diff --git a/progprac.png b/progprac.png new file mode 100755 index 0000000..9e553ab Binary files /dev/null and b/progprac.png differ diff --git a/slides.tex b/slides.tex index 2e27221..a620dad 100644 --- a/slides.tex +++ b/slides.tex @@ -3,6 +3,7 @@ \usepackage{tabularx} \usepackage{booktabs} \usepackage{csquotes} +\usepackage{graphicx} \usepackage{xcolor} \newcommand\todo[1]{\textcolor{red}{#1}} @@ -25,16 +26,17 @@ linkcolor=blue, filecolor=magenta, urlcolor=blue, - pdftitle={Overleaf Example}, + pdftitle={PhD Interview}, pdfpagemode=FullScreen, } % Referencing \bibliographystyle{IEEEtran} -\title{PhD Interview Presentation} +\title{Can the use of a virtual avatar and scaffolding with AI agents improve +first-stage programming students' debugging ability?} %\subtitle{aa} -%\date{Week 1} +\date{23rd of April 2026} \author{Warwick New} \begin{document} @@ -44,22 +46,40 @@ \begin{frame} \frametitle{What is the problem space?} - Students self perceptions about their programming ability largely affects - several factors in deciding to continue learning to program \cite{lewis2011}. + Novices tend to struggle with programming fundamentals, finding the basics + quite difficult to learn, and so introductory courses tend to have poor rates + of retention. + \newline + + Students self perceptions about their programming ability affects student + retention and increases the rate of students that drop out \cite{lewis2011} in + their first stage. \newline A particular point of frustration for novice programmers is attempting to debug without thinking of the problem critically \cite[p.~23]{vickers2008}. \newline - Recent literature has identified this as a problem space that generative AI - can attempt to help resolve. Renzella et al's work \cite{renzella2025} - demonstrates that there are aspects of learning that traditionally could only - be fulfilled by an instructor outside highly controlled learning - environments (like Gidget \cite{Lee2014}) that can be partially automated with - generative AI. And therefore can be made more available to students outside - time tabled sessions. - %in this case reducing the cognitive load \cite{sweller1988} of reading and understanding compiler messages by providing more human-readable descriptions of compiler errors. + Previous interventions in this space have shown promise such as Gidget + \cite{Lee2014} that has used personification of compilers to great effect and + Metacodenition \cite{Pechorina2023} which helps the student create a mental + model about actions to take when a bug appears. + \newline + + Recent literature has begun exploring the use of LLMs but out of the box, + found them in most cases to be worse than the alternatives. + \cite{Pechorina2023}. Though recent literature has found that there is a + benefit to providing code explanation outside business hours + \cite{Renzella2025}. + + %Recent literature has identified this as a problem space that AI + %can attempt to help resolve. Renzella et al's work \cite{renzella2025} + %demonstrates that there are aspects of learning that traditionally could only + %be fulfilled by an instructor outside highly controlled learning + %environments (like Gidget \cite{Lee2014}) that can be partially automated with + %generative AI. And therefore can be made more available to students outside + %timetabled sessions. + %%in this case reducing the cognitive load \cite{sweller1988} of reading and understanding compiler messages by providing more human-readable descriptions of compiler errors. \end{frame} %\begin{frame} @@ -90,22 +110,29 @@ \begin{frame} \frametitle{What is the Gap} - Use of generative AI to learn how to debug isn't new in research. But there - are some lessons learned from educational programming interventions applied in - tools that could be made more accessible outside of sessions and scaled to a - wider range of programming environments that we could apply, which has the - potential to improve self-efficacy within the student base. Including: + {\footnotesize + Two practices in helping students learn to debug have shown great promise when + it comes to improving self-efficacy and lowering programming anxiety. I + believe that we could use recent advances in AI to augment aspects of these + interventions. \begin{itemize} - \item Personification of programming tools as fallible and encouraging, as - presented by Lee et al \cite{Lee2011}, is shown to have a positive impact - on learning motivation and success. - \item Scaffolding what steps to take when you encounter a bug and - encouraging the student to think through them one step at a time, improves - self-efficacy and productivity as demonstrated in Pechorina et al's work - on Metacodenition \cite{Pechorina2023}. + \item Personification of programming tools as fallible and encouraging, is + shown to have a positive impact on learning motivation and success as + shown in Gidget \cite{Lee2011}. + \item Scaffolding what steps to take when encountering a bug, improves + self-efficacy by lowering cognitive load as demonstrated Metacodenition + \cite{Pechorina2023}. \end{itemize} - Both of these works are restriceted to very specific environments limiting - their application on real world coding projects. + %Both of these works are restriceted to very specific environments limiting their application on real world coding projects. + \begin{figure} + \centering + \includegraphics[width=0.45\textwidth]{progprac} + \caption{Figure 2 provided by Scott et al. \cite{Scott2014}} + \label{fig:question} + \end{figure} + This figure shows how programming self-concept influences programming anxiety + and increasing time on task, retaining more computing students. + } \end{frame} %\begin{frame} @@ -182,10 +209,14 @@ cognitive load \cite{Sweller1988}. \newline - This will be done by walking a student through any issues a - real programming environment can throw at them with the use of an AI agent - based avatar, which will break down the process of solving debugging issues in - their own real world programming environment. + This will be done by walking a student through any issues a real programming + environment can throw at them with the use of a conversational AI agent based + avatar, which will break down the process of solving debugging issues in their + own real world programming environment. + + The key feature of the agent being that it has been created with pedagogic + strategy in mind, namely scaffolding and personality to see if AI can help + fill these gaps when structured effectively. \end{frame} \begin{frame} @@ -207,8 +238,8 @@ that has then since gained access to the artefact in a longitudinal study \cite[p.~224]{Coe2025}. \begin{itemize} - \item Cohort A -- Pre-intervention Cohort - \item Cohort B -- intervention Cohort + \item Year 1 -- Pre-intervention Cohort + \item Year 2 -- intervention Cohort \end{itemize} \end{frame}