As legal research instructors, we often have trouble motivating students. Some of the obstacles are external: the law school curriculum deemphasizes legal research, usually awarding fewer credits for legal research courses or making them pass/fail. Other obstacles relate to the pedagogy, which has traditionally focused on bibliographic details that students can scarcely imagine putting to any useful end.
However, most of us recognize that students must be motivated in order to learn. And many studies confirm our intuitions: students who are motivated to learn have better retention rates and they perform better in their classes.
But how can we motivate our students? Just like anyone else, students have their own reasons for doing things, and those reasons can be difficult to discern. This can make motivation feel like a quality that exists outside of the instructor’s control or responsibility.
Enter the ARCS model of motivational design, John Keller’s well-studied and empirically validated model of “the process of arranging resources and procedures to bring about changes in people’s motivation.”[1] You can think of it as a subset of instructional design that focuses on designing materials that motivate learners.
ARCS has four components: Attention, Relevance, Confidence, and Satisfaction. Today, we’ll focus on Attention and Relevance, discussing their role within the overall model and taking a look at some of my own efforts to implement these components of the model in my classes.
Attention
When designing for motivation, we focus not on directing student attention, but on getting and maintaining that attention. We first seek to elicit the reflexive type of curiosity called perceptual arousal. Almost anything unexpected can do the trick, whether it’s a piece of surprising info, an arresting image, or a groanworthy pun. I get good results combining these, like you see in this introductory slide for my recent class on ‘codified law’:
Visual puns and memes work well here, because the humor itself has so little content. You want to avoid distracting from your next goal: inquiry arousal, which is best activated by presenting students with a problem that they must resolve by seeking knowledge. To do this, I quickly followed my ‘codified law’ title slide with a series of increasingly difficult questions, starting with queries they could answer by recapping the pre-class materials and progressively exploring deeper insights and implications:
- “What’s the statutory code here in Georgia?”
- “What makes it a form of codified law?”
- “Why is ‘codified law’ a useful category for organizing our research?”
Now that we have our students’ attention, we want to sustain it with variability. Here, we are trying to meet our students’ sensation-seeking needs by changing aspects of the instruction, such as the pace or the approach. I do this by breaking each class session into different sections that employ different modes of instruction. My codified law class shifts from the semi-Socratic exchange described above to a more conventional lecture on statutory structures like definitions and exceptions. From there, it segues into a hypothetical where I work with the class to research how Georgia’s consumer protection statute might apply to the sale of a stolen Delorean “time machine” at DragonCon.
Relevance
Why place the hypothetical up the street at Altanta’s gigantic multimedia/popculture convention? These details help to make it more familiar and interesting. Once we have students’ attention, we need to build bridges between the material and their needs, wants, desires. We need to establish relevance.
The first relevance subcategory, goal orientation, is the most important. You want to let students know how the course content relates to their goals. In my experience, students are receptive to the idea that they need to learn legal research to achieve the goal of becoming a successful lawyer. I make a point of explicitly discussing goal orientation early in the course: in the first session, we discuss survey data showing just how much legal employers value legal research in new lawyers:
We also want to create relevance with motive matching. We want to ensure that the learning environment is compatible with different student motives. This means giving achievement-oriented students a sense of control over their success, while also creating opportunities for affiliation-oriented students to collaborate with others. For my two individual assessments, I try to create a feeling of control by providing sample questions, answers, and rubrics, alongside a video discussing my expectations.
I also try to create opportunities for different kinds of collaboration throughout the course. For example, I collaborate with students to research the aforementioned “stolen Delorean” hypo, letting the class direct my on-screen research. In contrast, my ‘cases’ class includes a semi-competitive game where students form teams and compete to see who is the best at distinguishing mandatory and persuasive authorities.
Finally, familiarity creates relevance by connecting the material to students’ interests and experiences. The abstraction of legal research tends to make it feel very unfamiliar for students, something I try to mitigate with concrete examples. I have also found that students reliably have a high level of interest in certain areas of law, and I try to build assignments and examples around those topics. The student interactions that confirm this interest can be rewarding: after completing my immigration-focused statutory research assignment, students frequently ask me about careers in this area of law or express interest in our law school’s new immigration clinic.
To be continued
The ARCS model provides legal research instructors with a reliable way to improve student motivation. Today, we’ve looked at some strategies for employing the first two ARCS components: Attention and Relevance. In a future installment, I’ll cover Confidence and Satisfaction before wrapping things up with a few thoughts on ARCS as a whole.
[1] John M. Keller, Motivational Design for Learning and Performance: The ARCS Model Approach at 22 (2010).