It is common cases when students need a remedial learning process because they do not meet the learning achievement standards. The remedial process requires time and effort for both teacher and student. Some teachers prefer to lets their student doing self-directed learning. The teacher gives the material and direction in the classroom, then the students learn off-class. The face-to-face process is again conducted to evaluate the students’ achievements. This learning method called with the flipped-class learning method. The method needs learning media that are appropriate for the self-directed learning process. This study aimed to develop a mobile application that can be used as self-directed learning media. Learn Arabic Language App (LALA) was developed by adopting the learning process in regular face-to-face class which is listening, understanding words/sentences meaning and writing in Arabic. LALA was tested for its usability (ease of use) to 16 students of a 4th-grade elementary school. The testing results show that LALA is very easy to use tool for self-directed learning. From the LALA development process, it is known the potential of mobile applications and game elements that can be designed and developed for self-directed learning
There is a gap in the learning process of Computer Network in Informatics Education, Sebelas Maret University between Students from High School and Vocational High School. Some students with high school backgrounds do not have the basic concepts of Computer Networking, while students from SMK are already at a much more advanced stage. To bridge the thing then one solution that can be implemented is e-learning that offers independent learning system. E-learning developed will provide complete material, practice and video tutorials. In these research, unlike E-Learning that is the same (flat) for each user, E-Learning will be developed equipped with Machine Learning Naïve Bayes method. Machine Learning will help the learning process with a one-to-one approach, where the system will have the ability to detect the ability of students using E-Learning. The system adapts the material and exercises according to the user’s ability in the independent learning process. In this development, it is still at the functional testing stage with the Blackbox method by the developer. Based on the testing conducted by the researcher, it resulted that all functions in the Value Processing Information System were running well. With a step-by-step tutorial and a different approach in the learning process, each student is expected to be able to improve students’ understanding of Computer Network courses in PTIK, FKIP, UNS.
There has been recent attention to science, technology, engineering, and math (STEM) learning for early age children. Furthermore, the development of new technology learning standards and methods for integrating it into early age children education have been improved. Teacher-centred learning, information through lectures, relies on the use of whiteboards or reading materials recommended by teachers, causes passive students and such an approach can not address the challenges of the global economic era in educational processes and lacks equipping students with 21st-century skills needed recently. This paper reports preliminary findings on the robotics learning tools such as Arduino-based driverless vehicle technology influences student’ perception of the 21st-century skills pertinent to technological and vocational education and training (TVET) learning circumstances using a descriptive-qualitative research design. Two out of 100 target participants involved in this research, purposive sampling trough students with TVET learning environment. Interview such as open-ended and closed interview used to collecting the data and resulting in findings that indicate the correlation between student’s achievement on 21st-century skills and constructivist learning environment.
This research is aimed to test the originality of paper currency using detection system based on Linear Vector X1 Quantization (LVQ) Neural Network method. The input image of the system is the âdancerâ object image of paper currency Rp. 50.000,- fluorescent by ultraviolet light. The coding was carried out using visual programming language. The featureâs size of the dancer tested object is 114×90 px and the Red-Green-Blue-Hue- X2 Saturation-Intensity (RGBHSI) values were extracted as the input for LVQ. The experimental result shows that the system has an accuracy 100% of detecting 20 real test case data, and 96% of detecting 22 simulated test case data.