Pocket Labs Supported IoT Teaching

Cvjetkovic, Vladimir M

doi:10.3991/ijep.v8i2.8129

Cited by 17 publications

(19 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mono-color LEDs (single or 7-segment models) have been used to prototype user interfaces that display the state of 3-way traffic signals, the energy consumption in a 3-phase electrical system, and realtime sensor data [33], [48], [79], [90]. • Web Dashboard Interfaces: Web-based dashboards are also commonly used in IoT curricula for real-time sensor data visualizations and associated analytics [31], [34], [46], [52], [91], [92]. A reason for their widespread use is the availability of open-source JavaScript and Python-based visualization libraries [93]- [95].…”

Section: ) Key Observations About Iot Interface Layer Curriculummentioning

confidence: 99%

Internet-of-Things Curriculum, Pedagogy, and Assessment for STEM Education: A Review of Literature

et al. 2022

View full text Add to dashboard Cite

The exponentially growing Internet of Things (IoT) market presents compelling opportunities for innovators to develop and create meaningful applications affecting everyday life. This shifting IoT paradigm is placing new demands on educational institutions to train students with IoT-relevant skills. Numerous education researchers continue to document the implementation of IoT-based curriculum, pedagogy, and assessment techniques for STEM education. In this paper, we systematically reviewed this literature and identified 60 journal articles and conference papers that have reported implementing IoT curriculum and associated instructional approaches, educational technologies, and assessment strategies for K-12 and university students. The curriculum, pedagogy, and assessment strategies presented in these studies were analyzed in the context of the sensing, networking, services, and interface layers of the IoT technology paradigm. The review identifies best educational practices and synthesizes actionable strategies for educators to implement effective IoT learning experiences for students. These strategies leverage low-cost IoT hardware, open-source IoT software, active learning-based instructional approaches, and direct/indirect assessment methods. As IoT education becomes increasingly pervasive, the review and strategies provided in this paper may serve as a guide for future educational efforts.

show abstract

Section: ) Key Observations About Iot Interface Layer Curriculummentioning

confidence: 99%

Internet-of-Things Curriculum, Pedagogy, and Assessment for STEM Education: A Review of Literature

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The GA is the best solution for selecting the IoT nodes to exchange information among IoT nodes [19], [20], [21]. Also, the GA can be used for mining the intelligent objects in IoT [22], [40].…”

Section: In the Internet Of Thingsmentioning

confidence: 99%

Genetic Algorithm: Reviews, Implementations, and Applications

et al. 2020

View full text Add to dashboard Cite

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

show abstract

Section: In the Internet Of Thingsmentioning

confidence: 99%

Genetic Algorithm: Reviews, Implementations, and Applications

Alam

Qamar

Dixit

et al. 2020

Preprint

View full text Add to dashboard Cite

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation

show abstract

Pocket Labs Supported IoT Teaching

Cited by 17 publications

References 12 publications

Internet-of-Things Curriculum, Pedagogy, and Assessment for STEM Education: A Review of Literature

Internet-of-Things Curriculum, Pedagogy, and Assessment for STEM Education: A Review of Literature

Genetic Algorithm: Reviews, Implementations, and Applications

Genetic Algorithm: Reviews, Implementations, and Applications

Contact Info

Product

Resources

About