There has been a steady increase in the number of studies investigating educational robotics and its impact on academic and social skills of young learners. Educational robots are used both in and out of school environments to enhance K–12 students’ interest, engagement, and academic achievement in various fields of STEM education. Some prior studies show evidence for the general benefits of educational robotics as being effective in providing impactful learning experiences. However, there appears to be a need to determine the specific benefits which have been achieved through robotics implementation in K–12 formal and informal learning settings. In this study, we present a systematic review of the literature on K–12 educational robotics. Based on our review process with specific inclusion and exclusion criteria, and a repeatable method of systematic review, we found 147 studies published from the years 2000 to 2018. We classified these studies under five themes: (1) general effectiveness of educational robotics; (2) students’ learning and transfer skills; (3) creativity and motivation; (4) diversity and broadening participation; and (5) teachers’ professional development. The study outlines the research questions, presents the synthesis of literature, and discusses findings across themes. It also provides guidelines for educators, practitioners, and researchers in areas of educational robotics and STEM education, and presents dimensions of future research.

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A Systematic Review of Studies on Educational Robotics.

Empirical studies on the Maker Movement, a promising approach to learning: A literature review

Visual, block-based programming environments present an alternative way of teaching programming to novices and have proven successful in classrooms and informal learning settings. However, few studies have been able to attribute this success to specific features of the environment. In this study, we isolate the most fundamental feature of these environments, the block interface, and compare it directly to its textual counterpart. We present analysis from a study of two groups of novice programmers, one assigned to each interface, as they completed a simple programming activity. We found that while the interface did not seem to affect users' attitudes or perceived difficulty, students using the block interface spent less time off task and completed more of the activity's goals in less time.

Comparing Textual and Block Interfaces in a Novice Programming Environment

This Handbook describes the extent and shape of computing education research today. Over fifty leading researchers from academia and industry (including Google and Microsoft) have contributed chapters that together define and expand the evidence base. The foundational chapters set the field in context, articulate expertise from key disciplines, and form a practical guide for new researchers. They address what can be learned empirically, methodologically and theoretically from each area. The topic chapters explore issues that are of current interest, why they matter, and what is already known. They include discussion of motivational context, implications for practice, and open questions which might suggest future research. The authors provide an authoritative introduction to the field and is essential reading for policy makers, as well as both new and established researchers.

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The Cambridge Handbook of Computing Education Research

Novice Programmers and Introductory Programming

Educators are often seeking new ways to motivate or inspire students to learn. Our past efforts in K-12 outreach included robotics and media computation as the contexts for teaching Computer Science (CS). With the deep interest in mobile technologies among teenagers, our recent outreach has focused on using smartphones as a new context. This paper is an experience report describing our approach and observations from teaching a summer camp for high school students using App Inventor (AI). The paper describes two separate methods (one using a visual block language, and another using Java) that were taught to high school students as a way to create Android applications. We observed that initiating the instruction with the block language, and then showing the direct mapping to an equivalent Java version, assisted students in understanding app development in Java. Our evaluation of the camp includes observations of student work and artifact assessment of student projects. Although the assessment suggests the camp was successful in several areas, we present numerous lessons learned based on our own reflection on the camp content and instruction.

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Using app inventor in a K-12 summer camp

Omniscient debugging is a promising technique that relies on execution traces to enable free traversal of the states reached by a system during an execution. While some General-Purpose Languages (GPLs) already have support for omniscient debugging, developing such a complex tool for any executable Domain-Specific Modeling Language (xDSML) remains a challenging and error prone task. A solution to this problem is to define a generic omniscient debugger for all xDSMLs. However, generically supporting any xDSML both compromises the efficiency and the usability of such an approach. Our contribution relies on a partly generic omniscient debugger supported by generated domain-specific trace management facilities. Being domain-specific, these facilities are tuned to the considered xDSML for better efficiency. Usability is strengthened by providing multidimensional omniscient debugging. Results show that our approach is on average 3.0 times more efficient in memory and 5.03 more efficient in time when compared to a generic solution that copies the model at each step.

/pdf/supporting-efficient-and-advanced-omniscient-debugging-for-2c1wqoqu3c.pdf

Supporting efficient and advanced omniscient debugging for xDSMLs

This paper discusses a technique for supporting omniscient debugging for model transformations, which are used to define core operations on software and system models. Similar to software systems developed using general-purpose languages, model transformations are also subject to human error and may possess defects. Existing model-driven engineering tools provide stepwise execution to aid developers in locating and removing defects. In this paper, we describe our investigation into a technique and associated algorithms that support omniscient debugging features for model transformations. Omniscient debugging enables enhanced navigation and exploration features during a debugging session beyond those possible in a strictly stepwise execution environment. Finally, the execution time performance is comparatively evaluated against stepwise execution, and the scalability (in terms of memory usage) is empirically investigated.

Efficient and scalable omniscient debugging for model transformations

Complex systems typically involve many stakeholder groups working in a coordinated manner on different aspects of a system. In Model-Driven Engineering (MDE), stakeholders work on models in order to design, transform, simulate, and analyze the system. Therefore, there is a growing need for collaborative platforms for modelers to work together. A cloud-based system allows them to concurrently work together. This chapter presents the challenges for building such environments. It also presents the architecture of a cloud-based multi-view modeling environment based on AToMPM.

Cloud-Based Multi-View Modeling Environments

Model transformations (MTs) are central artifacts in model-driven engineering (MDE) that define core operations on models. Like other software artifacts, MTs may possess defects (bugs). Some MDE tools provide support for debugging. In this paper, we describe an omniscient debugging technique. Our technique enhances stepwise execution support for MTs by providing the ability to traverse, in either direction, the execution history of a live debugging session. We also introduce a proof of concept prototype applying the described technique and a preliminary study of the scalability, in terms of memory consumption, and performance, in terms of time to execute.

Towards Efficient and Scalabale Omniscient Debugging for Model Transformations

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