Gaze collaboration patterns of successful and unsuccessful programming pairs using cross-recurrence quantification analysis

A dual eye tracking experiment was performed on pairs of novice programmers as they traced and debugged fragments of code. These programming pairs were categorized into successful and unsuccessful pairs based on their debugging scores. Cross-recurrence quantification analysis (CRQA), an analysis using cross-recurrence plots (CRP), was used to determine whether there are significant differences in the gaze collaboration patterns between these pair categories. Results showed that successful and unsuccessful pairs can be characterized distinctively based on their CRPs and CRQA metrics. This study also attempted to interpret the CRQA metrics in relation to how the pairs collaborated in order to provide a somewhat clear picture of their relevance and meaning. The analysis results could serve as a precursor in helping us understand what makes a programming pair more successful over other pairs and what behaviors exhibited by unsuccessful pairs that should be avoided

Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis

Pair programming is a model of collaborative learning. It has become a well-known pedagogical practice in teaching introductory programming courses because of its potential benefits to students. This study aims to investigate pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. This research used eye-tracking methodologies and techniques such as cross-recurrence quantification analysis. The potential indicators for pair success were used to create a model for predicting pair success. Findings suggest that it is possible to create a model capable of predicting pair success in the context of pair programming. The predictors for the pair success model that can obtain the best performance are the pairs\u27 proficiency level and degree of acquaintanceship. This was achieved using an ensemble algorithm such as Gradient Boosted Trees. The performance of the pairs is largely determined by the proficiency level of the individuals in the pairs; hence, it is recommended that the struggling students be paired with someone who is considered proficient in programming and with whom the struggling student is comfortable working with

Characterizing individual and pair gaze patterns in a pair program tracing and debugging eye tracking experiment

This study investigated the individual patterns in the pairs as well as pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. Eye tracking methodologies and cross-recurrence quantification analysis were used to analyze these patterns. Machine learning techniques were employed to create models capable of predicting individual success in the pairs and pair success.Findings suggest that a successful collaboration is characterized as having individuals in the pairs who are always on-task, acquaint themselves first with the program, strike a good balance of encoding information and searching for errors, and who can quickly associate with program elements. A pair has better chances of succeeding when the individuals being paired together are highly proficient who frequently converge and engage in a productive conversation. It is possible to create a model capable of predicting individual success in the pairs and a model that is able to predict pair success

Characterizing Individual Gaze Patterns of Pair Programming Participants

In this paper, we investigate the gaze patterns of individuals within pairs as they traced and debugged fragments of code. We performed a dual eye tracking experiment, recorded their fixations and computed the gaze-based metrics of these individuals. The participants within pairs were categorized into a more successful and less successful participant based on the number of bugs found. Results suggest that the more successful participants acquaint themselves first with the program encoding more information about the program, have more increased attention on the erroneous lines of code, strike a good balance between processing and searching, read code less linearly, and are more engaged in the task. The goal of this study is to capture individual expertise to gain insights on what makes the more skilled participants in a pair programming setup efficient and effective through their gaze patterns

Do friends collaborate and perform better?: A pair program tracing and debugging eye-tracking experiment

We characterize the extent of collaboration of pairs of novice programmers as they trace and debug fragments of code using cross-recurrence quantification analysis (CRQA). Specifically, we compare the collaboration of pairs whose composition is based solely on degree of acquaintanceship and look how friendship affects collaboration and performance. Cross- recurrent plots (CRPs) were built using the pairs禽 eye tracking data and CRQA metrics such as recurrence rate (RR), determinism (DET), average diagonal length (L), longest diagonal length (LMAX), entropy (ENTR), and laminarity (LAM) were derived from the CRPs using the CRP toolbox for MATLAB. The pairs禽 degree of collaboration was assessed based on these metrics. Findings reveal that the highly acquainted (HA) pairs collaborated better than the poorly acquainted (PA) pairs based on their percentage of recurrent fixations and matching scanpaths. However, the former禽s average performance score was lower than the latter. It was also observed that the PA pairs had steadier scanpaths compared to the HA pairs, which suggests that the PA pairs employed more logical and consistent debugging strategies that produced better scores than the HA pairs. Finally, the HA pairs were found to have struggled more in program comprehension as they had spent more time on certain regions of codes resulting to a lower average performance score compared to the PA pairs

Characterizing Collaboration in the Pair Program Tracing and Debugging Eye-Tracking Experiment: A Preliminary Analysis.

This paper characterized the extent of collaboration of pairs of novice programmers as they traced and debugged fragments of code using cross-recurrence quantification analysis (CRQA). This was a preliminary analysis that specifically aimed to compare and assess the collaboration of pairs consisting of two individuals who may have different or same level of prior knowledge given a task. We performed a CRQA to build cross-recurrence plots using eye tracking data and computed for the CRQA metrics, such as recurrence rate (RR), determinism (DET), average diagonal length (L), longest diagonal length (LMAX), entropy (ENTR), and laminarity (LAM) using the CRP toolbox for MATLAB. Results showed that low prior knowledge pairs (BL) collaborated better compared to high prior knowledge pairs (BH) and mixed prior knowledge (M) pairs because of its high RR and DET implying that they had more recurrent fixations and matching scanpaths. However, the BL pairs\u27 high ENTR and LAM could mean that they seemed to have more difficulty in understanding and debugging the programs. All pairs regardless of category had more or less exerted the same level of attunement when asked to debug the programs as evident in their L values. The mixed pairs seemed to have struggled with eye coordination the most as it had the most incidences of low LMAX

Museums 2.0: Advancing Decolonizing and Participatory Approaches to Developing Museum Websites in the Global South

Web 2.0 and its participation-based culture offer rich possibilities for developing websites with museums in the Global South museum. It also offers spaces for virtual learning, and representing and expressing one’s culture. However, technology and partnerships between the Global North and South have a recolonizing potential. Decybercolonizing viewpoints and methods are needed to create web spaces for Global South museums. They bring attention to virtual spaces that can be colonized by the ideologies of the Global North.Knowledge Mobilization at York York’s Knowledge Mobilization Unit provides services for faculty, graduate students, community and government seeking to maximize the impact of academic research and expertise on public policy, social programming, and professional practice. This summary has been supported by the Office of the Vice-President Research and Innovation at York and project funding from SSHRC and CIHR. [email protected] www.researchimpact.c

Impact of Both Prior Knowledge and Acquaintanceship on Collaboration and Performance: A Pair Program Tracing and Debugging Eye-Tracking Experiment

We compared the collaboration of pairs whose composition was based on both prior knowledge and degree of acquaintanceship as they traced and debugged fragments of code. We performed a cross-recurrence quantification analysis (CRQA) to build cross-recurrence plots using the eye tracking data and computed for the CRQA metrics, such as recurrence rate (RR), determinism (DET), entropy (ENTR), and laminarity (LAM) using the CRP toolbox for MATLAB. Findings revealed that high prior knowledge pairs who were poorly acquainted (BH/PA) performed better among categories despite having collaborated the least. This confirmed the findings of prior studies that skilled strangers perform best. Mixed prior knowledge pairs who were highly acquainted (M/HA) collaborated the most but their familiarity did not translate to better performance. The results of this study could contribute to the learning sciences and pedagogy. If we know what makes collaboration successful as measured through their performance, we can design interventions that could facilitate the process of creating programming pairs who can collaborate and perform better

Exploring lag times in a pair tracing and debugging eye-tracking experiment

This paper investigated the leader/follower patterns that possibly occurred in a remote pair programming eye-tracking experiment. We intended to build the profile of the initiator and the follower and explore the lag times inherent to pairs categorized based on prior knowledge using the diagonal recurrence profile. Findings revealed that in a pair programming setup, the initiator was the low prior knowledge participant. We defined the “initiator” as the one who encountered a problem in the code first and hence initiated the contact to ask for help, and the “follower” was the one who responded to help. The characteristic lag times based on prior knowledge were 2.33 seconds for both high prior knowledge pairs, 1.96 seconds for both low prior knowledge pairs, and 1.51 seconds for mixed prior knowledge pairs