Enhancing student reflections with natural language processing based scaffolding: A quasi-experimental study in a large lecture course

Abstract

Multiple studies have shown that scaffolding plays an important role in regulating and enhancing students' metacognitive monitoring and reflections. However, scaffolding students' reflections in large courses is a major challenge. In the current study, we explored how real-time, technology-enhanced scaffolding affects the quality of students' reflections and academic performance. Two major research questions are: RQ1) Do students in the scaffolding condition construct more specific reflections than those in the non-scaffolding condition? RQ2) How do the scaffolding feature, reflection specificity, and the number of reflections relate to students' academic performance? To address these questions, we conducted a quasi-experimental study with a large sample of undergraduate students (N = 1268) in an introductory psychology course. We designed and used a mobile application called CourseMIRROR that prompts students to reflect on what they found confusing and interesting in the lecture. The app uses Natural Language Processing (NLP) algorithms to evaluate students' reflection quality and specificity using a 4-point scale, with 1 indicating shallow reflection and 4 indicating highly relevant or specific reflection. Course sections were randomly assigned into scaffolded or non-scaffolded conditions. Students in the scaffolded condition were provided an app version with the scaffolding feature, while students in the non-scaffolded condition were provided a different version of the app without scaffolding. Regarding RQ1, we found that students in the scaffolded condition wrote significantly more specific reflections on confusing and interesting concepts. For RQ2, results showed that the number of reflections was a significant predictor of academic performance.

Keywords

• Scaffolding
• Reflections
• Natural language processing
• Large-lectures
• Mobile
• Technology