Section 5 shows our study design and subject profile and in Sect. 4, we present related work concerning visual debuggers. 3, which deals with direct manipulation and development environments. We, then present the theoretical background in Sect. The structure of this paper is as follows: we show some aspects of deaf learning in Sect. Besides, the analysis of the results is more robust and consistent: the statistical test in the previous work was not very suitable for the experimental design and sample size furthermore, we show, in this article, a qualitative analysis of the studied tools which allows us to understand what the DHI feel and think on the use of visual tools. In this way, we can justify more appropriately the use of visual tools in the deaf learning. However, in this paper, we show some aspects of the teaching of DHI. The answers submitted to the Mann-Whitney U-Test give us a p-value of 0.01, a statistically significant result, thus we can conclude that JGrasp was better evaluated, usability-wise.Ī simular study is presented in. The average SUS score for JGrasp was 72 and 50 for Eclipse. We can conclude that the JGrasp makes use of elements that allow a better familiarization of the features available.Ī questionnaire based on the System Usability Scale (SUS) was also applied. But, although the Eclipse tool may have be benefited due to the background of the participants, the TCT, HA and TCS variables show a similar performance between the two tools, with JGrasp showing some advantage.
These metrics were submitted to the Mann-Whitney U-Test, the results were not statistically significant at \(p \le 0.05\). Performance was measured by: (a) Time to complete the task (TCT) (b) Number of times the subject asked for external help assistance (HA) and (c) Number of tasks completed successfully (TCS). We emphasize that all subjects have already had contact with the Eclipse tool for debugging activities in our course.
#JGRASP VS ECLIPSE CODE#
Ten participants from our basic Java course were recruited to debug code in JGrasp and Eclipse, in a between-subjects experiment. We compare how a visual debugger (JGrasp) affects the activities of a DHI programmer in this paper. We expect visual debuggers and direct manipulation might improve the performance of the DHI programmer. We know that DHI graduates from our courses has inferior performance in debugging tasks when compared to their hearing counterparts who took the very same courses. Such collaboration is part of our strategy to create and promote a collaborative environment between a DHI programmer and a hearing coworker (tutor is not versed in sign language). Java workshops are implemented on LMS, which allows online collaboration between a tutor, a translator and the DHI. Some lessons on our Java course are reinforced by programming exercises (or programming workshops).
#JGRASP VS ECLIPSE SOFTWARE#
We are interested in empowering the DHI programmer in the daily tasks of a regular software engineer, such as software evolution, debugging. Although our LMS is equipped for the DHI and those with missing limbs, the focus of this text is on the DHI java graduate. The Laboratory of Distance Education for People with Disabilities creates and offers seven courses in information technology (IT) through our accessible learning management system (LMS), among them a basic Java course using the industry-standard Eclipse programming environment. To secure a position in the workplace for deaf or hearing impaired (DHI) programmers, studies must show that they have performance similar to their hearing counterparts.
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