Measuring the usability and capability of App inventor to create mobile Applications

Xie, Benjamin; Shabir, Isra; Abelson, Harold

doi:10.1145/2824823.2824824

Cited by 33 publications

(15 citation statements)

References 9 publications

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“…As of August of 2019, there are 44,981,198 registered users on the Scratch website and 8,200,000 registered users on the App Inventor website. Both Scratch and App Inventor are educational programming languages that allow novice programmers or even young students to program easily by dragging and dropping their code blocks [5,6]. Although Scratch was originally developed for teaching young students (ages 8-16) [2], it has also been taught at the college level [7].…”

Section: Introductionmentioning

confidence: 99%

Comparing the Effectiveness of Scratch and App Inventor with Regard to Learning Computational Thinking Concepts

Park

Shin

2019

Electronics

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Scratch and App Inventor are two of the most widely used block-based programming languages for young students. These are educational languages which allow students to program easily by dragging and dropping their code blocks. One question that arises in relation to these educational languages is which of them would be more helpful in fostering computational thinking. It is difficult to answer this question because each language has its own advantages. In this paper, we propose a novel rubric based on Dr. Scratch for assessing both Scratch and App Inventor projects in terms of computational thinking concept learning. We crawled teachers’ and students’ open and popular projects and automatically calculated their effectiveness scores with regard to learning computational thinking concepts based on our rubric. The experimental results show that (1) Scratch projects scored higher on average in Parallelism, Synchronization and Flow Control, while App Inventor projects scored higher on average in User Interactivity and Data Representation. The results also show that (2) in many cases, large programs with numerous lines of code scored high in all areas of computational thinking concepts.

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Section: Introductionmentioning

confidence: 99%

Comparing the Effectiveness of Scratch and App Inventor with Regard to Learning Computational Thinking Concepts

Park

Shin

2019

Electronics

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“…The list picker interface provides the selection for Bluetooth device to be connected. Then, the mobile App starts linking with the Bluetooth transmitter with the designated IP address [6,7].…”

Section: Results and Analysismentioning

confidence: 99%

Bluetooth embedded digital ammeter with android app data logging

Soon

Teng

Tee

et al. 2020

IJEECS

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Monitoring current supplies to light emitting diode (LED) luminaires is one of the reliability tests performed manually. Bluetooth (HC-05) embedded digital ammeter was proposed to acquire currents of a badge of LED products and hence, quality can be assured. Android App was designed to remotely record the current supplies to the different models of LED products and checking if the current measured is within the allowed range. The current data can be further analysed to control the quality of the LED luminaires produced. The pass and fail-current range can be set in the digital ammeter for alerting abnormal current measured by the operator. Therefore, IOT embedded digital ammeter will help in monitoring data consistency of LED products and improve quality assurance procedure.

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“…Any sensor can be harvested. Data can be exchanged in an easy way utilizing Wi-Fi, mobile network, Bluetooth, and NFC [175]. A Google free account is the only requirement to start writing any complex app.…”

Section: How and Where To Start Digging Hidden Informationmentioning

confidence: 99%

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

et al. 2019

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Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

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Measuring the usability and capability of App inventor to create mobile Applications

Cited by 33 publications

References 9 publications

Comparing the Effectiveness of Scratch and App Inventor with Regard to Learning Computational Thinking Concepts

Comparing the Effectiveness of Scratch and App Inventor with Regard to Learning Computational Thinking Concepts

Bluetooth embedded digital ammeter with android app data logging

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

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