While discussing the shortcomings of learning styles theory with a colleague, he said he was surprised that people are still promoting them. “I thought that nonsense was over with,” he remarked. Anyone on Twitter with a serious interest in education has likely encountered some discussion about them, be it for, against, or both. That conversation brought me to examine the issue more deeply.
To that end, I tapped into my experimental psychology expertise (that last word expressed oh so loosely) to gauge how learning styles are represented on Twitter among English speakers. Does the Twitterverse largely favor them, oppose them, or represent a collision of the irresistible force and the immovable object (which one is which I will let you, the reader, decide)?
Using the Google Sheets add-on Twitter Archiver, public tweets posted between June 5th and July 4th of 2019 relating to learning styles were stored in a spreadsheet. A tweet was considered relevant if it contained any of the following phrases: learning styles, learning style, visual learner, kinesthetic learner, tactile learner, or auditory learner. Direct retweets and replies were ignored.
The Twitter Archiver collects and reports all of a tweet’s information that a Twitter user can ascertain through regular use. The following information is listed as it is part this analysis: tweet text, date of tweet, number of times liked and retweeted, user name, date joined Twitter, number of followers, and user-provided biography.
After the spreadsheet was assembled, additional tweets from a single user were deleted so that a single account would have equal representation. In other words, if a user posted several times about the topic, all but one of that account’s tweets were deleted. Each tweet was read and categorized into one of three categories:
Favors Learning Styles: The following were considered tweets that promoted or supported learning styles, either explicitly or implicitly:
- Declared that learning styles are important (“We must teach to all learning styles…”)
- Linked to a learning styles website, article, blog, or inventory
- Reported their perceived learning, often to support a contention (“I never did well with lectures because I am a visual learner…”)
Opposes Learning Styles: The following were considered tweets that opposed the approach to learning styles:
- Explicitly mentions that learning styles are a myth
- Linked to a website, article, or blog that refutes learning styles
Uncategorized: These are tweets that did not fit either category above because
- They were in a language other than English
- They could not be identified easily as one or the other (“There seems to be evidence supporting both sides of the learning styles debate”)
- Were nonsensical or difficult to comprehend
A total of 1885 tweets were collected and assembled. Elimination of repeat account postings decreased the number to 1495 tweets. Eliminating Uncategorized tweets yielded a sample of 1342 tweets for further analysis. There were approximately 2.5 times as many tweets favoring learning styles (n = 958) compared to those opposing them (n = 384). The results of the subsequent analyses are as follows:
There was no statistically significant difference in the percentage of verified users between those supporting learning styles (2.1%) and those opposing them (2.9%) (X² = .73, p = .39).
Accounts of those who posted a Favorable tweet had a mean following of 4406.25 (sd = 35464.92). Those posting an Opposing tweet had a mean following of 7636.38 (sd = 62306.47). Both distributions had strong positive skews as each group had a handful of accounts with over 100000 followers, thereby warranting non-parametric analysis. A Mann-Whitney U analysis revealed a significant difference (Median Favor: 418, Median Oppose: 660.50, U = 154750, p < .001).
Days on Twitter
The number of days an account has been on Twitter was calculated by subtracting the join date from the date 7/4/19 using Microsoft Excel. Those with a Favoring tweet had mean membership of 2028.18 days (sd = 1305.94) while those with an Opposing tweet had a mean membership of 2424.96 days ( sd = 1187.86). Though equal variances were not assumed, there was a statistically significant difference between the means, t(771.28) = 5.45, p < .001.
Likes and Retweets
Because the Twitter Archiver collects tweets hours after a tweet has been posted, ascertaining a tweet’s influence through liking and/or retweeting cannot be done in a reliable way. As such, no analysis was conducted with these data.
Doctoral Degree Representation
The full name of accounts were searched for inclusion of a title or letter series that would indicate doctoral level obtainment (e.g., “Dr.”. “Ed.D.”, “Ph.D.”, etc.). Within the Favor group, 16 (1.7%) had a profile indicating doctoral degree while 23 of the Oppose group (6.0%) reported the same. This difference is statistically significant, X² = 19.89, p < .001).
The source of a tweet was put into one of two categories. Tweets that were posted to Twitter through a mobile Twitter app or the Twitter website were labeled as “Direct”, while those that used a third-party app (e.g., Hootsuite) were labeled “3rd Party”. The percentage of those with Direct posts was significantly higher among those in the Oppose group (66.7% vs. 45%, X² = 51.32, p < .001)
The biography of each poster was copied and pasted into the website wordcounter.net. The most frequent thirty words (excluding common words such as “the”, “a”, etc.) were generated along with a frequency of each.
The following are the 30 most frequently appearing words among those in the Favor group (word, count, % occurring overall):
- education 70 (2%)
- teacher 69 (2%)
- school 65 (2%)
- all 48 (2%)
- students 36 (1%)
- life 36 (1%)
- people 35 (1%)
- development 34 (1%)
- training 33 (1%)
- mom 32 (1%)
- more 31 (1%)
- student 31 (1%)
- wife 31 (1%)
- author 30 (1%)
- world 30 (1%)
- love 29 (1%)
- educator 29 (1%)
- online 27 (1%)
- coach 26 (1%)
- own 26 (1%)
- help 25 (1%)
- support 25 (1%)
- technology 24 (1%)
- through 24 (1%)
- about 23 (1%)
- business 22 (1%)
- director 22 (1%)
- teaching 21 (1%)
- college 21 (1%)
- community 21 (1%)
The following are the 30 most frequently appearing words among those in the Oppose group (word, count, % occurring overall):
- teacher 64 (4%)
- education 57 (3%)
- own 34 (2%)
- educator 30 (2%)
- teaching 28 (2%)
- views 27 (2%)
- all 25 (1%)
- school 25 (1%)
- research 24 (1%)
- university 24 (1%)
- author 23 (1%)
- student 23 (1%)
- psychology 23 (1%)
- professor 23 (1%)
- about 19 (1%)
- ed 18 (1%)
- science 17 (1%)
- consultant 17 (1%)
- teachers 15 (1%)
- development 15 (1%)
- writer 15 (1%)
- instructional 15 (1%)
- social 14 (1%)
- technology 14 (1%)
- students 13 (1%)
- tweets 13 (1%)
- digital 13 (1%)
- work 13 (1%)
- director 13 (1%)
- schools 13 (1%)
Gee, I hate typing out a results section. Anyway…
In opposition to my colleague’s assumption, support for learning styles theory is quite strong. There were some interesting trends. First, tweets supporting learning styles were more likely than those opposing them to have been posted using a third-party app. While the reasons for using a third-party app may vary, one of their biggest draws is that multiple posts can be scheduled at one time. This may appeal to businesses and other entities that are trying to promote some sort of enterprise. A number of tweets referenced learning styles in some way to promote a variety of things, including carpets and astrology. One should not infer, however, that the majority of the Favor group is motivated by financial gain in making their post, though it is certainly reasonable to assume that some are. Likewise, there are certainly those in the Oppose group who have their own personal, financial agenda.
It was also interesting to see that those in the Oppose group were more likely to represent themselves as being in higher education or a research-based field. They were more likely to include a doctoral designation in their name, and the author bios were more likely to include words such as psychology, science, and professor. Interpretation of these data should be taken with great caution. Anything, including false credentials, can be posted on Twitter. Likewise, being a psychological scientist, career researcher, or possessor of a doctoral degree does not guarantee good teaching; in fact, some would claim the opposite. In a similar vein, one should not assume that those in the Favor group are impervious to research findings. Differences aside, these reporting academic designation in some way represented small subgroups.
Finally, although there are fewer of them, those in the Oppose group tended to have been on Twitter longer and have more followers. The fact that this relatively informal research could not measure the number of likes and retweets of each tweet limits the inference of how influential each group is in promoting their perspective. Nevertheless, it is clear that there are different characteristics between those promoting learning styles and those who oppose them.
It should be noted that these tweets took place mostly in the month of June, when many educators, for good reason, do not want to “talk shop” either in person or virtually. There might be different proportions in the month of August, when many teachers (particularly in the United States) are going through professional development sessions, during which they may be more inclined to voice their thoughts about the presentations being given. Some accounts, owing to their passion or interest, posted numerous times about this topic. Multiple posts are more likely to mean more exposure ideas being promoted. Limiting their presence to one post may undermine their relative influence. Future research should take multiple postings by a single user into account.
Finally, each group is not homogenous even though this report treats them as such. For example, within the Favor group, there were tweets from students/former students who expressed displeasure in a lesson/course/instructor who did not teach to their style, parents who were grateful that schools identified the style of their child, and teachers stating the importance of identifying and adapting to a student’s style. It would be interesting to compare these taxons for potential differences. Future research (I am talking to you graduate students looking for a research project that will likely get expedited IRB review) might benefit by using a more nuanced categorization scheme.
This website was funded by my credit card. Since I have to pay them back, never mind this.
I like research and I like bowling. My yearning to do the latter has detracted from my ability to do this post. In fact, this post would probably be better if knocking down kegels was not on my mind.
The spreadsheet is available for anyone wishing to do their own analysis. The archiver is still collecting tweets, which may be of interest to anyone wishing to continue this line of inquiry. I should warn you that reading through hundreds of tweets is a tiring process!
There is none as of this writing. If you have comments or corrections, you can post them below or find me on Lane 23.