MEMBANGUN KETERLIBATAN SISWA DISEKOLAH SEBAGAI BAGIAN DARI MAKHLUK SOSIAL

Students show negative behavior in class because they are not involved in the lessons that the teacher presents. This may happen because the material presented is not at the academic level for students. Lack of involvement can occur because students do not actively participate in class activities. teachers and administrators have the opportunity to observe the classroom and the learning strategies used to engage students. After the class visit, teachers and administrators have the opportunity to discuss strategies that score high and the types of activities that increase the level of involvement. Teachers who can experience and observe what high student engagement looks like by participating in the learning practice inventory guide can be the school's best resource in educating other teachers.Siswa menunjukkan perilaku negatif di kelas karena mereka tidak terlibat dalam pelajaran yang disajikan guru. Hal ini mungkin terjadi karena materi yang disampaikan tidak berada pada jenjang akademik bagi mahasiswa. Kurangnya keterlibatan dapat terjadi karena siswa tidak secara aktif berpartisipasi dalam kegiatan kelas. guru dan administrator memiliki kesempatan untuk mengamati ruang kelas dan strategi pembelajaran yang digunakan untuk melibatkan siswa. Setelah kunjungan kelas, guru dan administrator memiliki kesempatan untuk membahas strategi yang mendapat skor tinggi dan jenis kegiatan yang meningkatkan tingkat keterlibatan. Guru yang dapat mengalami dan mengamati seperti apa keterlibatan siswa yang tinggi dengan berpartisipasi dalam panduan inventaris praktik pembelajaran dapat menjadi sumber daya terbaik sekolah dalam mendidik guru lain

Analisis E-Learning Dalam Pembelajaran Evolusi Mahasiswa Pendidikan Biologi Selama Pandemi Covid-19

Penelitian ini bertujuan untuk mengetahui analisis e-learning dalam pembelajaran evolusi mahasiswa pendidikan biologi selama pandemi covid-19. Penelitian ini adalah penelitian studi pustaka (library research). Sumber data berasal dari jurnal nasional dan internasional bereputasi, buku dan sumber relevan lainnya. Teknik pengumpulan data dengan menelusuri jurnal dan buku yang berkaitan dengan kemampuan metakognitif mahasiswa. Hasil penelitian ini disimpulkan bahwa pembelajaran e-learning selama Covid-19 berjalan dengan efektif dalam pembelajaran evolusi dan memberikan kemudahan kepada mahasiswa dalam memahami materi pelajaran

Online Learning Management System and Analytics using Deep Learning

During this pandemic we have seen rise in popularity of online learning platforms. In this paper, we are going to discuss E-Learning using analytics and deep learning focusing on mainly four objectives which are login systems for teachers and students, Gamification to engage learners, AR contents to increase the involvement of learners and learning analytics to develop competency. We will use Data Mining and Buisness Intelligence to extract high level knowledge from the raw data of students. To predict engagement of students we have used several ML algorithms. This study provides an introduction to the technology of AR and E-Learning systems. The main focus of this paper is to use research on augmented reality and integrate it with Buisness Intelligence and Data Mining(DM). Engaging student till the end of the course became really difficult for traditional E-Learning Platform. Therefore, Gamification in E-learning is good way to solve this problem

PENGEMBANGAN MEDIA PEMBELAJARAN DASAR DESAIN GRAFIS BERBASIS MOODLE DI SMK NEGERI 1 SRAGEN

Along with the development of information technology, the development of information and communication technology provides many offers in the learning process, one of which is e-learning. E-Learning is an approved one which is assimilated in the Educational Environment. In the development of education, there are three popular types of e-learning namely edmodo, schoology and moodle. Moodle is open source software with various learning support facilities that are accommodated in one e-learning portal that can be developed in accordance with the world of education. The research objectives achieved were: 1) Describe the initial state of learning basic graphic design in SMK Negeri 1 Sragen, 2) Develop a basic learning media for moodle-based graphic design, 3) Describe the feasibility of developing a basic learning media for moodle-based graphic design? 4) Describe the development of moodle-based graphic design learning media? The research used is research and development (R&D). The development model used is Hannafin and Peck's model, with the following arrangements: needs analysis, design, development and implementation. This Development Model has been tested, where each test is carried out pretest and posttest. The effectiveness of the development of basic learning media for moodle-based graphic design in SMK Negeri 1 Sragen can increase the average value in examinations in class X TKJ 1 and in class X TKJ 2. This refers to Moodle learning media which is proven to be suitable for use

Evaluation of the Hybrid Pedagogic Method in Students’ Progression in Learning Using Neural Network Modelling and Prediction

The COVID-19 pandemic has changed dramatically the way how universities ensure the continuous and sustainable way of educating students. This paper presents the evaluation of the hybrid pedagogic methods in students’ progression in Learning using neural network (NN) modelling and prediction. The hybrid pedagogic approach is based on the revised Bloom’s taxonomy in combination with the flipped classroom, asynchronous and cognitive learning approach. Educational data of labs and class test scores, as well as students’ total engagement and attendance metrics for the programming module are considered in this study. Conventional statistical evaluations are performed to evaluate students’ progression in learning. The NN is further modelled with six input variables, two layers of hidden neurons, and one output layer. Levenberg-Marquardt algorithm is employed as the back propagation training rule. The performance of neural network model is evaluated through the error performance, regression, and error histogram. Overall, the NN model presents how the hybrid pedagogic method in this case has successfully quantified students’ progression in learning throughout the COVID-19 period

Extracting student patterns from log file Moodle course: A case study

This paper introduces a set of extracted factors from Moodle log file of the selected course as a case study that aims to capture student Engagement (E), Behavior (B), Personality (Pers) and Performance (P). The factors are applied to identify students’ EBPersP with different course activities. The data set used in this paper was selected from the "Introduction to Computer Science" online course that captures 273,906 records as a log file for 29 students, delivered in Spring 2020. The paper also tries to show whether there is a relationship between student engagement, behavior and personality and their performance. Results show different patterns of students’ interactions with course contents, activities, and assessments. Specifically, our findings highlight that students' EBPersP could be extracted from Moodle log files. In addition, the extracted factors could assist instructors on how to focus more on students with low and average performance, giving them more attention to enhancing their performance.

Student engagement during pandemic COVID-19 and its implications for guidance and counseling

Student engagement is a condition of the extent to which students play an active role in the learning process by focusing on time, energy, mind, effort, feelings and making it happen in action to complete their academic tasks completely. This study aims to explore and find out the level of student engagement in the Covid-19 pandemic period seen from gender differences and the school level. Quantitative descriptive research with this survey design involves 469 students, 245 students of the junior high school, and 224 senior high school students chosen using a stratified random sampling proportionate cluster. The results showed that secondary school students in the Covid-19 pandemic period had an average level of student engagement in the medium category. This study found, there was no different level of student engagement based on gender (t (467) = -1.86). But specifically, the participation has a significant difference, while the skill, emotion, and performance do not have a significant difference. At the school level, indicate that there are significant differences in the level of student engagement (t(467)= -3.39). Furthermore, it can be seen from every indicator of student engagement skills, participation and performance have a significant difference and only an emotion that does not have it. The results of this study have implications for the planning of guidance and counseling programs in schools during the Covid-19 pandemic period, which is important to see the level of student engagement, especially in the emotional indicator. Further discussion is discussed in this article

Data-Driven Modeling of Engagement Analytics for Quality Blended Learning

Engagement analytics is a branch of learning analytics (LA) that focuses on student engagement, with the majority of studies conducted by computer scientists.Thus, rather than focusing on learning, research in this field usually treats education as a scenario for algorithms optimization and it rarely concludes with implications for practice. While LA as a research field is reaching ten years, its contribution to our understanding of teaching and learning and its impact on learning enhancement are still underdeveloped. This paper argues that data-driven modeling of engagement analytics is helpful to assess student engagement and to promote reflections on the quality of teaching and learning. In this article, the authors a) introduce four key constructs (student engagement, learning analytics, engagement analytics, modeling and data-driven modeling); b) explain why data-driven modeling is chosen for engagement analytics and the limitations of using a predefined framework; c) discuss how to use engagement analytics to promote pedagogical reflection using a pilot study as a demonstration. As a final remark, the authors see the need of interdisciplinary collaboration on engagement analytics between computer science and educational science. In fact, this collaboration should enhance the use of machine learning and data mining methods to explore big data in education as a means to provide effective insights for quality educational practice.Peer reviewe

An approach for improved students’ performance prediction using homogeneous and heterogeneous ensemble methods

Web-based learning technologies of educational institutions store a massive amount of interaction data which can be helpful to predict students’ performance through the aid of machine learning algorithms. With this, various researchers focused on studying ensemble learning methods as it is known to improve the predictive accuracy of traditional classification algorithms. This study proposed an approach for enhancing the performance prediction of different single classification algorithms by using them as base classifiers of homogeneous ensembles (bagging and boosting) and heterogeneous ensembles (voting and stacking). The model utilized various single classifiers such as multilayer perceptron or neural networks (NN), random forest (RF), naïve Bayes (NB), J48, JRip, OneR, logistic regression (LR), k-nearest neighbor (KNN), and support vector machine (SVM) to determine the base classifiers of the ensembles. In addition, the study made use of the University of California Irvine (UCI) open-access student dataset to predict students’ performance. The comparative analysis of the model’s accuracy showed that the best-performing single classifier’s accuracy increased further from 93.10% to 93.68% when used as a base classifier of a voting ensemble method. Moreover, results in this study showed that voting heterogeneous ensemble performed slightly better than bagging and boosting homogeneous ensemble methods