Applying temporal chunk signals analysis to measure programming competence by the transcription of Java program code

Albehaijan, Noorah Abdullah (2022) Applying temporal chunk signals analysis to measure programming competence by the transcription of Java program code. Doctoral thesis (PhD), University of Sussex.

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This thesis investigates the basis for a novel method of quickly and efficiently assessing programming comprehension. It investigates the feasibility of assessing learners’ mental chunk structures, and their temporal chunk signals, as a way of measuring their competence. The focus is on the Java programming language. The thesis investigates the feasibility of chunk-based measures in two different simple transcription tasks: view display, where stimulus is visible at all times; and hide and show, where the stimulus is only made visible when a participant presses a special button. University computer science students and faculty are the target group. Chunking theory is utilised to define three chunking measures of competence and to anticipate how they would vary across participants with different degrees of Java competence. The measures are as follows: (1) the number of characters transcribed per view (or the number of views) of the Java program code; (2) the time spent writing between the views; and (3) the duration of pauses before writing each written character. Ninety-six participants participated in the three experiments, transcribing on graphics tablets in experimental settings, and evidence of chunking’s essential role in transcription tasks was revealed. Significant relationships were discovered between the chunking measures of competence and independent measures of Java competence (Java familiarity scores and students’ final test marks (for the third experiment)). The third experiment included a longitudinal post-test component spanning three months of learning, in which changes to the mean scores in characters per view, writing-times, and pauses reflected the students’ amount of learning.

Item Type: Thesis (Doctoral)
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science > QA0076.6 Programming
Depositing User: Library Cataloguing
Date Deposited: 17 Jun 2022 13:12
Last Modified: 17 Jun 2022 13:12

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