Zoom vs. Google Classroom: Which is likely more effective for supporting students’ learning in mathematics?

[English]: The purpose of this study is to predict the probability of successful mathematics online learning using two platforms; Zoom Meeting and Google Classroom, towards students’ learning outcomes. The sample used was sixty-four grade 11 students. Data was collected through a test on matrix topics and then analyzed using probit regression. The results of the analysis show that the student's probability of success to achieve better learning outcomes using Zoom Meeting is 12.46% higher than Google Classroom. In this case, Zoom Meeting can be used as a virtual face-to-face platform, so that the teaching and learning process can be more communicative and interactive compared to Google Classroom, where its use is limited to the delivery of learning content only. Therefore, online learning using Zoom Meeting in mathematics is more recommended because it has a higher chance of improving students’ learning outcomes. [Bahasa]: Penelitian ini bertujuan memprediksi peluang keberhasilan pembelajaran daring matematika menggunakan platform Zoom Meeting dan Google Classroom terhadap hasil belajar siswa. Sampel yang digunakan sebanyak 64 siswa kelas XI. Pengambilan data dilakukan dengan tes hasil belajar pada materi matriks kemudian dianalisis menggunakan regresi probit. Berdasarkan hasil analisis, diperoleh peluang sukses siswa terhadap hasil belajar menggunakan platform Zoom Meeting 12,46% lebih tinggi dibandingkan dengan Google Classroom. Zoom Meeting merupakan platform yang dapat digunakan sebagai sarana tatap muka maya, sehingga pembelajaran dapat lebih komunikatif dan interaktif dibandingkan dengan Google Classroom yang penggunaannya dibatasi untuk penyampaian konten pembelajaran saja. Dengan demikian, pembelajaran daring menggunakan Zoom Meeting lebih direkomendasikan karena berpeluang lebih tinggi untuk meningkatkan hasil belajar matematika siswa.

The development and testing of Thai facial soft tissue thickness data in three-dimensional computerized forensic facial reconstruction

Forensic facial reconstruction is a useful tool to assist the public in recognizing human remains, leading to positive forensic investigation outcomes. To reproduce a virtual face, facial soft tissue thickness is one of the major guidelines to reach the accuracy and reliability for three-dimensional computerized facial reconstruction, a method that is making a significant contribution to improving forensic investigation and identification. This study aimed to develop a facial soft tissue thickness dataset for a Thai population, and test its reliability in the context of facial reconstruction. Three-dimensional facial reconstruction was conducted on four skulls (2 males and 2 females, with ages ranging between 51 to 60 years). Two main tools of three-dimensional computer animation and modeling software—Blender and Autodesk Maya—were used to rebuild the three-dimensional virtual face. The three-dimensional coordinate ( x, y, z) cutaneous landmarks on the mesh templates were aligned homologous to the facial soft tissue thickness markers on the three-dimensional skull model. The final three-dimensional virtual face was compared to the target frontal photograph using face pool comparison. Four three-dimensional virtual faces were matched at low to moderate levels, ranging from 30% to 70% accuracy. These results demonstrate that the facial soft tissue thickness database of a Thai population applied in this study could be useful for three-dimensional computerized facial reconstruction purposes.

MODEL KOOPERATIF WHO AM I MENGGUNAKAN ZOOM MENDORONG LITERASI ANAK DALAM PEMBELAJARAN DARING

Technological advances can help learning during the Covid-19 pandemic. The development of internet-based technology provides zoom video conference applications. The use of zoom helps teachers and students in virtual face-to-face interactions separated by space and distance. The Who Am I cooperative model uses zoom meetings to stimulate student literacy in online learning so as to increase literacy during the Covid 19 pandemic. Literacy stimulation is an inner drive (without coercion) to carry out reading and writing activities. Who Am I model that uses game tournaments as a learning-while-playing strategy. Learning while playing is able to provide a good stimulus for literacy activities. The characteristics of the Who Am I learning model provide a stimulus to literacy. These characteristics are: 1) learning in groups, 2) learning while playing, 3) fun learning, 4) learning vocabulary mastery.

INOVASI PENDIDIKAN ISLAM MASA PANDEMI COVID 19 DI MA AL URWATUL WUTSQO JOMBANG

Pandemic of Coronavirus Disease (Covid-19) in Indonesia has changed all education models, including in Jombang. Many problems changes for school education. Such as teachers are facing hard challenges. Educators are required to be creative in delivering material through online learning media. It also needs to be invincible with the level of educational needs. MA Al Urwatul Wutsqo Jombang makes effort to deliver Islamic education even in pandemic conditions. This is done through online and offline. The online or distance learning has been carried out in several ways, namely the delivery of materials and assignments through the e-learning program, email, What Sapp and using virtual face through zoom meeting application. Meanwhile the offline is done by taking an assignment in the morning at school and collected on the following day, then next is limited meeting that is reducing face-to-face hours, and designing classes based on the health protocols.

Implementing Conversational AI in Libraries: A Practical Approach

The outbreak of the present pandemic Coronavirus (official designation COVID-19 or 2019-nCov) forced us to adopt “new normal” and give a greater use and reliance on virtual space than ever before with physical spaces. In the context of the library, shifting of the user assistant programme from physical to virtual face a problem in this transition. This paper based on the probable solution with the use of practical implementation of conversational AI i.e. “Bot” or “Chatbot” to fulfil user needs 24*7 without human intervention. A chatbot is a computer program that can simulate conversation and interact with humans — spoken, written or both. It acquired a set of pre-programmed commands and continue learning based on the inputs it receives. This paper gives a general overview of conversational AI, Chatbot and its multitasking features and a practical overview of the implementation of one of the famous chatbot provider Kore.ai using its free to use plan. Additionally, the paper explained the requirement of the present situation & long term benefits of chatbot in the library.

DELIVERING MESSAGES THROUGH COMIC STRIP AS LEARNING MEDIA: An Analysis of Message Forms

Communication is an activity that cannot be stopped in human life. Especially during the Covid -19 pandemic, face-to-face communication is difficult. This is also quite influential in the world of education where during the Covid -19 pandemic there were many face-to-face meetings between lecturers and students through virtual face to face. In the learning process, sometimes students are embarrassed or reluctant to express their opinions. In this study, researchers tried to use comic strip learning media as a medium for conveying opinions from students. This study used qualitative method with data collection obtained from the analysis of the form of messages from comic strip was made by students. The purpose of this study is to determine the types of messages made by students through comic strip media with educational themes. From the data, it shows that 21 comic strips have informative messages, namely 52.5%, and 19 comic strips that have persuasive form messages or 47.5% of the percentage. While the coercive message form shows the number 0. From this study it can be concluded that comic strips can be an alternative learning medium in the process of delivering messages or opinions.

Auxiliary Dictionary of Diversity Learning for Face Recognition with a Single Sample Per Person

Face recognition for a single sample per person is challenging due to the lack of sufficient sample information. However, using generic training set to learn an auxiliary dictionary is an effective way to alleviate this problem. Considering generic training sample of diversity, we proposed an algorithm of auxiliary dictionary of diversity learning (ADDL). We first produced virtual face images by mirror images, square block occlusion and grey transform, and then learned an auxiliary dictionary of diversity using a designed objective function. Considering patch-based method can reduce the influence of variations, we seek extended sparse representation with l2-minimization for each probe patch. Experimental results in the CMUPIE, Extended Yale B and LFW datasets demonstrate that ADDL performs better than other related algorithms.