Learner analytics of student programmers: the use of innovative technologies to better understand the learning behaviours of student programmers
type: CreativeWork
description: The challenges of effective teaching in mass education environments are well documented, with one of the recurring themes being high attrition and failure rates. This is especially prevalent in third level computing courses, particularly in programming based modules. The size of these cohorts generally means that identification of struggling students is usually only at a point when meaningful interventions are too late. This thesis uses new and novel technologies to provide insights and add to the existing research into areas of learner behaviour in large-scale programming related modules. The aim is to be able to identify new Learning Analytics to be used potentially as early warning indicators of struggling students. These areas generally have thus far either not been studied or have been inaccessible or difficult to measure. Accordingly, this thesis presents a series of investigative studies into student key engagement points during a typical programming module for post graduate conversion students, including consideration of 1) seat position tracking during programming lectures, 2.) Video Lecture Capture (LC) viewing behaviours and 3.) Student Heart Rate monitoring during lectures. The first study sought to track students’ seating positions in lectures to investigate student behaviour in terms of preferred seating position and whether this is related to their final grade performances. Unlike most previous studies in this area, it did not control the students’ seating arrangements enabling an unrestricted study of the effects of lecture theatre seating choices on assessment performance. It found there was a correlation between sitting closer to the front and higher grades and assessment score. Their scores degraded the further students sat from the front. Students tended to sit in the same area out of habit throughout the module and seating was a potential early marker of prediction for module score. The LC Video analytics study sought to investigate and assess t…
abstract: The challenges of effective teaching in mass education environments are well documented, with one of the recurring themes being high attrition and failure rates. This is especially prevalent in third level computing courses, particularly in programming based modules. The size of these cohorts generally means that identification of struggling students is usually only at a point when meaningful interventions are too late. This thesis uses new and novel technologies to provide insights and add to the existing research into areas of learner behaviour in large-scale programming related modules. The aim is to be able to identify new Learning Analytics to be used potentially as early warning indicators of struggling students. These areas generally have thus far either not been studied or have been inaccessible or difficult to measure. Accordingly, this thesis presents a series of investigative studies into student key engagement points during a typical programming module for post graduate conversion students, including consideration of 1) seat position tracking during programming lectures, 2.) Video Lecture Capture (LC) viewing behaviours and 3.) Student Heart Rate monitoring during lectures. The first study sought to track students’ seating positions in lectures to investigate student behaviour in terms of preferred seating position and whether this is related to their final grade performances. Unlike most previous studies in this area, it did not control the students’ seating arrangements enabling an unrestricted study of the effects of lecture theatre seating choices on assessment performance. It found there was a correlation between sitting closer to the front and higher grades and assessment score. Their scores degraded the further students sat from the front. Students tended to sit in the same area out of habit throughout the module and seating was a potential early marker of prediction for module score. The LC Video analytics study sought to investigate and assess t…
author: McGowan, Aidan
language: eng
keywords: Machine learning, learner analytics, programming, teaching
date published: 2021-12
date modified: 2026-07-24T03:56:04Z
provider: Queen's University Belfast
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Queen's University Belfast