Yanjin Long | Learning Scientist and User Experience Researcher

 

Motivational Design in an Intelligent Tutoring System that Helps Students Make Good Task Selection Decisions

Making effective problem selection decisions is an important yet challenging self-regulated learning (SRL) skill. Although efforts have been made to scaffold students’ problem selection in intelligent tutoring systems (ITS), little work has tried to support students’ learning of the transferable problem selection skill that can be applied when the scaffolding is not in effect.

This project uses a user-centered design approach to extend our ITS for equation solving, Lynnette, so it motivates and helps students learn to apply a general, transferable rule for effective problem selection, namely, to select problem types that are not fully mastered (“Mastery Rule”).

This project is a collaboration with a Master of HCI student Zachary Aman from the Human-Computer Interaction Insititute at CMU.

We conducted user research through classroom experimentation, interviews and storyboards. We found that the lack of motivation, especially lack of a mastery-approach orientation, may cause difficulty in applying the Mastery Rule. Based on our user research, we designed prototypes of tutor features that aim to foster a mastery-approach orientation as well as transfer of the learned Mastery Rule when the scaffolding is faded.

- Check out the design process and prototypes from Zachary's blog post.

 

Integrating Gamification Features in Intelligent Tutoring Systems to Support Fun and Learning

Integrating gamification features in Intelligent Tutoring Systems (ITSs) has become a popular theme in ITSs research. The goal is typically to make the system more engaging for students, while maintaining its effectiveness in supporting learning.

This project focuses on gamification of shared student/system control over problem selection in a web-based linear equation tutor, Lynnette, on Android tablets, where the system adaptively selects the problem type while the students select the individual problems.

We conducted a 2x2+1+1 classroom experiment with 267 middle school students to study the effect, on learning and enjoyment, of two ways of gamifying shared problem selection: performance-based rewards and the possibility to redo completed problems, both common design patterns in games. We also included two ecological control conditions: a standard ITS and a popular algebra game, DragonBox 12+.

- Long, Y., & Aleven, V. (2014) Gamification of Joint Student/System Control Over Problem Selection in a Linear Equation Tutor. ITS' 2014 [PDF]

 

Supporting Students' Self-Regulated Learning in Intelligent Tutoring Systems

Theories of Self-Regulated Learning (SRL) emphasize that students actively plan, monitor and evaluate their own learning. Studies have shown that better learners tend to have better SRL skills. There are different processes involved at the different stages of Self-Regulated Learning, such as self-assessment, self-explanation, help-seeking, goal setting, study choice, etc.

This project focuses on two SRL processes: self-assessment and study choice, and studies 1) the roles the two processes play in enhancing students' math learning with intelligent tutoring systems; and 2) how to design the intelligent tutoring systems to support these two processes. Specifically, we have focused on the design of an Open Learner Model (an Open Learner Model is a visualization in ITS that displays students' learning status (how much/how well they have learned) based on the intelligent tracking of the system).

- Long, Y., Aleven, V. (2013) Active Learners: Redesigning an Intelligent Tutoring System to Support Self-Regulated Learning. EC-TEL' 2013. [PDF]

- Long, Y., Aleven, V. (2013) Supporting Students’ Self-Regulated Learning with an Open Learner Model in a Linear Equation Tutor. AIED' 2013. [PDF]

- Long, Y., Aleven, V. (2013) Skill Diaries: Improve Student Learning in an Intelligent Tutoring System with Periodic Self-Assessment. AIED' 2013. BEST STUDENT PAPER AWARD [PDF]

- Long, Y., Aleven, A. (2011). Students’ Understanding of Their Student Model. AIED' 2011. [PDF]