Design Matters: Students' Adherence to High-Structure Course Design Activities Predicts Course Performance

• In Event: Differential Learning Formats for Self-Regulated Learning

Abstract

Background and Aim
Many undergraduate STEM lecture courses facilitate active learning as a key component of instruction through high-structure course designs (Eddy & Hogan, 2014; Freeman et al., 2014). Integrating active learning has proven largely beneficial, but further explorations into how and why these approaches benefit students are needed (Singer et al., 2012).

We employed a novel method to measure the how students’ adherence to instructors’ intended activity sequence related to their performance in a high-structure undergraduate biology course. To accomplish this, we developed rules to interpret digital trace sequences that would indicate whether students followed the instructors’ intended “golden path” of course activities by completing activities before, during, and after class as assigned. Next, we analyzed whether the amount of students’ adherence to the golden path predicted their course performance, beyond mere completion of activities.

Method
Our study involved 432 college students enrolled in an introductory biology course at a large United States postsecondary institution in Fall 2020. Course instructors adopted an asynchronous virtual format due to the COVID-19 pandemic and implemented a high-structure framework that was further facilitated by the timed release of learning materials. Instructors encouraged students to complete specific tasks prior to and during scheduled class times (Table 1). Students’ completion of activities, in their intended order, represented an instance of a student following the golden path for that lesson.

Digital trace data from the course learning management system (LMS) was used to measure student adherence to the golden path for each lesson throughout the semester. We summed counts of golden path completions across each content unit into unit counts and a cumulative semester count. Similar counts were generated for students’ lesson completions. Lesson completions were inclusive of the golden path count and included any instances in which students engaged in all the instructor recommended activities, regardless of order. Then, we regressed unit exam (Table 3), final exam, and course grades (Table 4) on lesson completions and golden path completions, prior biology knowledge, and completion of a digital SRL intervention (i.e., those who consented to receive learning support were randomly assigned to either a control group or a digital Science of Learning to Learn intervention completed before the first exam [SoL2L]).

Results
Adherence to the golden path was predictive of outcomes early in the semester (Table 3). Over subsequent units, the intended sequence of the golden path offered no additional predictive value beyond completing activities. Overall, more frequently following the golden path predicted higher performance on both the final exam and course grade. Interactions with the intervention (see Tables 4, 5, and 6) revealed that golden path adherence was most predictive for control students.

Significance

Our analyses substantiate the value of high-structure courses and student adherence to their structure. The SRL intervention may have provided learners with tools and strategies that served as a comparative replacement to benefits others derived from following high-structure instructional design guidance. Positive effects of high-structure courses may diminish as learners become more adept at self-regulating their learning.

Authors

• Michael Berro, University of North Carolina - Chapel Hill
  Presenting Author

• Laura Ott, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Jeff A. Greene, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Matthew L. Bernacki, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Robert D Plumley, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Shelbi Laura Kuhlmann, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Linyu Yu, University of North Carolina - Chapel Hill
  Non-Presenting Author

• Kelly Hogan, University of North Carolina - Chapel Hill
  Non-Presenting Author