In this post entry, we are interested in the analysis of the students’ interactions on Twitter. The interactions between the peers, between the lecturer and the students, or between the students and the TA, and the students’ interactions with the subscriptions change in Fall 2016, Spring 2017, Fall 2018 and in Spring 2018. We would like to compare the references of that category and identify the trigger of those variations.
Source image: pixabay
In Fall 2016, there are 219 references in the node interaction with the lecturer, 100 in Spring 2017, 135 in Fall 2018, and 70 references in Spring 2018. It appears here that the references in Fall are higher than those in Spring. It has also occurred to us that the lecturer’s engagement was higher than in the following years. Actually, the students were constantly answering questions about the course material. This assumption must nevertheless be backed up by the information about the node theme.
There are 94 references in the node interactions with peers in Fall 2016, 87 in Spring 2017, 439 references in Fall 2018, and 86 references in Spring 2018. In this case, the references are also higher in Fall. But it is even higher in Fall 2018. It will be interesting to understand what reasons could have possibly triggered that result. In Fall 2018, the lecturer was not involved in the Twitter activity. Moreover, in the same semester, more than 40 students are taking the French course. The high level of interactions between them may indicate the convenience to interact with peers, and probably a motivation originating from the non-participation of the lecturer on Twitter.
In Fall 2016, the node interactions with TA has 16 references node in Spring 2017. In Fall 2018, 204 references in that node and 164 in Spring 2018. The numbers are lower in Fall 2016, which breaks the trends we were trying to build.
In Fall 2016 and 2018, the interactions with subscriptions are low. 23 references in 2016 against 15 in 2018. In Spring, only one interaction with the subscriptions is counted. Those numbers also show that the student does not really interact with subscriptions. It also reveals that the interactions between peers were higher when the professor was not interacting.
As a research assistant, I have been involved in Dr Caws’ project for approximately a year now. As a student in literature, I can assert that I was playing out of my comfort zone. My roles in her research were split between my tasks as a research assistant and a teaching assistant.
Image reference: pixabay
As a RA*, I have basically been collecting and coding data collected from Twitter using Nvivo. The coding comprehends 3 main parts. Firstly, I am coding the students’ interaction with their peers, their teacher and the TAs. Secondly, I was also coding their production, and thirdly the themes. The popularity of certain categories varies from one year to another.
When I was a TA in Fall 2018, I interacted with the students on Twitter. We were mostly encouraging them to produce data using the new vocabulary learned in their French class. Our conversations revolved around what they learned in the French class with their teacher.
Image refence : pixabay
Thanks to this research, I have discovered the possibility to use social media to learn a new language by following and interacting with pages in French. Nevertheless, the students’ motivation drop after each term since their interactions ceased.
During this research, I am also learning to recognize the common mistakes of English-speaking people who are learning French.
*RA= Research assistant
TA= teaching assistant
Here is the long overdue post on the grammar project, I have carried out in the lab for the past three years. I think the last post was four years ago when the pilot study had been completed.
The grammar project had students come to the lab and practice prepositions. Students were intermediate learners of German at an A2 proficiency level. Each student had six sessions in the lab, one per week. Each week each student read a fairy-tale and then practiced comprehension by either using a multiple-choice format (MC) or a fill-in-the blanks format (FiB). These formats represent what in cognitive psychology is referred to as “maintenance rehearsal” (MC) and “elaborate rehearsal” (FiB). A pre-test and two post-tests were carried out, one in the FiB and one in the MC format. The study was repeated for reasons of validity.
All in all, the data of 47 Intermediate German language learners has been analyzed by compiling the data in two groups (MC and FiB) and using inferential statistics as well as a log that tracked every klick (MC) and word typed (FiB) of every learner. Results showed that the learners in both groups improved their scores in the post-tests compared to the pre-test. The MC group took less time to do so. According to the log, the time, the MC group practiced was about half of what the FiB group needed. Furthermore, the MC group improved the number of correct answers by the third practice session while the FiB group needed at least four practice sessions. However, the FiB activity outperformed the learners using the MC activity in both tests, although differences were only significant in the test using the FiB format.
What do we learn from this? If my students ask me how to practice prepositions, I ask them how much time they have to practice. The MC activity type should assist them to improve their knowledge of prepositions in a relatively short period of time. Using the FiB activity type, they need to put in more time and effort to see some improved results. However, they will likely improve their knowledge of prepositions more than with the MC activity.
This week I have been working on using NVivo to code the language used by UVic French students in a peer tutoring relationship with French students in Brazil. Under the direction of Dr. Catherine Caws, I have sorted their transcribed discussions into the categories of “language”, “instrument” and “task”. Through these categories, certain qualitative trends have become more evident, such as the type of metalanguage used by UVic students while helping the Brazilians, the strategies that they use, and the sorts of tools that they use for searching and correcting language errors. Below is a diagram illustrating a frequent word in that category of “instrument” and the contexts in which it was employed.
After identifying the vocabulary associated with each category, diagrams like this, as well as the frequency of their occurrences can be easily generated. This coding helps us to see from the context of their own dialogue, what technology these second language learners use as support.
Over the past few weeks I have been working on the qualitative data analysis of tweets produced by students during their French language learning experience in Dr. Caws’ class, FRAN 160. I have been using the program NVivo to sort and to categorize their tweets by theme or by type of interaction (i.e. with classmates, with the professor or TA, with people they follow). This has allowed us to clearly understand trends in the content of student tweets, as well as ways in which we, as language learning guides, can help to better their learning experience.
Looking through these tweets, I have found it particularly interesting to explore what motivates students to participate. It seems that most of their participation comes from tweets related to their class (i.e. responses to questions, discussing assignments or tests, mentioning an event or topic that was discussed in class) as shown on figure 1 below, rather than tweets related to their personal life, to pop culture or to the people they follow. This data, as well as data from answers they provided in a student survey has lead me to believe that their motivation for using Twitter in a class is highly performance based, rather than intrinsic. Thus, it is our goal to figure out how to motivate them! Looking at the Self-Determination Theory, which states that intrinsic motivation is a result of autonomy, competence and relatedness, I have been exploring the content of their tweets and survey answers to determine what factors might have an impact on future learning experiences. I am therefore exploring questions such as: what will motivate students intrinsically? How can we make them feel that using Twitter is not a chore, but a choice? How can we make them feel more like they are part of an online francophone/francophile community? How can we make them feel more comfortable and competent using Twitter, especially if they are new to the website?
Figure 1: analysis of tweets content themes
I’m eager to continue exploring these questions and the ideas that they will spark for impactful ways to improve the language learning experience!