Development and evaluation on a wireless multi-gas-sensors system for improving traceability and transparency of table grape cold chain

There is increasing requirement to improve traceability and transparency of table grapes cold chain. Key traceability indicators including temperature, humidity and gas microenvironments (e.g., CO2, O2, and SO2) based on table grape cold chain management need to be monitored and controlled. This paper presents a Wireless Multi-Gas-Sensors System (WGS2) as an effective real-time cold chain monitoring system, which consists of three units: (1) the WMN which applies the 433 MHz as the radio frequency to increase the transmission performance and forms a wireless sensor network; (2) the WAN which serves as the intermediary to connect the users and the sensor nodes to keep the sensor data without delay by the GPRS remote transmission module; (3) the signal processing unit which contains embedded software to drive the hardware to normal operation and shelf life prediction for table grapes. Then the study evaluates the WGS2 in a cold chain scenario and analyses the monitoring data. The results show that the WGS2 is effective in monitoring quality, and improving transparency and traceability of table grape cold chains. Its deploy ability and efficiency in implantation can enable the establishment of a more efficient, transparent and traceable table grape supply chain.N/

Design and Implementation of Anonymized Social Network-based Mobile Game System for Learning Mathematics

Group work and student collaboration during problem solving sessions are teaching methods which positively affect learning outcomes and socialisation. It is extremely complex to find a way of applying these methods to make them appropriate for and interesting to the new digital generation of students. This paper proposes a model which enables social network collaboration between primary school students within the system for mobile game-based learning of mathematics. It also suggests technology and proposes a general model which enables researchers to access anonymized data, teachers to keep track of student progress, and students to keep track of their own progress relative to other students, all at the same time. A microblogging social network service is integrated in the system in a way that enables sending messages without additional authentication and thus facilitates dynamics of the system. The proposed model enables mining of anonymized data streams originating from both the game and the social network. In this paper the model is used for the analysis of concepts which students most often publish, and for the analysis of their correlation with other activities within the system. Social network posts are analysed with the aim to detect students capable of taking advanced classes which cover more complex areas than the regular curriculum

Cognitive Predispositions of Students for STEM Success and Differences in Solving Problems in the Computer Game for Learning Mathematics

STEM education forms a basis for an innovation-based society, and Mathematics is, besides being an integral part of STEM, also a prerequisite for success in mastering remaining STEM constituents. With the aim of early detection of gifted students, who would be able to follow advanced forms of teaching and be successful in STEM, this paper analyses cognitive predispositions of students gifted for Mathematics and the differences in their ways of solving problem tasks in the computer game for learning primary school Mathematics. Additionally, the paper analyses success related to finishing different levels of the game

Implementing M-Learning System for Learning Mathematics Through Computer Games and Applying Neural Networks for Content Similarity Analysis of an Integrated Social Network

In order to make e-learning systems more readily available for use, the majority of new systems are being developed in a form suitable for mobile learning, i.e. m-learning. The paper puts focus on the parts of the implementation of an e-learning system which is not restricted to desktop platforms, but works equally well on smartphones and tablets in the form of m-learning. The implemented system uses educational computer games for learning Mathematics in primary schools and has an integrated social network, which is used for communication and publishing of the content related to the game. Besides analysing the platforms used for accessing the system (desktop/mobile), since students are given a choice, the paper also questions how to interpret messages when they contain concepts in student jargon or generally unknown to teachers, and shows that these messages can be interpreted by applying neural networks

Motivational Elements in Computer Games for Learning Mathematics

One of the main drawbacks of delivering new teaching lessons in e-learning systems is the lack of motivation for using those systems. This paper analyses which elements of computer games for learning mathematics have a beneficial effect on intrinsic motivation and give students continuous feedback in order to improve the learning process. While the control group has access to the basic version of the educational computer game, the experimental group uses the version enriched with additional motivational elements which include enhanced graphics for indulging in the game, messages of support while playing the game, and the possibility to compare results with fellow peers in terms of trophies and medals won

Detecting Students Gifted in Mathematics with Stream Mining and Concept Drift Based M-Learning Models Integrating Educational Computer Games

One of the problems of individualized classes which adapt contents and methods of teaching to students of different cognitive capabilities is early and widely available detection of students gifted in certain educational fields. The paper proposes models which are based on stream mining and which can detect students gifted in Mathematics solely on the basis of their interaction with the m-learning system using educational computer games and with no access to any other feature except for student age. Classification accuracy and time-efficiency of different feature selection methods are examined in order to make the models more interpretable, hence less complex. Stream mining classification accuracy in the utilized models is evaluated on new (yet unseen) records, while the concept drift detection analyses at which point of time should new models be built