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Radical awakenings. A new teaching paradigm using social media.


Clarissa Sorensen-Unruh.

10/30/16 to 11/01/16

This paper focuses on one instructor’s integration of social media into her classroom and her teaching life as both a communication tool, mainly between professor and student, and also a way to build community amongst the students. Multivariate statistical analyses were implemented to determine the relative success of the integration as compared to the baseline of an active learning model. Future directions include qualitative analyses and further quantitative analyses on questions raised by the initial findings.


Unmotivated or under motivated students are common at all levels of education, but are more prevalent at the community college level (McFarlane, 2010).  Student motivation impacts student grades (Maurer, 2013) and persistence in STEM careers.  Research has shown that faculty members can impact student motivation through classroom activities and assignments (Maurer, 2013).  Samson (2015) found that activities that promote critical thinking skills increase student engagement.

Bransford, Brown, and Cocking (1999) discuss the importance of active learning within the classroom, and found that students who actively engaged with course material learned more effectively.  Extensive research demonstrates the value of active learning for STEM disciplines (Freeman et al., 2014).  Many students leave STEM fields because of lack of engagement (Chang et al., 2012), and therefore, incorporating more active learning into STEM fields can increase student learning and retention in STEM disciplines.

These findings, coupled with my own reflective practice, lead me to employ an active learning pedagogy within my classroom starting in 2009.  Beginning in 2012, I employed an active learning model that looked like the following (based on The Learning Strategies Triangle developed by Gary A. Smith at UNM):

The underlying theme within this model is that the pinnacle of learning should be inside the classroom, with student preparation before class and synthesis after class.

As CNM is the largest higher education institution in New Mexico with an enrollment of 29,000, of which 46.3% of students are Hispanic and 7.1% American Indian or Alaskan Natives (National Center for Educational Statistics, 2015), I believed active learning pedagogies could help my students tremendously. However, the relationships built within the classroom were not enough, particularly because I was often teaching hybrid students who only physically met together (face-to-face or F2F) once a week.

Osterman (2000) argued that both community and relationships among peers increase commitment and engagement in individual student learning, and are thus essential building blocks for any learning space. Selwyn (2010) developed this line of thinking further, arguing the use of social media can not only help build a better learning community for students both inside and outside the classroom but also democratize the digital and face-to-face learning environments further. Dunlap and Lowenthal (2009) contend that students’ use of social media can benefit from modeling and can extend far beyond the classroom, enabling the building of digital citizens. These ideas - using social media within classroom environments and developing students as digital citizens - couldn’t be more timely.  As of April 2015, approximately 74% of all online adults used at least one social media site (Wormald), and 71% of teens (aged 13-17) used more than one social network site (Lenhart, 2015). While 79% of AAAS scientists believe it is a major problem for science communication that news reports don’t distinguish between scientific findings that are well-founded vs. those that are not well-founded, only 47% of those same scientists use social media to talk about science or read about scientific developments (Rainie, Funk & Anderson, 2015).  Four in ten Americans say they regularly get news updates from social media (American Press Institute, 2015) and that number is only expected to increase (Mitchell & Holcomb, 2015).

Therefore, I decided to incorporate a social media component into my classroom pedagogy to enhance the active learning model.  The integrated model looked like the following:

At this point, one might ask what was gained in the Integrated Model? As was found in Selwyn (2008), I found that the community aspect of my class increased because I implemented the digital facets of the classroom community using a social media platform (mostly Facebook in closed groups) my students already understood, and I found that my students increased their academic self-efficacy skills as a result of the multimodal communal aspects of learning.



The educational research on the usage of social media within learning and professional environments remains in its beginning stages, and currently a dearth of empirical articles exists on the use of social media within these environments (Conole and Alevizou, 2010; Tess, 2013).  To that end, after obtaining the appropriate IRB approvals, I also performed a series of statistical analyses centered around the use of social media to communicate chemistry within a classroom setting. The pedagogical goals of the research project included building a digital learning community for students and aiding in their development as digital citizens.  The project asked two main research questions to explore the use of social media to communicate chemistry:

  1. How does the presence of class-based social media affect persistence and impact students’ course grades in their beginning chemistry courses?
  2. How does the presence of class-based social media in beginning chemistry courses affect persistence and impact students’ course grades in next level coursework?


Results and Discussion

                The questions were explored using both mainly quantitative methodologies, including multivariate regression analysis in the beginning chemistry course and then paired t-tests between the beginning chemistry courses and the next level course. The variables used for the statistical analysis can be found in the table below:







Final Course Grade

Students grades for the course

Numerical: Continuous



Final Exam Grade

Grades on comprehensive, entirely multiple choice final

Numerical: Continuous



Average Exam Grade

Average of 4 midterm exams

Numerical: Continuous



Learning Catalytics

Average from in-class exercises (80% given for participation)

Numerical: Continuous



Mastering Chemistry

Average for online homework

Numerical: Continuous



Muddy Points

Experience sampling survey combined with metacognitive reflections on student’s progress through the class (Muddy Point)


0, 100


Social Media Involvement

H (or 3) reflects high use of social media (>20 postings)

M (or 2) reflects medium use of social media (10-20 postings)

L (or 1) reflects minimal or non-use of social media (<10 postings)

Can be either:

categorical or



H, M, L

1, 2, 3



Binary gender (M = Male; F = female) was taken from survey results. No third category (trans or other) was checked & therefore not used.


M, F


                The multivariate scatter plots with correlation values (top) and colored according to social media usage (high = Pink; Medium = Blue; Low = Green bottom) are below:

From these plots, we can see that the highest correlation values exist between Exams and MC when regressed against FinalExam. The FinalExam variable was chosen as the dependent variable in the multivariate regression as the final exams used in CNM’s general chemistry classes have several features of interest. The final exams are independent grades (unlike FinalCourse grades, in which every other variable contributes some percentage) and originated as the first and second semester general chemistry exams from the ACS Exam Institute, and then, when time was no longer available for the longer exams, were modeled on the same exams. The analyzed semesters used the final exams based on the ACS finals, not the ACS finals themselves. As the ACS Exam Institute exams have been externally validated and are entirely multiple choice, so strict reliability measures are less obligatory, the final exam was deemed to be at least a reasonable measure of whether students learned the important chemistry concepts during the semester. Thus, the full regression model used was:

with each variable contributing positively to the final exam grade except for the learning catalytics (LC) variable. Why the LC variable contributed negatively is certainly cause for future analysis, but from viewing the student grades for LC, I believe the fact that many students did not regularly complete the learning catalytics assignments is the biggest reason this variable had a negative impact on their final exam grades.

                From the colored multivariate scatter plot, we can also see that there is a positive correlation between social media usage and active learning. This relationship has been confirmed repeatedly by observation throughout the time I have been offering social media as an extension within my active learning classes, ranging from Introductory Chemistry to Organic Chemistry.

Checking the assumptions of this model from the plots above, we can see, in summary, the following:

  • From the qq plot, we can see the normality of this model looks good.  The histogram (not included) as well as the Shapiro-Wilk test (p-value = 0.7261) confirm normality.
  • From the residuals vs. fitted plot, we can see the assumption of constant variance is confirmed.  The Breusch-Pagan test (p-value = 0.6984) confirms constant variance as well.
  • No significant x or y outliers and no real influential points (Cook’s Distance vs. Leverage) are shown, so no data points were excluded.
  • From the residuals vs. order (not shown), we can see the independence of model is confirmed as well.
  • The full model was reduced, and the equivalence of the reduced model was analyzed (via ANOVA Type 3 test).  The reduced model was significantly different than the full model, and thus not equivalent, so the full model was kept.

Essentially, the full model meets all of the assumptions and is the best model for this data.


A mosaic plot was made to compare gender differences in terms of social media usage. From the Pearson residuals, we can see that the only statistically significant relationship is between females (F) and high social media usage (H) (p-value: 0.001229). Therefore, we can say that there is definitely a positive correlation between women and their use of social media within this project and furthermore, we can say that women use classroom social media at higher rates, at least within this project. However, qualitatively we have no information as to why women use social media more often.

Bar graphs were used to compare the integrated model with previous classes using the active learning model exclusively (i.e. reference). On the far right, the next level class (General Chemistry II) was analyzed. While pairwise t-tests for the pass rate (green) and retention rate (purple) for the semesters in which the integrated model was used (Gen Chem I: Fall 2015 and Spring 2016) showed no significant differences (at alpha = 0.10) from the reference (Gen Chem I: Spring 2015), there is evidence via a pairwise t-test between Gen Chem I Fall 2015 and Gen Chem II: Spring 2016 (p-value = 0.12) that the next course pass rate and retention rate might improve dramatically and in a statistically significant way (especially as we increase our n value).


                Within the context of this project, there was definitely a positive relationship between social media integration and final exam grades, in that the more students posted on social media, the higher their final exam grades were.  However, the extent of this relationship remains to be seen as it is neither a particularly strong correlation nor a deep one - the relative quality of student postings has yet to be analyzed. Obviously, to simply know the number of social media posts is far different than knowing their content, and so analysis on quality of social media posts using qualitative methods is definitely warranted. Women tend to post to classroom social media at higher rates, but again, qualitatively we have no information as to why women use social media more often, and are thus left with more questions to explore than answers.

However, the next level course results show great promise: the general chemistry II blended spring 2016 class that had begun their studies in the general chemistry I hybrid fall 2015 had a 76% pass rate and 85% retention/persistence rate (including 4 audits). While these results do not significantly differ from the active learning model in the next level class (76% pass rate and 79% retention/persistence rate), the remarkable thing here is that the active learning model was successful when offered in the same class format (i.e. hybrid) and the integrated model was successful when offered in different class formats, where the F2F (face-to-face) component was essentially eliminated in the next level coursework.  The students in the integrated model next level class also independently set up their own GoogleDrive and GroupMe accounts to further their communication and community. Comparison of different types of classes (F2F, hybrid, blended) and levels (introductory chemistry, general chemistry I and II, or organic chemistry I and II) is still needed to determine if this is a trend or just a one-time occurrence.

The pedagogical use of social media to build community and enhance communication between students tends to “mirror much of what we know to be good models of learning, in that they are collaborative and encourage an active participatory role for users” (Maloney, 2007: B26). I found my experience with social media integration to be positive, affirmative and a success. Yet much work remains to be done. Social media integration does not work the same for every class, especially when not required as a graded component. Framing by the instructor when “selling” the idea of social media integration to students seems to be a defining factor according to the literature (Cole, 2009; Minocha et al., 2009), and has not been adequately addressed within the context of this study.

I’d also like to collect more surveys detailing how students feel about the social media integration, particularly as a reflection of the semester and possibly several semesters. Interviews and/or focus groups could also be conducted to gather more about the individual student experience.

The one thing I did not expect when the classroom social media integration was implemented was what a professional development opportunity it provided for me personally. While there isn’t time to detail the community building or many PD interactions it’s afforded me, it was a great and unexpected benefit of this project and pedagogy.


I would like to thank the following persons/entities involved with this project: my students, who allowed me to teach them (and thus gather data on them); Emily Alden, my supplemental instructor during the time the social media extension was offered; and CNM Community College and its IRB*.



  1. Bransford, J. D., Brown, A. L., & Cocking, R. R. (1999). How people learn: Brain, mind, experience, and school. National Academy Press.
  2. Cole, M. (2009). Using Wiki technology to support student engagement: Lessons from the trenches. Computers & Education, 52(1), 141–146.
  3. Conole, G., & Alevizou, P. (2010). A literature review of the use of Web 2.0 tools in Higher Education (p. 111). The Open University, UK: Higher Education Academy. Retrieved from
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  6. Gasiewski, J. A., Eagan, M. K., Garcia, G. A., Hurtado, S., & Chang, M. J. (2012). From gatekeeping to engagement: A multicontextual, mixed method study of student academic engagement in introductory STEM courses. Research in higher education53(2), 229-261.
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  10. McFarlane, D. A. (2010). Teaching Unmotivated and Under-motivated College Students: Problems, Challenges, and Considerations.  College Quarterly, 13(3), 1-5.
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  12. National Center for Education Statistics. (2015). U.S. Department of Education. Institute of Education Sciences.
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  17. Selwyn, N. (2008). An investigation of differences in undergraduates’ academic use of the internet. Active Learning in Higher Education, 9(1), 11–22.
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Bob Belford's picture

Hi Clarissa,

Thank you for sharing your work with us.  Could you describe your classes a bit more, as I got confused.  You said you"often teach hybrid students who only physically meet F2F once a week", and I got confused.  Were some students in your class meeting once a week, while other students in the same class were meeting more than once?   Or was this a hybrid class, that meet f2f once a week?

Hi Bob! Thanks so much for your comment.

I would happy to clarify the course structure my students were involved in for this project. The General Chemistry I students took a hybrid class (my apologies for the sloppy language), which was taught 1/2 online and 1/2 in class. So they met for 1 class meeting on campus per week.

The General Chemistry II students took a blended class, which was offered entirely online with tests proctored on campus during a given timeframe. The tests are the reason why the class is not fully DL (distance learning).

No students involved in this project were taught in a traditional college learning environment, with multiple in class (or face-to-face) meetings per week.

Does this clarify your question adequately?

Thanks again!

Bob Belford's picture

Hi Clarissa,

I am very confused on your final exam.  You make the comment that:  The final exams are independent grades....and originated as the first and second semester general chemistry exams from the ACS Exam Institute, and then, when time was no longer available for the longer exams..."  What do you mean?  How much time does it take to take an ACS exam?    And then you give this formula for calculating the final exam grade.

FinalExam=-15.33 +0.27(Exams) - 0.14(LC) + 0.44(MC) + ).16(MP) + 3.52(SM)

Could you explain this in more detail. Are you taking commercial material like Learning Catalytics, Mastering Chemistry, and mixing those into the final exam grade, and if so, how do ACS exams institute final exams validate that?  Can you explain the logic of the above equation?  Why does Learning Catalytics have a negative weight, while Mastering Chemistry a positive?  Please forgive me, but you have sparked my interest as I really do not understand what the above equation does, and how it equates to a final exam grade, that is "an independent grade" and validated by ACS exams institute.  Or am I misunderstanding this?

Cheers, and thanks again for sharing your very interesting work with us.




Hi again Bob!

Thanks again for reading the paper and your comment.

So, the final exam is fashioned after the first semester general chemistry exam provided by ACS Exams Institute. We used the actual ACS Exams Institute exams (2002, 2006, 2009, 2012) for many years (probably at least 15), but when we no longer offered a "Finals Week" at CNM Community College due to the shortening of our semester length, our class time for the final exam went from 2 hours to 1 hour and 15 minutes. Thus, we could no longer offer the actual ACS Final.

(As a side note: if there had been a shorter ACS Final from the Exams Institute, we absolutely would have used it.)

We, as a department, made our own final exam that was shorter (40 questions) and that was based on the questions (but not the actual questions, which are copyrighted) from the ACS finals we had. We have done some concurrent validation of this new department final vs. the ACS standardized final and have found them to be reasonably similar.  Both finals are entirely multiple choice, so reliability measures in their grading are minimal.

Hopefully that helps with understanding what the final exam is. Now on to the model equation for the multivariate statistics.

The formula for the model doesn't show a causal relationship - i.e. I didn't calculate their final exam grade using it - but shows a correlational relationship. The numbers in the formula are the estimated beta coefficients for a multivariate regression line, which means that these numbers equal the slope of the line for the X they describe (like Exams) vs. Y (FinalExam) if all other X's were fixed. We can use the betas as an estimate of that X value's relative contribution to the Y value if all other independent variables remained constant. 

More on multivariate analysis and beta coefficients can be found here: 

I hope that helps.  Thanks again!

Tanya Gupta's picture

Hello Clarissa,

This is an interesting paper and I had the pleasure of listening to your presentation at BCCE. I would like to know that with a subject like chemistry that involves different representations macroscopic, microscopic/particulate and symbolic representations, how do you foster student interactions on social media for these aspects.  What suggestions do you have for other instructors to effectively integrate social media in their classroom? 

Hi Tanya!

Thanks first for listening to the presentation and reading the paper and second for your comment and questions.

So, for the chemistry aspect of social media, we tend to take pictures, make animations, or stream video to share reactions, equations, and all representations relevant to chemistry. It's admittedly clunky at times, but the ability to easily upload media was one of the major reasons I abandoned Bb Learn discussions and embraced social media. We are also able to link to resources, such as blogs, peer-reviewed articles, professional media outlets, updates from professional societies (ACS), etc. much more effectively via social media as well. Therefore, we can integrate the "outside" world much more effectively in the social media portal than within Bb Learn or Moodle (I haven't tried Canvas so I'm unfamiliar with its ability to integrate media).

In terms of my suggestions for other instructors, the idea of "selling" social media is very important as the literature is abundant with failures in social media integration due to a lack of proper framing. Students need to feel like while you're not necessarily forcing them to join a social media platform, it's an important aspect of classroom community building. I tend to emphasize social media's importance by only posting some supplemental (not required) material there. And I rely on my students who took me for a previous class and who can immediately see social media as a useful tool to help add peer pressure (by posting pictures of the board, funny memes (that are material relevant), or helpful websites) for those students who do not want to join the platform. Eventually, everyone tends to not only join but also see the merit of social media usage.

I also allow them to vote on which platform they would like to use at the beginning of the semester. The vote allows them to voice their opinions at the outset. I also try to address any dissension (and correct disrespectful behaviors) as quickly as possible within the social media forum. 

Does that address what you were asking thoroughly enough?

Thanks again!


Thanks for sharing this interesting paper with us.  As an prganic chemist, I'm not accustomed to thinking bout multivariate statistics.  But my understanding of thise equations like you show for explaining student performance on the final exam has me intrigued.  It looks like a principal component analysis, and the larger coefficient on the social media factor seems to indicate that a student's participation in the social media component of your course has a large contribution to their final exam grade for the course.  Am I on the right trck?  Do you have an explnation for why the social media participation would be so important?


Hi Jennifer,

Thanks for reading the paper and for your comment.

And yes, you are on the right track. From the model equation I provided, it seems that students who embraced social media tended to perform better on the Final Exam, and that social media usage had a higher correlation with their final exam grade than even their exams, for instance, did. And yet, this might be entirely due to social media as a confounding variable (either moderator or mediator) as it seems that those students who were higher achievers overall contributed more to social media than those students who were not high achievers.  Also, while my supplemental instructor during this study, Emily Alden, regularly held her sessions for the entire class regardless of their social media usage, she exclusively posted supplemental instruction materials to the social media platform. Those postings may entirely account for the social media beta coefficient (3.52) as well.

Obviously, there is still much work to be done, especially on the qualitative side, where we can determine more thoroughly what social media postings were more helpful vs. less helpful.

Does this answer your question?

Thanks again!

Layne Morsch's picture


During your conclusion you discussed a piece of data that I was looking for on the Persistence and Retention Data graph. The issue is comparing these data for a continuation course when the first semester has already eliminated a group of students. The persistence and retention should always increase, but how much? Have you looked at this data across more years of the courses? Have you looked into any methods to account for this?

I am also interested in looking at this area since I teach the year long organic chemistry sequence. 


Hi Layne!

Thanks so much for reading the paper and for your comments and questions.

So, the next level course data has not been analyzed nearly to the extent it should be.

I ran some matched pair t-tests and calculated the persistence (called retention here), for which my operational definition includes everyone who finished the class with either a grade or an audit, and the pass rate, which included those students who got an A, B, or C divided by those who were retained. In terms of statistical analysis, this is not even skimming the surface...

If I was going to analyze gen chem II (the next level course in this study) in the same way I've done the general chemistry I data, I would build the statistical model for the active learning gen chem II classes and the integrated model gen chem II classes using multivariate statistics, then run the ANOVAs to test whether the models are statistically significantly different, which I did in gen chem I as well but didn't discuss here. Then I'd take a subset of the data, specifically those students who were in both my general chemistry I and II courses in this sequence, and I would analyze not only their performance in relation to the overall class, but qualitatively, I would try to ask (and answer) some research questions just for the subset, like, "Did the quality of the student's social media posts increase over time?" and "What kinds of trends or patterns in social media postings contributed to overall class success?".

I have the data from almost 15 years of teaching. But I only integrated social media starting in Fall 2015, so the sample size (n) is still rather small for this particular study.

Does this at all answer your questions, Layne?

Thanks again!

Gregorius's picture

Hi Clarissa,

Very interesting read, thank you.

I was wondering if you were able to isolate the social media effect from the interest/motivated student effect, i.e. correlate performance results to social media alone as opposed to the performance that interested/motivated students would have had anyway without the social media.


Hi Greg!

Thanks so much for reading the paper and for your comment.

The short answer to your question is no, I haven't teased out motivation as a variable within these analyses. And while I could certainly do this quantitatively (i.e. run the mutivariate analysis with social media as the dependent variable, then look for correlations between performance on the final exam (for instance) and social media usage), I'm more interested in doing this qualitatively, with interviews and surveys built into the study methodology to really understand the motivation behind using the social media as a tool for success.  Clearly, some of my students REALLY bought into the social media as they have expanded what we used in the general chemistry I class (and future classes) to new platforms - GroupMe, GoogleDrive, and now some are on Twitter and Instagram to 1. discuss chemistry with other undergraduates (not necessarily at CNM) or with the professional community and/or 2. take pictures of lab as they perform their experiments to either help classmates in future sections or do the internet equivalent of "hanging evidence of their lab reaction on the refrigerator door".  Some students have continued to see social media integration as helpful, but not to the extent of the others. And a few really never bought into the social media integration and continued to be annoyed that I highly encourage it.  (These general categories, BTW, are not mine, but the patterns I saw from general student surveys given over the social media integration.)

Does this at all answer your question?

Thanks again!

Hi Clarisa,

I am curious about the details of the social media that you used.  For example, was it a facebook page, social media built into systems like Blackboard, etc. and how did you monitor the students using it


Josh Halpern



Hi Josh!

Thanks so much for reading the paper and for your comments and questions.

The first semester we used social media, I was VERY concerned about FERPA violations and the ethics surrounding social media usage in the classroom. I needed a social media platform that embraced these concerns, so we used the Pi App (which is now defunct but much like Slack HQ). This app allowed students to create an account specific to the class, and once the class was finished and I deleted it from the server, the students were essentially deleted from the app. The app could be accessed for free from smart devices or from web browsers. Using the app didn't require them to make an account on a public social media platform and since the only people who could see their posts were fellow students, my SI, and myself, it felt like the FERPA violations were no longer an issue.

Once I embraced the idea of building digital citizens as part of my learning objectives, public platforms seemed more usable. And my students continually voted (in a survey on the first day) to use them. So now we use Facebook Closed Groups for the most part. I monitor usage by getting onto the group to answer questions somewhere between 1-4 times a week. As my Facebook account for the closed group is a public account so my students can find me easily (ease of discovery is also why we use closed and not secret Fb groups), I keep it entirely separate from the Facebook account I maintain for personal use.

Does this answer your question adequately, Josh?

Thanks again!