Different Generations of eLearners

INTRODUCTION

I am a Gen X (born in the mid-seventies). About 98% of students I teach are a mix of Millennials and Generation X students. The generational cohort is said to significantly influence one’s developmental years, shaping the way an individual interacts and experience the world (Holyoke & Larson, 2009). Thus, identifying the era a person was born to, can help educators understand major influences that may shape one’s disposition towards learning. Assigned readings for this module highlighted characteristics typically identified amongst Millennial and Generation Z students, and how emerging technologies have influenced their world view. For the purpose of this journal entry, I would like to focus on the question: assumptions made about learners (that belong to a generation) and their learning needs: in order to reflect on the four guided questions.  In addressing the above question, I will be cautioning the readers of potential dangers in applying these generational characteristics monolithically, to all students that belong to a cohort. I intend to offer an alternative view point as to how online educators should identify student characteristics in designing and delivering effective online courses.

WORKING WITH DIFFERENT GENERATIONS: LEARNING/RESEARCH

The assigned readings for this module highlighted how technological developments over the years have changed the way current students interact with each other and learn.  The video content presented by Wesch (2008) and Heppell (n.d.) made a compelling case to support this.  Other readings summarized generational characteristics mainly amongst millennials and generation Z. I was first exposed to research on generational differences of learners about six years ago, as I was working on my doctoral research. In general, I was interested to understand how differences in student characteristics (generational, gender, ethnicity, nationality, cultural) could potentially predict their online discussion behaviours. Thus, the content in the assigned articles were not new, but brought back memories.

In setting the stage, I do recognize the existence of generational differences (as much as gender, cultural differences etc.) and the usefulness in understanding those differences to distinguish one group from another. Oblinger and Oblinger (2005), Windham (2005), Erickson (2008), Price (2009) provided useful insights as to how millennial students like to interact with each other, their preferences in using short bursts of text messages, their fondness for asynchronistic activities, their willingness to apply and learn in a more relaxed mode, the need to convince merits of learning to them etc. In a separate study, Holyoke & Larson, (2009) provided evidence as to how Gen X students were ready to learn with very little convincing, showed preferences for synchronous learning, displayed a tendency to make a personal connection with new materials and their inclination to hinge towards instructors to orient themselves with learning. Holyoke & Larson (2009), further compared the millennials and Gen X students and concluded these two groups differed significantly in terms of their readiness, orientation and motivation to learn. There is a plethora of similar studies that can be found in literature. However, I do think it is problematic when online educators use findings pertaining to a collective group, to refer to individual student behaviours in an online environment. There is a danger to assume all students that belong to a generational cohort will demonstrate the same or similar online behaviours without understanding why. Let me start with Prensky’s (2001) argument on digital natives and the digital immigrant conceptualization. Reading Prensky’s argument (all parts) at a first glance made a lot of sense. It seemed so intuitive to recognize that students who were exposed to technology throughout their developmental years, may be wired differently than those who were trying to learn how to use technology later in life. When I explored this topic further, I found an overabundance of empirical studies (Bennett, Maton & Kervin, 2008; Guo, Dobson and Petrina, 2008; Bennett & Maton, 2010) that found evidence contrary to those conceptual claims made by Prensky. For example, Guo et al. (2008) did not find any statistically significant differences between the two generations of students’ level of technology literacy. In 2008, for my master’s program, I compared technological competencies between millennials and late boomers. To my surprise, there were no statistically significant differences of competencies between these two groups.   Bennett et al., (2008) reported the technological competencies millennial students appear to possess were not as widespread as claimed and they were present in only a small percentage of digital natives sampled. They also found those technological competencies that defined digital natives to be limited to a few skills that did not necessarily translate well into educational technology.  Further, Bennett and Maton (2010) found the use of technology amongst students to be subtle and complex and not so straight forward as claimed by these broad dichotomous labels.

When I looked at studies that researched gender, ethnicity, nationality based differences of students, a similar sentiment was noted. Many researchers documented demographic characteristics in terms of generational, gender, ethnicity, race, nationality based differences of students at group level, and applied them monolithically to explain how individual students (who were part of those groups) behave in online environments. For example, there are many studies (Rovai & Baker, 2005; Warden, Chen & Caskey, 2005) that concluded male vs. female students, Chinese vs. Canadian students, Millennial vs. Generation X students behave differently in online environments, suggesting a change to online interventions accordingly. Conclusions from these studies tend to assume all students that belong to a given generation (only focusing on generations here due to the topic of this journal entry) to be monolithic, i.e. identical to all those reported by the category at large. These general claims often do not consider the complex and nuanced behavioural differences that exist within individuals across generational categories. These group based characteristics are not causally proximate to individual behaviours, due to complexities that exist at the individual level. Thus, designing online interventions purely based on group characteristics (succumbing to cross level fallacies) is problematic, since it may mislead educators to design online interventions and later find out major variations in behaviour amongst individuals.  A better alternative therefore, would be to identify differences in student characteristics such as technological competencies, language competencies, readiness to engage in self-learning, motivation to learn, mental maturity, level of conscientiousness, organization skills etc. (that could transcend across demographic characteristics such as generations) and design online interventions to reinforce/support deficiencies of these skills, competencies and values.

INTERACTING AND LEARNING WITH DIFFERENT GENERATIONS: MY EXPERIENCE

In this section, I would like to reflect on differences between generations based on my teaching experience. I have a 70:30 ratio mix between university aged (Mostly millennials) and matured (Generation X and some late boomer) learners. While reading the assigned material for this module, I was trying to reflect on my experiences dealing with these student groups. I concur with Erickson’s (2008) comments about university aged students being more comfortable using social media, liking more asynchronous activities than Gen X students. But I have noted students’ ability to use Moodle (LMS) for learning depended on their competency and readiness to use technology for learning. Both groups who did not have prior experiences and competencies in using educational technologies, often struggled using Moodle. Price (2009) reported that millennials like to interact with each other. While this was generally true, my experiences suggest that it mostly depended on their personality type and language competencies. I found native English speakers (both university aged and mature) enjoying and performing well in interactive assignments. Also, students with extroverted personalities loved more face to face contact. I find students who were less confident with English (Millennials in particular) and more introverted in nature were hesitant to engage with others. However, they did well in modules (such as quizzes, cross word puzzles, true/false answers) that did not tax their written or verbal language competencies. I document personality characteristics of students using the big five personality test every semester. I found students who scored high on conscientiousness among university aged and matured students to perform best in my courses. Previous studies (Schniederjans & Kim, 2005; Verela et al., 2012) also confirmed this. Contrary to general findings that report students with high levels of neuroticism not doing well in online courses (Nussbaum & Bendixen, 2004), I find this not to be the case with mature students. Their levels of maturity and life experiences seem to help them manage the stress and anxiety most neurotic students encounter. When I look at these two generations as a group, I do see some of the common characteristics that is often reported in literature. But when trying to apply these characteristics to individual students (which is the unit of measure of educational success at KPU), my experiences do not reconcile with some of the commonly cited generational differences.

INSIGHTS GAINED/THINKING CHANGED

Since I was familiar with the content stipulated in the assigned readings for the current module, there were no significant “aha” moments. But the research work and the results obtained over the last several years did change my thinking significantly. By profession, I am a marketer teaching business courses. In marketing, we use various methods to segment our customers to understand them better. Commonly used variables for this purpose are, demographic (age, gender, ethnicity, religion etc.), geographic, psychographic and behavioural criteria. There are many documented cases as to how demographic segmentation has led to stereotypes in profiling customers, and totally misreading their behaviours.  In fact, the current political rhetoric that is experienced in the United States is a classic example of this monolithic misrepresentation, assuming all individuals that belong to a religious or ethnic group would behave similarly to those behaviours noted by a few belonging to that group at large.

I would like to emphasize that I am not trying to discount the value of understanding generational cohort based differences at the group level. There are genuine differences between generations at the collective level. Referring to such differences is useful in certain situations. However, my argument rests on the problematic nature when online discussion researchers’ cross levels and make assumptions about individual behaviours based on data obtained at the collective level.

New insights gained from this work has led me to consider alternative methods to research student characteristics to understand behavioural dispositions, that could be applied across demographic profiles. Now I tend to primarily focus on skills, competencies, attitudinal, motivational, readiness based attributes to differentiate student behaviours and use generational, (and gender, ethnicity, nationality) differences as a secondary source to fill in the gaps.   The advancement of technology/learning analytics has now afforded us information about individual student performances. I tend to gravitate towards using such data to develop individualized learning modules that can help enhance learning.

APPLICATION OF NEW LEARNING/INSIGHTS DESIGNING ONLINE COURSES

In the earlier sections, I acknowledged the importance of considering generational differences of students at the collective level. I was trying to caution educators of the potential dangers of over relying on assumptions about student learning that exist at the collective level and applying them to design technological interventions. The following are some examples of ways I use insights gained from my learning/research to design my online courses.

I get students to assess their personality types at the beginning of the semester. Past studies have reported highly conscientious students to organize their time, material and complete educational modules in a thorough and a systematic manner. I discuss best practices emulated by these students and caution others who score less on conscientiousness, to watch out for potential pitfalls.

In order to help students challenged with technology (both university ages and matured), I include short courses on how to use Moodle, technology tools, instructions to use other technological platforms (such as Mahara portfolios, McGraw-Hill Connect and Practice modules) that I currently use in my courses to help students navigate the learning curve. I also have an F2F orientation session for my online courses, giving students face time with me to ask questions. During the orientation session, I demonstrate the use of technology resources that are required to complete the course successfully.

To help students who are less motivated to try, or who lack the initiative (due to no experience with technology/maturity), I include many practice assignments, quizzes that students could complete in order to understand how the system work and award bonus marks to motivate them to earn a grade while trying. I use images for each module to make it pleasing to the student eye and motivate them to navigate section by section. I have not used videos in my courses before. This is something that I intend to use significantly, since millennial students are reported to use multimedia for learning. I am hoping to use a simulation game this summer to teach marketing principles, given the inclination millennials show towards games to learn. In order to accommodate students who may not like the idea of playing a game (Gen X or matured students), a written report component is attached to this assignment allowing them to contribute more towards the generation of the report.

Recognizing many students work given their social-economic status, I use multiple modes for them to contact me. I find undergraduate students tend to contact me more frequently than graduate students. I use e-mail, face to face office hours, allow students to call me during specified times, to help them navigate their work-education life commitments. I have not used skype or other apps that allow face time, and am hoping to use them in future.

The entrance requirements set at KPU for many business courses is a modest GPA, since our inclusive policy to provide post-secondary education to citizens in geographical regions served by our institution.  We have many local and foreign students who identify English as their second language. While I still hold students to a higher standard of language competency, I set up multiple types of assessments (quizzes, online discussions, written submissions, oral submissions, in class participation activities) giving students with multiple language competencies (both excellent and marginal) opportunities to demonstrate their knowledge.

In order to accommodate students who, like to interact face to face, orally (mostly Gen X as highlighted earlier), I provide students opportunities for in class presentations on one designated day during the semester. I find those students who are less confident to present content orally (mostly university aged students from high school, or international students), tend to use the option to post comments online. Considering some students like to engage in group work, I set up online discussion forums. I find some matured students like to complete their assignment individually than in groups. Thus, I include an individual and a group component in the grading scheme allowing to compensate loss of grade in one mode over the other.

CONCLUSION

Given my own experiences and what I found from my own research, I am hesitant to use demographic labels such as generational differences, to understand characteristics of students. As I have demonstrated above, I find using other competency and motivational based characteristics as better measures that represent different propensities and dispositions to learn. I strongly believe, educators should not overly rely on assumptions pertaining to characteristics of a particular cohort (generation or otherwise) in designing online courses. Instead we should consider measures that cast a wide net, that is capable of transcending across demographic categorizations.

REFERENCES

Barnes, K., Marateo, R. C., & Ferris, S. P. (2007). Teaching and learning with the net generation. Innovate: Journal of Online Education3(4), 1.

Bennett, S., Maton, K. & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775-786.

Bennett, S., & Maton, K. (2010). Beyond the ‘digital natives’ debate: Towards a more nuanced understanding of students’ technology experiences. Journal of Computer Assisted Learning, 26(5), 321-331.

Erickson., T. (2008). Managing generation Y. Retrieved from https://www.youtube.com/watch?v=rDAdaaupMno

Guo, R. X., Dobson, T., & Petrina, S. (2008). Digital natives, digital immigrants: An analysis of age and ICT competency in teacher education. Journal of Educational Computing Research, 38(3), 235-254.

Heppell., S. (n.d). ICT and learning Retrieved from http://moodle.vcc.ca/mod/page/view.php?id=319302&inpopup=1

Holyoke, L., & Larson, E. (2009). Engaging the adult learner generational mix. Journal of Adult Education38(1), 12.

Nussbaum, E. M., & Bendixen, L. M. (2005). Approaching and avoiding arguments: The role of epistemological beliefs, need for cognition, and extraverted personality traits. Contemporary Educational Psychology, 28(4), 573-595.

Oblinger, D., Oblinger, J. L., & Lippincott, J. K. (2005). Educating the net generation. Boulder, Colo.: EDUCAUSE, c2005. (various pagings): illustrations.

Prensky, M. (2001). Digital natives, digital immigrants part 1. On the horizon9(5), 1-6.

Price, C. (2010). Why don’t my students think I’m groovy? The new “R” s for engaging millennial learners. Essays from e-xcellence in teaching9, 29-34.

Rovai, A. P., & Baker, J. D. (2005). Gender differences in online learning.Quarterly Review of Distance Education, 6(1), 31-44.

Schniederjans, M. J., & Kim, E. B. (2005). Relationship of Student Undergraduate Achievement and Personality Characteristics in a Total Web‐Based Environment: An Empirical Study. Decision Sciences Journal of Innovative Education, 3(2), 205-221.

Varela, O. E., Cater III, J. J., & Michel, N. (2012). Online learning in management education: an empirical study of the role of personality traits. Journal of Computing in Higher Education,24(3), 209-225.

Warden, C. A., Chen, J. F., & Caskey, D. (2005). Culturally related values and communication online: Chinese and Southeast Asian students in a Taiwan international MBA class. Bus. Comm. Qu, 68(2), 222-232.

Wesch., M. (2007). A vision of students today. Retrieved from https://www.youtube.com/watch?v=dGCJ46vyR9o

Windham, C. (2005). The student’s perspective. Boulder, Colo.: EDUCAUSE, c2005. (various pagings): illustrations.

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