Time Management and Procrastination During Online Learning

This study aimed to determine the relationship between students’ time management and delays in doing academic assignments while learning online at the Faculty for Teacher Training and Education. This was a quantitative study. The population was students of FKIP UPI YPTK Padang, the Department of Informatics Engineering and of English Education. Purposive sampling was used, with a focus on students who are accustomed to doing online lectures with the Learning Management System (LMS) application in the form of Edmodo and Schoology applications. Data were collected through a questionnaire distributed online via Google Forms. The results showed that the correlation between time management and postponement of academic assignments was 0.6546, which meant that time management had a moderate correlation with academic procrastination. Thus, it can be concluded that students learning online may experience conditions that are less stable for managing their time to work on academic assignments. Keywords: online learning, time management, academic procrastination

HYBRID LEARNING TIME MODIFICATION CAN IMPROVE LEARNING ACTIVITY AND LEARNING OUTCOMES

The purpose of this study was to determine the effectiveness of hybrid learning time modification in terms of learning outcomes; knowing the relationship between learning activities and learning outcomes and knowing the effect of hybrid and one other group is the conventional group (face-to-face only), this group is the control group.Collecting data using a learning activity questionnaire and a knowledge test to determine learning outcomes. Data analysis technique with Ancova. The results of the study: (1) hybrid learning time modification is effective in improving learning outcomes (2) significant relationship between learning activity and learning outcomes, significance 0.000; (3) there is a significant difference in the effect of variations in hybrid learning time modification on learning activity and learning outcomes, the significance of 0.037 Keywords: Time Modification, Hybrid Learning, Active Learning, Learning Outcomes

The Mental Health Status and Associated Factors Among Medical Students Engaged in Online Learning at Home During the Pandemic: A Cross-Sectional Study From China

Background: The purpose of this study was to assess the mental health status of medical students engaged in online learning at home during the pandemic, and explore the potential risk factors of mental health.Methods: A cross-sectional study was conducted via an online survey among 5,100 medical students from Wannan Medical College in China. The Depression, Anxiety and Stress scale (DASS-21) was used to measure self-reported symptoms of depression, anxiety, and stress among medical students during online learning in the pandemic.Results: In total, 4,115 participants were included in the study. The prevalence symptoms of depression, anxiety, and stress were 31.9, 32.9, and 14.6%, respectively. Depression was associated with gender, grade, length of schooling, relationship with father, students' daily online learning time, and students' satisfaction with online learning effects. Anxiety was associated with gender, length of schooling, relationship with father, relationship between parents, students' daily online learning time, and students' satisfaction with online learning effects. Stress was associated with grade, relationship with father, relationship between parents, students' daily online learning time, and students' satisfaction with online learning effects.Conclusions: Nearly one-third of medical students survived with varying degrees of depression, anxiety, and stress symptoms during online learning of the COVID-19 pandemic. Gender, grade, length of schooling, family environment, and online learning environment play vital roles in medical students' mental health. Families and schools should provide targeted psychological counseling to high-risk students (male, second-year and third-year, four-year program). The findings of this study can provide reference for educators to cope with the psychological problems and formulate the mental health curriculum construction among medical students during online learning.

Building a mathematical model and an algorithm for training a neural network with sparse dipole synaptic connections for image recognition

Large enough structured neural networks are used for solving the tasks to recognize distorted images involving computer systems. One such neural network that can completely restore a distorted image is a fully connected pseudospin (dipole) neural network that possesses associative memory. When submitting some image to its input, it automatically selects and outputs the image that is closest to the input one. This image is stored in the neural network memory within the Hopfield paradigm. Within this paradigm, it is possible to memorize and reproduce arrays of information that have their own internal structure. In order to reduce learning time, the size of the neural network is minimized by simplifying its structure based on one of the approaches: underlying the first is «regularization» while the second is based on the removal of synaptic connections from the neural network. In this work, the simplification of the structure of a fully connected dipole neural network is based on the dipole-dipole interaction between the nearest adjacent neurons of the network. It is proposed to minimize the size of a neural network through dipole-dipole synaptic connections between the nearest neurons, which reduces the time of the computational resource in the recognition of distorted images. The ratio for weight coefficients of synaptic connections between neurons in dipole approximation has been derived. A training algorithm has been built for a dipole neural network with sparse synaptic connections, which is based on the dipole-dipole interaction between the nearest neurons. A computer experiment was conducted that showed that the neural network with sparse dipole connections recognizes distorted images 3 times faster (numbers from 0 to 9, which are shown at 25 pixels), compared to a fully connected neural network

TINGKAT KEPRAKTISAN PENGGUNAAN E-LEARNING PADA MODEL PEMBELAJARAN BLENDED LEARNING DI TINGKAT SMA

Technological developments have a considerable influence on education, especially in learning media. Learning media is no longer conventional but is starting adapted with existing technology. The results of the initial observations found that the majority of senior high schools already have facilities for technology-based learning media, but the utilization of technology-based learning media is still very low. This resulted in less utilization of school facilities which already provided. Based on these observations and references from existing researches, it is assumed that e-learning, which is one of the technology-based learning media, can be used as a learning media for blended learning models. This research purpose was to produce an e-learning as learning media and the assessment level of practicality. This research used the Research and Development’s method with four-D development procedures to examine the level of practicality. To assess the level of practicality of e-learning, an assessment through a questionnaire was given to randomly selected subjects. The practicality assessment questionnaire consists of three main indicators such as indicators state of use, effectiveness of learning time and indicators of benefits. Referring to the results of the assessment, the average level of e-learning’s practicality is 80.70% so it can be concluded that e-learning is practical to be used as a learning media.

A Multiclass Nonparallel Parametric-Margin Support Vector Machine

The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this paper extends TPMSVM for multiclass classification and proposes a novel K multiclass nonparallel parametric-margin support vector machine (MNP-KSVC). Specifically, our MNP-KSVC enjoys the following characteristics. (1) Under the “one-versus-one-versus-rest” multiclass framework, MNP-KSVC encodes the complicated multiclass learning task into a series of subproblems with the ternary output {−1,0,+1}. In contrast to the “one-versus-one” or “one-versus-rest” strategy, each subproblem not only focuses on separating the two selected class instances but also considers the side information of the remaining class instances. (2) MNP-KSVC aims to find a pair of nonparallel parametric-margin hyperplanes for each subproblem. As a result, these hyperplanes are closer to their corresponding class and at least one distance away from the other class. At the same time, they attempt to bound the remaining class instances into an insensitive region. (3) MNP-KSVC utilizes a hybrid classification and regression loss joined with the regularization to formulate its optimization model. Then, the optimal solutions are derived from the corresponding dual problems. Finally, we conduct numerical experiments to compare the proposed method with four state-of-the-art multiclass models: Multi-SVM, MBSVM, MTPMSVM, and Twin-KSVC. Experimental results demonstrate the feasibility and effectiveness of MNP-KSVC in terms of multiclass accuracy and learning time.

Enhanced DQN Framework for Selecting Actions and Updating Replay Memory Considering Massive Non-Executable Actions

A Deep-Q-Network (DQN) controls a virtual agent as the level of a player using only screenshots as inputs. Replay memory selects a limited number of experience replays according to an arbitrary batch size and updates them using the associated Q-function. Hence, relatively fewer experience replays of different states are utilized when the number of states is fixed and the state of the randomly selected transitions becomes identical or similar. The DQN may not be applicable in some environments where it is necessary to perform the learning process using more experience replays than is required by the limited batch size. In addition, because it is unknown whether each action can be executed, a problem of an increasing amount of repetitive learning occurs as more non-executable actions are selected. In this study, an enhanced DQN framework is proposed to resolve the batch size problem and reduce the learning time of a DQN in an environment with numerous non-executable actions. In the proposed framework, non-executable actions are filtered to reduce the number of selectable actions to identify the optimal action for the current state. The proposed method was validated in Gomoku, a strategy board game, in which the application of a traditional DQN would be difficult.