Investigating Novice Programmers' Mental Models

 Syeda Fatema Mazumder

Wednesday February 21, 2024 11:00a-12:00p

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Novice programmers are known for holding incomplete and inconsistent mental models. A mental model stores knowledge that reflects a person's belief system, helps determine actions, and facilitates learning. Mental model correctness and consistency are two criteria that make a mental model useful. Though the literature on mental models is rich with more than two decades of research, novice programmers' mental model is understudied in the CS education research community. Guided by the mental model theories from psychology and cognitive science, I investigated novice programmers' mental models of arrays before and after CS1 course instruction. Furthermore, I explored the gap that might exist between students with varying levels of prior programming experience. To that end, by following the theories of mental models, I defined the mental models for Java arrays, including assertions of the array's parts and state changes. I further decomposed the array's parts and state changes into four sub-components each (parts: name, index, type, elements}; state changes: declaration, instantiation, assigning literals, assignment). To elicit the mental model assertions of novice programmers from large CS1 classrooms, I adopted a multiple choice-based questionnaire approach (the Mental Model Test) covering each array's component. I collected responses from novice programmers as they entered a CS1 course and transitioned into a CS2 course. I analyzed participants' mental model assertions based on their correctness and consistency. 

The results show that participants' mental model correctness and consistency improved after formal classroom instruction. Moreover, even though improved, I found evidence that the mental model components of the array's state changes were less accurate and consistent than the parts. In addition, participants with prior programming experience had significantly lower mental model correctness and consistency than those with prior programming experience before classroom instruction on arrays. The mental model test highlighted several novice programmers' misconceptions. Over half of our participants held at least one misconception before and after learning arrays in classrooms. Novice programmers mostly held misconceptions about the arrays’ declarations (state change) as incoming CS1 students and when transitioning into CS2. After classroom instruction, the number of students holding misconceptions about the parts components decreased. However, for the state changes components, in most cases, the number of students holding misconceptions remained almost the same even after classroom instruction. I close my dissertation by summarizing the overall findings while investigating novice programmers' mental models in their different learning trajectories. Lastly, I discuss the implications of my research in designing instructional materials for CS educators on possible solutions to mitigate the mental model gap of novice programmers.

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Black Research Support Network: Studying Change By, With, and For Black Undergraduate Computer Science Faculty & Students

 Dr. Marlon Mejias and Dr. Dale-Marie Wilson

View Marlon's website: https://cci.charlotte.edu/directory/marlon-mejias

Thursday November 9, 2023 12:30p-1:30p

This talk describes a holistic research experience for Black undergraduate students that was developed to help them obtain research experience while also addressing psychological and social challenges potentially encountered while developing a Black computing research identity. The BRSN group is a multi institutional research group co-directed by Black computing faculty and a Ph.D. program coordinator from two Historically Black Colleges and Universities and a Predominately White Institution. The research experience is grounded in Black liberatory and social justice perspectives which provides culturally and socially relevant research mentorship geared towards increasing students’ sense of belonging, self-confidence and knowledge of graduate school opportunities while cultivating their research skills.

Assessing Equity in Computer Science Education in Charlotte-Mecklenburg Schools: 

Our Research Questions and Approach

Dr. Xiaoxia Newton and Dr. Audrey Rorrer

View Xiaoxia's Website Here: https://edld.charlotte.edu/directory/xiaoxia-newton-0/

 View Audrey's Website Here:  https://www.audreysrorrer.com/

Wednesday October 18, 2023 4:00 p-5:00 p

This one year project will explore whether and how computer science learning opportunities in Charlotte might vary depending on factors like student population, school characteristics, and community wealth. The research will utilize integrated data leveraged through the Charlotte Regional Data Trust, which includes longitudinal data on CMS schools, DSS Economic Services, Homeless Management Information Systems, and Crisis Assistance Ministry. The research will apply a pre-established model called the CAPE framework — measuring students’ capacity for, access to, participation in and experience of computing coursework — to assess equity in Charlotte’s computer science education. The ultimate goal is identifying common obstacles to a computing career path in the hope of helping more students from underrepresented backgrounds gain a valuable skill set that could unlock economic opportunity later in life.

View Xiaoxia and Audrey Talk here: https://youtu.be/suA9DGKJXnA