Abstract
In order to promote the practice of co-creation, a real-time collaboration (RTC) version of the popular block-based programming (BBP) learning environment, MIT App Inventor (MAI), was proposed and implemented. RTC overcomes challenges related to non-collocated group work, thus lowering barriers to cross-region and multi-user collaborative software development. An empirical study probed into the differential impact on self-efficacy and collaborative behavior of learners in the environment depending upon their disciplinary background. The study serves as an example of the use of learning analytics to explore the frequent behavior patterns of adult learners, in this case specifically while performing BBP in MAI integrated with RTC. This study compares behavior patterns that are collaborative or individual that occurred on the platform, and investigates the effects of collaboration on learners working within the RTC depending on whether they were CS-majors or not. We highlight advantages of the new MAI design during multi-user programming in the online RTC based on the connections between the interface design and BBP as illustrated by two significant behavior patterns found in this instructional experiment. First, the multi-user programming in the RTC allowed multiple tasks to happen at the same time, which promoted engagement in joint behavior. For example, one user arranged components in the interface design while another dragged blocks to complete the program. Second, this study confirmed that the Computer Programming Self-Efficacy (CPSE) was similar for individual and multi-user programming overall. The CPSE of the homogeneous CS-major groups engaged in programming within the RTC was higher than that of the homogeneous non-CS-major groups and heterogeneous groups. There was no significant difference between the CPSE of the homogenous non-CS group and the CPSE of the heterogeneous groups, regardless of whether they were engaged in individual programming or collaborative programming within their groups. The results of the study support the value of engaging with MAI collaboratively, especially for CS-majors, and suggest directions for future work in RTC design.
Highlights
Computer-supported collaborative learning (CSCL) and computational thinking (CT) are regarded as crucial skills today (Stahl et al, 2006)
We present the results of the experiments in the following sequence: the behavior patterns, the behavior patterns of variously distinguished groups, and the Computer Programming Self-Efficacy (CPSE) scale
After the users collaborated to program with Real-Time Collaboration (RTC), the results indicated that the population mean CPSE was significantly different for the three types of grouping in collaborative learning based on the Kruskal-Wallis Test (The chi-squared test of groups =6.380*; p=0. 041
Summary
Computer-supported collaborative learning (CSCL) and computational thinking (CT) are regarded as crucial skills today (Stahl et al, 2006). This study aims to explore a teaching method characterized by the use of a Real-Time Collaboration (RTC) system integrated with a software development environment previously popularized as a platform aimed at supporting students learning individually. Whether it is to develop programming ability or to participate in other large scale projects, opportunities for teamwork are both frequent and indispensable. It is important for the individual to develop an understanding of CT to enable problem solving, but it is crucial to do so while having the opportunity to collaborate with others, in part to overcome various challenges that have been noted in the literature (Cen et al, 2016)
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