InterestMe Math provides students with personalized MWPs based on their interests (including familiar names and their career interests) using a simple website design. I developed, designed, and evaluated the system.
Many students tend to view math word problems as uninteresting, unimportant, and unnecessary when the problems are perceived to be irrelevant and disconnected from student's real-world experiences. As a result, students may become disengaged and uninterested in the subject altogether. How could I design and develop a math word problem technology that generates comprehensible problems to increase student comprehension, engagement, and interests?
For my dissertation research, I have designed, developed, and will be evaluating a math word problem generator that outputs math word problems based on students' interests.
In-person interviews were conducted with 14 stakeholders (10 5th-7th grade participants and four math teachers) to analyze the needs of the users and inform the design of the system. I wanted to understand student's:
For the teachers, I wanted to understand:
Needs of Stakeholders
The interview transcripts were cleaned and added to MAXQDA for coding. Based on the scope of the project, relevant needs were created from participant statements (see below).
InterestMe Math will facilitate ownership by involving familiar content based on students’ interests. The app is focused on increasing students’ perceived usefulness of math and math word problems by rewriting non-personalized math word problems to include their career interests.
Have ownership of the theme of problems for personalization
Interact with engaging material that includes their interests
Learn the math skills that will be helpful in the present and future.
Traditional math word problems are boring and irrelevant
Students have difficulty retaining math skills learned in class
Student perceive little to no utility value among traditional math word problems
Provide materials with a good balance between learning and engagement
Access to student data easily
Provide systems that reinforce taught math solving strategies
Lack of classroom management support
Lack of systems that align with teacher lesson plans
Lack of systems with aids and hint features to support student retention of information
Relevant pain points and goals were identified. Due to the scope of this project, this research satisfied a limited number of them.
Personas were created to reflect the motivations and expectations of the users to inform the design of the system. The illustrations of these key users were based on the interviews and research reports in math education. Two student and one teacher personas were created:
Trends in math technology were observed. Google Classroom was mentioned by one of the teacher participants to describe the ease of logging in using a generated Class ID. The final design system included this feature.
The first sketch of InterestMe Math's assessment pages displays multiple math word problems on one page (as shown below). Khan Academy's website design inspired the reorganization of the assessment/math question pages. The design was updated to include this design practice.
The system went through several iterations based on user feedback. Below are illustrations of the wireframes, low-fidelity prototype, and the final design.
Feedback was gathered from four teachers using the think-aloud method. The goal was to receive input on the functionality and features of the system. The prototype was created in Powerpoint and the pages were uploaded to Invision.
The usability of the system was examined by the target users and measured by the System Usability Scale (SUS). Based on the SUS results (82.62), the usability is considered "Above average" (score above 68) and rated as "Excellent" as suggested by an adjective rating scale (12.5 = Worst Imaginable to 90.9 = Best Imaginable).
The effectiveness of the system was examined in the evaluation/learning impact phase. In this study, the cohesiveness of the problems, student performance, and students' triggered situational interests was explored. The students did not solve the personalized problems significantly better than the non-personalized math word problems. Nor was there significant differences in students' triggered situational interest when solving personalized problems compared to non-personalized problems but they perceived the personalized problems to be more comprehensible than the non-personalized problems.
For future studies, a longitudinal study should be conducted. Additionally, the algorithm canbe improved.
Students found the system usable. They were able to comprehend the problems more efficiently than non-personalized math word problems, revealing that familiar content helps students make connections between their experience and learning materials.