opment (Washington: U.S. Department of Education, September 2010). 
 
Radford, A.W. Learning at a Distance: Undergraduate Enrollment in Distance Education 
 Courses and Degree Programs, Report prepared for National Center for Ed-
 ucation Statistics (Washington: U.S. Department of Education, October 
 2011). 
 
Sitzmann, T. and others. “The Comparative Effectiveness of Web-Based and Class
 room Instruction: A Meta-Analysis,” Personnel Psychology 59 (2006): 623–-
 64.  
 
Sitzmann, T. “A Meta-Analytic Examination of the Instructional Effectiveness of 
 Computer-Based Simulation Games,” Personnel Psychology 64 (2011): 489–
 528. 
 
Taylor, P. and others. The Digital Revolution and Higher Education: College Presidents, 
 Public Differ on Value of Online Learning, Report prepared for Pew Social & 
 Demographic Trends (Washington: Pew Research Center, August 2011). 
 
 
 
 
The Center for Advanced 
Human Resource Studies 
(CAHRS) is an international 
center serving corporate 
human resources leaders and 
their companies by providing 
critical tools for building and 
leading high performing HR 
organizations. CAHRS’ mis-
sion is to bring together part-
ners and the ILR School’s 
world-renowned HR Studies 
faculty to investigate, trans-
late and apply the latest HR 
research into practice excel-
lence.  
 
 
 
193 Ives Hall 
Ithaca, NY 14853 
607-255-9358 
www.ilr.cornell.edu/cahrs 
cahrs@cornell.edu  
  
CAHRS 
ResearchLink  
No. 1 
January 2013  
 
 
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