Project Activities
In Year 1 and 2, research and development will occur in a research laboratory at Texas A&M University. Following a laboratory usability evaluation study, in Year 3 a series of pilot studies will evaluate the feasibility, fidelity, and promise of proposed environment to improve student learning of algebraic equations.
Structured Abstract
Setting
The research will take place in a research laboratory at Texas A&M University and in classrooms in suburban schools in medium to-large sized cities in Texas and Pennsylvania.
Sample
For the lab evaluation studies, researchers will recruit participants through Texas A&M University's subject pool. The classroom research in schools will include 120 grade 7 and 8 students and eight algebra teachers.
In prior IES and NSF projects, the researchers developed and tested the Artificial Peer Learning Environment Using SimStudent (APLUS), an online environment for students to tutor a simulated peer. In the interaction, students and the simulated peer student solve questions step by step and are provided feedback on the accuracy of the steps performed. In this project, the researchers will develop a module of APLUS within a specific domain, algebraic linear equations for grades 7 to 9. The team will develop a machine learning engine to enhance the student and simulated peers interaction in working through algebraic manipulations.
Research design and methods
To develop the environment, the researchers will employ a data-driven system-engineering procedure where iterations of research and development occur in lab evaluation studies and classroom pilot studies until feasibility, usability, and learning aims are met. After development is complete, the researchers will conduct a series of between subjects randomized control trials to examine whether the environment shows promise for improving student learning of algebraic equations. In the studies that will occur over the course of a school year, classrooms will be randomly assigned to implement the tutoring environment or not. Learning outcome measures will be collected from pre-, post-, and delayed-tests to assess the competency in solving equations and conceptual understanding.
Control condition
The researchers will compare several versions of APLUS to a control group that uses a different version of APLUS to isolate the effect of different approaches and to understand when and how learning by teaching becomes most effective. For each study, students will be randomly assigned into one of the study conditions at the beginning of the school year, with treatment students using the same type of intervention throughout the year.
Key measures
To iteratively develop and refine the technology, the researchers will use a think-aloud protocol where participants provide feedback while using the prototype. For the pilot studies, the researchers will gather data using a combination of researcher-developed assessments of content and procedural knowledge, will conduct classroom observations, and will use learning processdata to measure the interactions between students and the simulated peer.
Data analytic strategy
The research team will employ analysis of variance with between-subjects comparisons to understand the effect of the treatment. Additionally, the researchers will use within-subjects comparisons for all students to examine learning gains by comparing performance at pre-, post-, and delayed-exposure to the intervention and performance over a longer period of time. In addition, researchers will employ correlation and causal analyses to identify key cognitive and background variables that are significantly related to learning outcome measures.
People and institutions involved
IES program contact(s)
Products and publications
Researchers will develop a module of Artificial Peer Learning Environment Using SimStudent (APLUS), a game-like environment for middle school students to practice and learn mathematics by collaboratively solving algebraic equations with a simulated peer student.
Publications:
Shahriar, T., & Matsuda, N. (2024, July). “I Am Confused! How to Differentiate Between…?” Adaptive Follow-Up Questions Facilitate Tutor Learning with Effective Time-On-Task. In International Conference on Artificial Intelligence in Education (pp. 17-30). Cham: Springer Nature Switzerland.
Additional project information
Related projects
Supplemental information
Co-Principal Investigator: Huang, Ruihong
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
