by Megan Mocko
In a previous blog, I wrote about representation as one of the guidelines for Universal Design for Learning (UDL). In this blog post, I am going to concentrate on engagement.
In this blog post, I will concentrate on the guideline for representation, which is primarily processed in the back portion of our brains. UDL encourages instructors to provide multiple ways for students to receive the information. There are twelve checkpoints for this principle, but I am going to focus on two of them: “Illustrate through multiple media” and “Clarify vocabulary and symbols” (Meyer et al., 2014, p. 99).
Engagement, the next UDL guideline, is processed in the middle portion of the brain. In this guideline, we work to invoke students’ motivation and maintain their movement toward achieving the learning objectives. Engagement encourages the instructor to think about the “why.” There are ten checkpoints for this guideline, so once again, I will concentrate on only two of them: “Optimize individual choice and autonomy” and “Optimize relevance, value, and authenticity.” More information can be found at Cast.org.
One way to increase the relevance of learning statistics for students is by applying the GAISE guideline (GAISE College Report ASA Revision Committee, 2016), “Integrate real data with context and purpose.” For example, in their interviews with students, Neumann, Hood, and Neuman (2013) found that more than 50% of the students found that actual data made statistics more applicable to everyday life.
However, no real data set works perfectly for all students. So, to integrate individual choice (as well as relevance), we can have students choose their own data that is relevant to them. In an assignment in my course, I provide several links to data set repositories on the web in addition to data sets that I have curated. The students choose which data they want to analyze for their homework. To make sure this is successful, I found that it’s important for the instructor to set up appropriate scaffolding so that students pick an appropriate data set to perform a given analysis.
Another way that I incorporate individual choice in my class is by giving students options on how to demonstrate their understanding after completing the “prep” material for class (e.g., reading the textbook or watching the videos). Some of the options include:
- Infographic about the material: Students can create a graphic that depicts essential parts of the reading or videos and uses their creativity to design the page.
- Written summary of the material: Students are asked to write a summary of key concepts that are presented in the material. They are reminded to look at the posted learning objectives and use those as their summary’s focus.
- Muddy points: Students submit a list of topics from the material in which the information was unclear. In other words, their understanding was vague or muddy.
- Answers to a reading guide: For each of the readings, students complete a list of essential questions that I create to help students focus on the critical parts.
- “Fill-in-the-blank” notes to accompany the videos: For the videos, the notes include all of the examples, software output, and key definitions. However, blanks are left for students to write the hypotheses, define the parameters, write the conclusions, etc. This way, when a student is watching the video, they can fill in the notes.
- Perusall comments on the textbook chapters: Perusall is social annotation software that allows learners to comment on the textbook chapters and ask questions. Perusall will then use artificial intelligence to grade the students’ comments and activities. (I still go in and update the grades.)
- List of new vocabulary: Students are asked to create a list of new vocabulary and their definitions.
- Video summary of the material: Students can create a video (roughly 5 minutes) that covers the main themes presented in the reading or the instructor videos.
- An auto-graded quiz on the material: Students can complete a quiz on the concepts for the module.
For all but the quiz and the Perusall comments, the students can either post to the discussion board or submit directly to me as an assignment, which other students do not see. I provided this individual choice for assignment submission to take into account student preference for sharing work with the rest of the class.
In reality, students tended to primarily select one of four options: “fill-in-the-blank” notes, a written summary, the reading guide, and the auto-graded quiz. Therefore, I think decreasing the number of choices would be more efficient, while still providing an opportunity for students to choose how they demonstrate their knowledge. In fact, Iyengar and Lepper (2000) suggested using six or fewer options.
Encouraging choice and creating authentic assignments can help students see the relevance of the material and increase their motivation to learn.
Contributing author Megan Mocko is a lecturer at the Warrington College of Business. She teaches statistics to undergraduate and graduate students. Before that, she rose through the ranks from lecturer to senior lecturer and master lecturer in the Department of Statistics in the College of Liberal Arts and Sciences also at UF. Megan has taught statistics in multiple formats: face-to-face, hybrid, and completely online.
In addition to her teaching, Megan’s involvement in statistics education led to her work as co-chair on the 2016 GAISE (Guidelines for Assessment and Instruction in Statistics Education) report. The American Statistical Association endorsed the revised 2016 GAISE report. Megan was also program chair for the eCOTS (electronic Conference on Teaching Statistics) in 2022 and 2020. In the Fall of 2022, she began her doctoral journey in Curriculum and Instruction with an emphasis in Educational Technology at the UF College of Education. Her area of specialization is Virtual Exchange. She is interested in engaging everyone in the classroom using educational technology and using virtual exchange to promote communication about data across international boundaries.
CAST (2018). Universal Design for Learning Guidelines version 2.2. Retrieved from http://udlguidelines.cast.org
GAISE College Report ASA Revision Committee, “Guidelines for Assessment and Instruction in Statistics Education College Report 2016,” http://www.amstat.org/education/gaise.
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995–1006. https://doi.org/10.1037/0022-3518.104.22.1685Neumann, D. L., Hood, M., & Neumann, M. M. (2013). Using real-life data when teaching statistics: Student perceptions of this strategy in an introductory statistics course. Statistics Education Research Journal, 12(2), 59-70.
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