Mass Customization of Learning: Necessity or Luxury?

Training and development in most organizations is based on a popular “one size fits all” phenomenon, where customized training is viewed as a threat to organization’s ROI.  The “one size fits all” idea is very well received in today’s corporate culture as it provides certainty around organizational training costs. The illusion of ‘certainty’ may serve as enough justification for organizations to resist change and maintain their existing training and development practices. Generally, once an illusion is formed, it is very difficult to break it.  This is especially the case for large multinational organizations, where organizational ego stands guard to ideas that are not fully supported by empirical findings.

In recent years, numerous researchers have attempted to break these illusions (i.e., certainty); among them are Dr. Jean M. Adams (Assistant Professor from Schulich School of Business and Associate Director of Institute for Research on Learning Technologies), Dr. Gareth Morgan (Distinguished Research Professor, Schulich School of Business), Dr. Ronald Owston (Professor at York University, Director of Institute for Research on Learning Technologies) and Rita Hanesiak (Senior Manager, Scotiabank Human Resource).  In their comprehensive report published in 2010, Dr. Adams and her colleagues examined the influence of four different blended learning models on performance outcomes of the management teams in Scotiabank (third largest bank in Canada).

One major take away from this study was that factors such as learner characteristics and uniqueness make it impossible for learners in the same program to have consistent learning outcomes.  In other words, this study was unable to find a single ‘perfect’ blended model for all learners.

Dr. Adams and her colleagues argued that the ‘mass customization’ approach, where learners would have more control in own learning experiences, would provide an opportunity for learning professionals to work alongside learners and select learning strategies (for developing specific skills) that will work best for them. This study is an important addition to the limited but growing field of research examining training outcomes and employee uniqueness in large organizations. It is important for research to continue to explore learner uniqueness and characteristics, in order to provide a more accurate picture of the factors that can influence job performance in the workplace.

Pushing the Envelope: Hot Training Trends to Look for in 2013

 

 

As the New Year is rapidly approaching, you may be interested in learning about the trends that are expected to influence training and development in 2013. The following reports will give you an overview of what to expect in the wonderful world of training and development!

On Fire in 2013 – What’s going to be hot in e-learning (Author: Craig Weiss)

Nine Trends in Sales Force Effectiveness and Learning & Development for 2013 (Author: Dario Priolo)

Seven Trends Expected to Influence Training in 2013 (Society for Human Resources Management)

5 Trends in Learning for 2013 (Author: Tellis Usher)

2013 Trends to Watch: Education Technology (Author: Navneet Johal)

Seven Trends to Influence Training in 2013 (Author: Shari Fryer)

 

 

Too Competent to Seek Help?: Organizational Values That Could Jeopardize Employee Progress

In this post I am going to make a case for why it is important to encourage ‘help-seeking’ when individuals (employee or executive) encounter challenges (during training or on the job) that they may find difficult to solve.  At first glance, you may think: “of course they should seek help when they face a challenge or problem.”

The fact of the matter is that many employees or executives do not seek help when they need it the most. Toxic ideas such as  “asking for help will alter others’ perception of me” or “asking for help will make me feel less competent than my colleagues” prevent them from seeking help from informal (e.g., friends) or formal (e.g., colleagues, project manager, etc) sources.  Since competence and Independence are among the most cherished values in today’s organizations (DePaulo & Fisher, 1980), many believe that help seeking may undermine these values (Lee, 1997).

 

In recent years, research has shown that help seeking behaviour is significantly associated with motivational attributes of Intrinsic goal orientation and self-efficacy (Ryan, Gheen, Midgley, 1998).  Ryan and his colleagues concluded that higher levels of help seeking is significantly associated with higher levels of self-efficacy and intrinsic goal orientation.  Findings revealed that individuals with lower levels of self-efficacy and intrinsic goal orientation were less likely to seek help when they need it the most (for more information on intrinsic goal orientation and self-efficacy please see my previous posts).

This study validates the previous findings and confirms that 1: help seeking is an essential component of successful performance in individuals; and 2. Highly resourceful and more successful individuals are more likely to seek help when they face a difficult problem (Karabenick & Knapp, 1991; Pintrich, 1991, 1999, 2004).

Perhaps it is time for organizations to emphasize the importance of help seeking and destigmatize incompetency and dependency from help seeking behavior.

References 

DePaulo, B., & Fisher, J.  (1980).  The cost of asking for help.  Basic and Applied Social Psychology, 7, 23-35.

Karabenick, S. A., & Knapp, J. R. (1991). Relationship of academic help-seeking to the use of learning strategies and other instrumental achievement behavior in college students. Journal of Educational Psychology, 83, 221–230.

Lee, F.  (1997).  When the going gets tough, do the tough ask for help? Help seeking and power motivation in organizations.  Organizational Behaviour and Human Decision Processes, 72, 336-363.

Ryan, A., Gheen, M., & Midgley, C. (1998). Why do some students avoid asking for help? An examination of the interplay among students’ academic efficacy, teachers’ social-emotional role, and classroom goal structure.  Journal of Educational Psychology, 90, 528–535.

Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31, 459-470.

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385-407.

Maximizing E-learning ROI: Intrinsic Goal Orientation

 

As discussed in the last post, intrinsic goal orientation is another motivational factor that tends to play a significant role in online learning environments.

Intrinsic motivation refers to one’s tendency “…to be participating in a task for reasons such as challenge, curiosity, and mastery” (Pintrich, 1990, p. 10). Intrinsic goal orientation is an important component of self-regulated learning as it is strongly associated with deep learning in students (Yukselturk & Bulut, 2007).  Intrinsic goal orientation also helps cognitive resources to reorganize knowledge, hence making it more meaningful for students (Chyung et al., 2010).  Therefore students with high intrinsic motivation are significantly more likely to be successful than students with low intrinsic motivation (Lepper et al., 2005; Lin et al., 2003; Pintrich, 2004; Yukselturk & Bulut, 2007).

In a study by Lin et al. (2003), the findings revealed that intrinsic motivation was a strong predictor of students’ final grades.  Similarly, Lepper et al. (2005) found a significant relationship between students’ intrinsic motivation and their academic performance as measured by their GPA.

Although intrinsic motivation has been studied extensively in traditional settings, very little research has been conducted on the role of intrinsic goal orientation in online learning environments. Yukselturk and Bulut (2007) investigation is among the few studies that examined the relationship between intrinsic goal orientation and students’ academic performance in online courses.  Eighty students were instructed to complete the necessary questionnaires at the end of the university course.  The findings revealed that intrinsic goal orientation was significantly associated with students’ academic performance in their course.

More recently, Radovan (2011) investigated the relationship between different dimensions of self-regulation and academic performance in online students. MSLQ was selected as the primary instrument for this study. With regards to motivation, students having high levels of intrinsic goal orientation were more likely to be academically successful than students having low levels of intrinsic goal orientation.

Some findings also suggest that intrinsic goal orientation is significantly associated with other dimensions of self-regulated learning such as participation in online environments.  For example, a recent study by Xie, Durrington, and Yen (2011) illustrated that students with high levels of intrinsic motivation were more likely to use the online resources and participate in online discussions during a 16-week college online course than students with lower levels of intrinsic goal orientation.

A self-regulatory dimension that is closely associated with intrinsic goal orientation is students’ level of help-seeking, which has been shown to play an important role in online learning environments (Kitsantas and Chow, 2007). The next post will examine the role of ‘help-seeking’ in online learning environments.

Resources

Kitsantas, A., & Chow, A. (2007). College students’ perceived threat and preference for seeking help in traditional, distributed, and distance learning environments. Computers & Education, 48, 383-395.

Lepper, M. R., Corpus, J. H., & Iyengar, S. S. (2005). Intrinsic and extrinsic motivation orientations in the classroom: Age differences and academic correlates. Journal of Educational Psychology, 97, 184–196.

Lin, Y. G., McKeachie, W. J., & Kim, Y. C. (2003). College student intrinsic and/or extrinsic motivation and learning. Learning and Individual Differences, 13, 251-258.

Pintrich, P. R., & De Groot, E. V.  (1990).  Motivational and self-regulated learning components of classroom academic performance.  Journal of Educational Psychology, 82, 33-40.

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385-407.

Radovan, M. (2011). The relation between distance students’ motivation, their use of learning strategies, and academic success. The Turkish Online Journal of Educational Technology, 10, 216-222.

Xie, K., Durrington, V., & Yen, L. L. (2011). Relationship between students’ motivation and their participation in asynchronous online discussions. Journal of Online Learning and Teaching, 7, 17-29.

Yukselturk, E. & Bulut, S. (2007). Predictors for Student Success in an Online Course. Educational Technology & Society, 10 (2), 71-83.

 

Maximizing E-learning ROI: Self-Regulated Learning and Employee Performance

Advances in online technologies in the past two decades have made it possible for many organizations to incorporate online learning into their training and development programs.  In doing so, new and existing employees were encouraged to access portion or most of their training materials online. Traditionally, the growth of online technologies is based on the expectation that technologically enhanced settings would significantly influence employee performance in workplace. However, very little is known about the motivational, cognitive, and behavioral attributes that may influence employee performance in online settings. An organization that is proficient in identifying these attributes will prosper and succeed.

When conducting a need assessment analysis, some organizations tend to overlook the influence of self-regulation on performance outcomes. Employee performance is one ‘contributing factor’ that can have a significant impact on organizational success. The performance of any organization is directly associated with the degree to which the organization helps individual employees develop their capacity (e.g., skills, knowledge, abilities, and attitudes, etc.) to complete tasks in a competent way.

Self-regulated learning is one skill that organizations cannot afford to ignore.  In fact, strengthening employee’s self-regulation would directly or indirectly improve all the other skills necessary for successful performance in the workplace (If you are not familiar with the concept of self-regulation, please see my previous posts).  The role of self-regulation becomes even more important when e-learning is part of the training curriculum. Many individuals are simply not equipped enough to exercise high degrees of self-regulation in online or blended environment.

Studies examining the role of self-regulation have consistently indicated that successful performance in work place or academia is directly associated with individual’s ability to exercise high degrees of self-regulated learning. Self-regulation is simply one of the most important predictors of success in online, face-to-face, or blended learning environments.  Yes, it is even more important than IQ.  Future posts will discuss the relationship between IQ and self-regulation in more details. This post will only focus on the manner in which self-regulated learning can influence one’s performance in the workplace.

Although there is a growing body of research on the role of self-regulation in online environments, there are very few studies that have examined the motivational and cognitive components of self-regulation in blended settings (Bernard, Lan, To, Paton, & Lai, 2009; Lynch and Dembo, 2004; & Orhan, 2007).

Researchers have consistently found that self-regulatory dimensions of self-efficacy, intrinsic goal orientation, help seeking, and time and environment management tend to play a significant role in online learning environments (Bell & Akroyd, 2006; ; Joo et al., 2000; Kitsantas & Chow, 2007; Radovan, 2011; Vaughan, 2007; Xie, Durrington, and Yen, 2011). A description of the findings for each of these domains follows.

Self-efficacy and Online Learning

Pintrich (1990) defined self-efficacy as “judgment about one’s ability to accomplish a task as well as one’s confidence in one’s skills to perform that task”.  As stated by Peterson and Arnn (2005) in Hodges (2008), the quality of performance is significantly associated with their levels of self-efficacy. Research examining the role of self efficacy in face-to-face settings has consistently found self-efficacy to be one of the most important predictors of academic success for face-to-face students (Linnenbrink & Pintrich, 2003; Pintrich, 2004; Zimmerman, 2002; Zimmerman, Bandura, & Martinez-Pons, 1992). For example, Zimmerman et al. (1992) examined the causal role of students’ self-efficacy beliefs and academic goals in self-motivated academic attainment. Using a path analysis, Zimmerman and his colleagues found self-efficacy to be a strong predictor of academic achievement and student goals. Self-efficacy accounted for a substantial 31% of the variance in predicting academic grades.

Although the concept of self-efficacy has been extensively studied in traditional learning environment, very few studies have examined the role of self-efficacy in online learning environments. Research examining the role of self-efficacy in an online environment is mixed.  For example Joo, Bong, and Choi (2002) examined the influence of academic self-efficacy on the academic performance of students enrolled in web-based portion of a science course.  Students were instructed to attend web-based instruction sessions once a week for three weeks during their traditional science courses. Students` academic performance was measured using a written and an internet based exam. Although students` self-efficacy predicted their performance on the written exam, it did not appear to influence their performance on the internet-based exam.  Bell and Akroyd (2006) found that online students` level of expectancy, which is an important component of self-efficacy (Pintrich, 2004), was a positive predictor of academic success in online learning settings.

Even though previous research has found self-efficacy to be one of the most important determinants of academic success in students, it is noteworthy to mention that self-efficacy is a situation-specific phenomenon which appears to vary from one context to the next (Hodges, 2008).  Another motivational construct that works closely with self-efficacy is intrinsic goal orientation (Pintrich, 1999, & 2004; Zimmerman 2002, 2008). The next post will examine the   role of intrinsic goal orientation in online learning environment.

* Please Note: I will be adding a separate page for all the references. 

Pintrich’s Model of Self-Regulated Learning

Unlike Zimmerman’s model, Pintrich’s (1999, 2004) model examines self-regulatory strategies using a well-known questionnaire (i.e., Motivated Strategies for Learning Questionnaire, MSLQ) that was developed by Pintrich and his colleagues (Pintrich, Smith, Garcia, & Mckeachie, 1993). MSLQ is very well received by the scholars in the field of self-regulation, and it has also been one of the most widely used measures of self-regulated learning in technologically enhanced environments. Pintrich’s model was designed to examine self-regulated learning using four phases, namely the forethought phase, monitoring phase, control phase, and reflection phase. As illustrated in the Table, each of these phases are placed under four domains of self-regulation: cognition, motivation/affect, behavior, and context.

The components of Pintrich’s model of self-regulation in phase one include goal setting,activation of prior content and metacognitive knowledge (cognitive domain), adaptation of goal orientation and efficacy judgments (motivation/affect domain), effort planning (behavioral domain), and perception of task and context (context domain). Phase two (monitoring) refers to student’s ability to make adaptive changes (when needed) while progressing towards a particular goal. As seen in phase one, phase two also includes metacognitive awareness and monitoring of cognition, affect, motivation, effort, time use, and context conditions. Phase three (control) refers to selection and adaptation of cognitive strategies for learning, thinking, managing, motivation, and affect. Similarly phase four (reflection) includes cognitive judgments, affective reaction, choice behavior, and task evaluation. As access to online education has increased, researchers have moved quickly to identify the underlying factors that may influence one’s performance in technologically enhanced settings. In my next post, I will discuss the important role of self-regulation in technologically enhanced learning environments.

A Learning Theory You Can’t Afford Not to Know: Self-Regulated Learning

Social Cognitive Models of Self-Regulated Learning

As discussed earlier, previous research has turned to social cognitive model of self-regulated learning in further understanding the manner in which behavioural, motivational, and cognitive attributes contribute to performance in online and blended learning environments. Self-regulated learning is defined as “the process by which learners personally activate and sustain cognition  affects, and behaviours that are systematically oriented toward the attainment of learning goals” (Zimmerman & Schunk, 2008, p. 2). Influenced by Bandura’s (1986) social cognitive theory, the social cognitive view of self-regulated learning characterizes self-regulated learning as a goal-oriented process and emphasizes the importance of goal orientation (Puustinen & Pulkkinen, 2001). Known for their contributions to the field of selfregulated learning, theorists Zimmerman (1989, 1998, & 2002) and Pintrich (1999, & 2004) have used the social perspective to develop their respected models of self-regulated learning.

Zimmerman’s Social Cognitive Model of Self-Regulation

Zimmerman’s (1989, 1998, 2002, & 2008) social cognitive model of self-regulation occurs across three cyclical phases, namely the forethought phase, performance phase, and self- reflection phase. As shown in Figure 1, goal setting and motivational factors such as self- efficacy (i.e., belief in one’s own ability) are major components in the forethought phase. Moreover, factors such as self-control and self-observation tend to play a major role during an implementation of behaviour (i.e., performance phase). The self-reflection phase is the final phase of Zimmerman’s model and it focuses on self-regulatory factors such as self-judgment and self-reaction.

Zimmerman’s model is a well-respected model and has been recognized as appropriate model for measuring self-regulation in face-to-face environments (Cleary & Zimmerman, 2004; Young & Ley, 2005; Zimmerman & Martinez Pons, 1986, 1998). However, Zimmerman did not quantify his measures and examined self-regulated learning from mainly a qualitative perspective (i.e., structured interviews). Alternatively, research examining the role of self-regulation in online and blended learning has primarily focused on empirical methods (i.e., quantitative research) to explore the self-regulatory processes of students. In doing so, research has relied on Pintrich’s model of self-regulated learning (1999, 2004). Pintrich’s model will be reviewed in the next post.

Providing Blended Learning as Part of Your Corporate Training Services?: A Learning Theory You Can’t Afford Not to Know

Recent advancements in e-learning technologies have inspired many corporate trainers to search for more creative ways of maximizing employee productivity and performance in their work place. As discussed in my previous posts, empirical findings from university settings have convinced many trainers that blended learning can have a significant positive impact on the company bottom line.  This is not surprising as blended learning provides a pedagogical design where characteristics of online environment (e.g., time flexibility, student autonomy, reduced in class requirement) complement the characteristics of a face-to-face environment (e.g., higher quality of interaction between students and instructor, more direct feedback from instructor, and more instructor-controlled course structure).

Although many companies have begun introducing blended learning as part of their training and development services, very little is known about the motivational, cognitive, and behavioural attributes that may contribute to employee performance and productivity in the work place. In recent years, some researchers have turned to the social cognitive model of self-regulation in order to examine the role of cognitive and motivational attributes in online and blended learning environments (Joo, Bong, & Choi, 2000; Kitsantas & Chow, 2007; Lin, McKeachie, & Kim, 2003). Self-regulation is a multi-dimensional construct that combines behavioral, motivational, and cognitive components of learning in order to understand how students attain their learning goals (Zimmerman, 1998, & 2002).

By understanding the role of self-regulation in blended learning environments, corporate trainers can focus on the factors that can contribute to employees’ performance, thus maximizing their potential in the market place. To date, four self-regulatory variables have been shown to influence one’s performance in blended learning environments:

1. Self-Efficacy
2. Intrinsic Goal Orientation
3. Time and Environment Management
4. Help Seeking

A description of the findings for each of these domains will be provided in my upcoming posts.

References

Joo, Y., Bong, M., & Choi, H.  (2000).  Self-efficacy for self-regulated learning, academic self-efficacy, and internet self-efficacy in web-based instruction.  Educational Technology Research and Development, 48, 5-17.

Kitsantas, A., & Chow, A.  (2007).  College students’ perceived threat and preference for seeking help in traditional, distributed, and distance learning environments. Computers & Education, 48, 383-395.

Lin, Y. G., McKeachie, W. J., & Kim, Y. C.  (2003).  College student intrinsic and/or extrinsic motivation and learning.  Learning and Individual Differences, 13, 251-258.

Zimmerman, B, J.  (1998).  Academic studying and the development of personal skill: A self-regulatory perspective.  Educational Psychologist, 33, 73-86.

Zimmerman, B. J.  (2002). Becoming a self-regulated learner: An overview.  Theory Into Practice, 41, 64-70.

My Experience at the 2012 Sloan Consortium Blended Learning Conference and Workshop

The 9^th annual Sloan consortium blended learning conference was held at the Hyatt Regency hotel in downtown Milwaukee last week and who’s who of online and e-learning environments researchers and consultants were gathered to discuss the latest developments in the field of e-learning and blended learning environment.  The theme for this year’s conference was “perfecting the blend” and Dr. David Wiley, the distinguished professor of instructional psychology and technology was selected as the keynote addressee for the conference.

The Sloan-c conference took place over two days and the extensive program offered papers from number of consultants and distinguished international speakers. Similar to previous years, this year’s conference was divided into number of specific sessions:

1. Workshop session
2. Information session
3. Poster presentation
4. Plenary session

The sessions were fantastic and i could not stop taking notes! I wanted to attend all the sessions but that was not possible as some of the presentations were conducted simultaneously at different parts of the hotel. I had the privilege of attending the following workshop sessions:

1. Blended Learning: The Next Steps
2. How to Successfully Evaluate Blended Learning
3. Developing Open Educational Program: Blending OER Into Curriculums

I also had the privilege of attending the following information sessions:

1. On the Fast Track, Administering a Quickly Growing Accelerated-Blended Learning Program
2. Strategic Integration for Developing and Implementing Blended Learning
3. Online and Blended Learning: Maximizing the Investment

The poster presentations were held on Monday afternoon (April 23) and i had the privilege of presenting my recent research alongside other international speakers from United States, Canada, and Lebanon. I was one of the only two Canadian presenters that participated in this year’s poster presentation session! The title of my presentation was “The Impact of Lecture Webcasts and Student Self-Regulated Learning on Academic Outcomes”.

Setting up for Poster Presentations!

Although this was not the first time i was invited to present my masters research (i also attended the 2010 Sloan-c conference) i was much more confident and relax during this year’s presentation.  I have spent many months mastering my craft and learning about the principles and theories of e-learning and blended learning environments, and this presentation was an opportunity for me to do my part as a researcher and contribute to the growing field of blended learning environment.

Overall, this year’s conference was a definite success and i had the opportunity to meet and speak with some of the notable directors, deans, and leaders from well known American and Canadian Universities.  When minds work together, amazing things can happen, and by gathering international researchers, consultants, and entrepreneurs in one place, the conference provided  ideas and inspiration for individuals  to  “perfect the blend” in their own institution.

Three Categories of Studies that Every E-Learning Professional Should Know – Part 2

As discussed in Part 1, research examining effectiveness of online and face-to-face instructions fall under three categories.  In this post, i will analyze a few empirical studies in each of the categories. In doing so, you will have a better understanding of the topics that are  frequently examined in online studies.

Category 1: No significant difference in effectiveness of online and face-to-face learning

            As shown in Table 1, Leasure, Davis, and Thievon (2000), Johnson (2002), Smeaton and Keogh (1999), and Thirunarayanan and Perez-Prad, (2001) are among the studies that found no significant difference in the learning outcomes of students in online and face-to-face environments.  At first glance, it is clear that apart from Smeaton and Keogh’s (1999) study, the rest of students in above studies were enrolled either in education (Thirunarayanan & Perez-Prad, 2001) or medical science (Johnson, 2002; Leasure et al. 2000) courses.  This is not consistent with the findings of Zhao et al. (2005) who suggested that online students enrolled in medical science courses are much more likely to perform better than their face-to-face counterparts.

As demonstrated in Table 1, not all instructors demonstrated high levels of involvement in the above studies.  For example, Johnson (2002) reported that the instructor was not responsible for writing the final exam in their study, which reflects in low levels of instructor involvement in the course. On the other hand, both Thirunarayanan and Perez-Prad (2001), and Smeaton and Keogh (1999) reported that instructors were highly involved in their courses.  This is not consistent with the findings of Zhao et al. (2005) who argued that online students would generally perform better than face-to-face students when the instructors is highly involved in the course.  The following section will critically analyze the studies that found online learning to be more effective than face-to-face learning.  In doing so, the present post will examine the key characteristics that may contribute to students’ learning outcomes in online learning environments.

Category 2: Online learning is more effective than face-to-face learning

As shown in Table 2, Campbell et al. (2002), Maki et al. (2000), Schoenfeld-Tacher et al. (2001), and Dutton et al. (2002) are among the studies that found online learning to be more effective than face-to-face learning. The critical review of the above studies indicated that apart from Dutton et al.’s (2002) study, students in the above studies were enrolled either in accounting (Campbell et al., 2002), introductory psychology (Maki et al., 2000) or Histology (Schoenfeld-Tacher et al., 2001). Again, this is not consistent with the findings of Zhao et al. (2005) since none of the above courses fall under the categories of education, medical science, or computer science courses.

With respect to instructor involvement, the analysis of the above studies also indicated that instructors were generally highly involved in the courses. This is consistent with the findings of Zhao et al. (2005) where instructor involvement was found to be a significant contributor to students’ learning outcomes in online courses.  However, one cannot overlook the fact that results from the above studies partially contradicted the findings of Zhao et al. (2005), since course content did not appear to influence the performance of online students within their courses.  The following section will critically analyze whether course content and instructor involvement contributed to the performance of online students in studies that found face-to-face learning to be more effective than online learning.

Category 3: Face-to-face learning is more effective than online learning

As shown in Table 3, Anstine and Skidmore (2005), Faux and Black-Hughes (2000), and Ferguson and Tryjankowski (2009) are among the studies that found face-to-face instruction to be more effective than online learning. The critical review of the above mentioned studies indicated that apart from Anstine and Skidmore’s (2005) study, instructors were highly involved in the courses. Again, this is not consistent with the findings of Zhao et al. (2005), since the authors reported that high instructor involvement will positively contribute to the performance of online students.

With respect to course content, the contents did not fall under business, computer science, or medical science categories, which may justify the performance of face-to-face students in the above courses. However, it is difficult to interpret the findings of the above studies since Faux and Black-Hughes (2000) and Ferguson and Tryjankowski (2009) reported high levels of instructor involvement in their courses.  Therefore course content and instructor involvement may not necessarily play an instrumental role in success of students in online settings.

Summary

The critical analysis of studies partially contradicted the findings of Zhao et al. (2005), where the authors reported that effectiveness of online learning may be significantly associated with course content and level of instructor involvement. Although Zhao et al. (2005) claimed that their meta-analysis provided a clear pattern for understanding the role of environmental characteristics within online learning environments; the present critical analysis was unable to confirm these claims in studies that were not included in Zhao et al. (2005).

Overall, to date, the results have been mixed from studies which examined the academic outcomes of students in online and face-to-face learning environments. Some studies have found no significant differences in the academic outcomes of students in online and face-to-face settings, whereas other studies have either supported or condemned the use of technology in higher education institutions.

References 

Anstine, J., & Skidmore, M.  (2005). A small sample study of traditional and online courses with sample selection adjustment.  Journal of Economic Education, Spring 2005, 107-127.

Campbell, M. C., Floyd, J., & Sheridan, J. B.  (2002).  Assessment of student performance and attitudes for courses taught online versus onsite.  The Journal of Applied Business Research, 18, 45-52.

Faux, T.  L., & Black-Hughes, C.  (2000).  A comparison of using the internet versus lectures to teach social work history.  Research on Social Work Practice, 10, 454-466.

Ferguson, J., & Tryjankowski, A. M.  (2009).  Online versus face-to-face learning: Looking at modes of instruction in Master’s-level courses.  Journal of Further and Higher Education, 33, 219-228.

Johnson, M.  (2002).  Introductory biology online:  Assessing outcomes of two student population.  Journal of College Science Teaching, 31, 312-318.

Leasure, A. R., Davis, L., & Thievon, S. L.  (2000).  Comparison of student outcomes and preferences in a traditional vs. world wide web-based baccalaureate nursing research course.  Journal of Nursing Education, 39, 149-154.

Maki, R. H., Maki, W. S., Patterson, M., & Whitaker, P. D.  (2000). Evaluation of a web-based introductory psychology course: I. learning and satisfaction in on-line versus lecture courses.  Behavior Research Methods, Instruments, & Computers, 32, 230-239.

Schoenfeld-Tacher, R., McConnell, S., & Graham, M.  (2001).  Do no harm—A comparison of the effects of on-line vs. traditional delivery media on a science course. Journal of Science Education and Technology, 10, 257-265.

Smeaton, A. F., & Keogh, G.  (1999). An analysis of the use of virtual delivery of undergraduate lectures. Computers & Education, 32, 83-94.

Thirunarayanan, M. O., & Perez-Prado, A.  (2001).  Comparing web-based and classroom-based learning: A quantitative study.  Journal of Research on Technology in Education, 34, 131-137.

Zhao, Y., Lei, J., Yan, B., & Tan, S.  (2005).  What makes the difference? A practical analysis of research on the effectiveness of distance education.  Teacher College Record, 107, 1836-1884.