Statistics and data science courses have often been described as full of student engagement opportunities. As the information age advances, data, graphs, and statistics have become an everyday staple on every Internet and social media platform available. Our students need to be able to understand and discuss this statistical information in appropriate ways so they can make informed decisions and develop informed opinions about various issues. I believe our grand goal is always to develop more statistically literate citizens.
IMPACT (2018) guides us in the implementation of student-centered learning environments that engage students:
“Students should interact with each other often through meaningful discourse and collaborative activities for the purpose of sharing and refining ideas.
- Students should develop as mathematical thinkers by engaging in inquiry-based learning through exploration, conjecturing, questioning, sense making, and seeking alternate solution paths.
- Students should be provided opportunities to make mistakes and collectively learn from them.
- Students should work in a physical setting that promotes teamwork, builds respect for one another’s ideas, and critique the thinking of others.
- Students should work with appropriate tools to expedite computations and symbolic manipulations, but also to formulate hypotheses, test conclusions, and validate their thinking” (p. 46).
I would like to briefly share my own thoughts, with the goal of sparking some dialog around these strategies in statistics and data science courses.
“Students should develop as [statistical] thinkers by engaging in inquiry-based learning…”
In statistics and data science, it is possible (and some say necessary) to emphasize for our students the investigative process when working with data (see GAISE, 2016). Asking questions, seeking information, evaluating that information, and presenting solutions are valuable opportunities for students to engage: not only with the course content, but with each other and with their instructor as well. Data analysis projects often involve teams of individuals, and creating this collaborative environment in our courses is an important consideration.
Question: How can we help instructors find the best scenarios for inquiry-based learning?
“Students should be provided opportunities to make mistakes …”
Exploration, by nature, involves choosing a path and seeing where it leads. Sometimes, it turns out to be the wrong path. Instructors should offer students an opportunity to explore data, explore statistical information (e.g., a graph shared on the Internet), and explore their own (and others’) thinking about statistics. This means mistakes are going to happen. How can we promote a learning environment that allows for students to make mistakes and learn from them without being penalized? This question leads into a whole other area – assessment.
Question: What assessment opportunities can we use to encourage making mistakes while also promoting appropriate interpretations and computations?
“Students should work in a physical setting that promotes teamwork …”
This idea ties in nicely with the first – creating a collaborate environment that promotes teamwork and learning from and with others can lead to powerful results. All of this takes time, however, and very often instructors struggle to make large, sweeping changes to their course structure all in one term.
Question: How can we help instructors find ways to build in collaboration and teamwork in small steps?
“Students should work with appropriate tools to expedite computations and symbolic manipulations, but also to formulate hypotheses, test conclusions, and validate their thinking”
Here, we remind ourselves that there are procedures and mechanics involved in statistics and data science courses; we cannot escape that fact. However, how do we appropriately emphasize those mechanics, as well as generating correct answers, while also emphasizing what the results actually tell us about the data in question? This is an important dichotomy to explore further – “doing statistics” vs. “thinking about statistics”.
Question: When in our statistics and data science courses can we shift the computations to technology, and when do we (if at all) need to give students the opportunity to see how the computations work?#ST-Statistics#Engagement