Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

scholarly journals DAMPAK PANDEMI COVID-19 TERHADAP KEGIATAN PEMBELAJARAN ONLINE DI STMIK KOMPUTAMA MAJENANG

2021 ◽  
Vol 2 (2) ◽  
pp. 16-25
Author(s):  
Willy Setiawan ◽  
Dede Yusuf
Keyword(s):  
Elementary School ◽  
Online Learning ◽  
Data Collection ◽  
Qualitative Methods ◽  
Learning Activities ◽  
Tertiary Level ◽  
Internet Connection ◽  
The World ◽  
E Learning ◽  
The Impact

COVID-19 is a virus originating from Wuhan, China that spread rapidly throughout the world. Indonesia began to be infected since the beginning of March 2020. The impact of the spread of COVID-19 caused losses to many countries, especially in the economic field. In the field of education, learning activities carried out in class are replaced online as a result of the spread of COVID-19 starting from elementary school to tertiary level. This study uses descriptive qualitative methods that describe online learning activities at STMIK Komputama Majenang during the COVID-19 pandemic that was held at home online. The object consists of 5 students of STMIK Komputama Majenang. Data collection was carried out using a questionnaire containing questions related to online learning at STMIK Komputama Majenang during the COVID-19 pandemic. Based on the results of research, online learning activities at STMIK Komputama Majenang have been effective and run well. Some applications used in online learning are Web E-learning, WhatsApp, Telegram, Edmodo, YouTube, Zoom, and Google Classroom. Constraints experienced during online learning are problems with a bad internet connection, limited quota, difficulty in learning, and the presence of other people's distractions when learning takes place.

scholarly journals EVALUASI KEPUASAN PENGGUNA E-LEARNING PADA PROSES PEMBELAJARAN DARING DI INSTITUT MANAJEMEN WIYATA INDONESIA

ARIKA ◽  
2020 ◽  
Vol 14 (2) ◽  
pp. 75-82
Author(s):  
Eneng Fitri Handayani ◽  
Mariati Tirta Wiyata
Keyword(s):  
Online Learning ◽  
Data Collection ◽  
Learning Process ◽  
User Satisfaction ◽  
Weighted Average ◽  
Learning Systems ◽  
E Learning ◽  
Descriptive Method

The research aims to obtain an overview of the (1) The quality of e-learning systems, (2) The quality of e-learning information, (3) The quality of e-learning services, and (4) e-Learning user satisfaction in the online learning process at the Institut Manajemen Wiyata Indonesia. This research uses the descriptive method of weighted average evaluative. Data collection is implemented by spreading the questionnaire. The results showed: (1) The quality of E-learning systems in the online learning process is categorized (well), (2) The quality of E-learning information on the online learning process is categorized (well), (3) The quality of E-learning services on the category of online learning is not good, (4) The satisfaction of E-learning users in the online learning process is categorized well.

scholarly journals Adaptive Upgradation of Personalized E-Learning Portal using Data Mining

2020 ◽  
Vol 10 (1) ◽  
pp. 224-227
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Clustering Algorithm ◽  
Learning Experience ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Development Phase ◽  
Data Mining Techniques ◽  
E Learning ◽  
Learning Portal

Implementation of data mining techniques in elearning is a trending research area, due to the increasing popularity of e-learning systems. E-learning systems provide increased portability, convenience and better learning experience. In this research, we proposed two novel schemes for upgrading the e-learning portals based on the learner’s data for improving the quality of e-learning. The first scheme is Learner History-based E-learning Portal Up-gradation (LHEPU). In this scheme, the web log history data of the learner is acquired. Using this data, various useful attributes are extracted. Using these attributes, the data mining techniques like pattern analysis, machine learning, frequency distribution, correlation analysis, sequential mining and machine learning techniques are applied. The results of these data mining techniques are used for the improvement of e-learning portal like topic recommendations, learner grade prediction, etc. The second scheme is Learner Assessment-based E-Learning Portal Up-gradation (LAEPU). This scheme is implemented in two phases, namely, the development phase and the deployment phase. In the development phase, the learner is made to attend a short pretraining program. Followed by the program, the learner must attend an assessment test. Based on the learner’s performance in this test, the learners are clustered into different groups using clustering algorithm such as K-Means clustering or DBSCAN algorithms. The portal is designed to support each group of learners. In the deployment phase, a new learner is mapped to a particular group based on his/her performance in the pretraining program.

scholarly journals Harmonizing Some Solutions For E-Learning And Online Learning

2018 ◽  
Vol 2 (4) ◽  
pp. 447-460
Author(s):  
Amjed Abbas Ahmed ◽  
Ali Hamza Najim
Keyword(s):  
Student Population ◽  
Learning Systems ◽  
Virtual Classrooms ◽  
Descriptive Information ◽  
Education Industry ◽  
Online Classrooms ◽  
The World ◽  
E Learning ◽  
Islamic Science ◽  
Recent Trends

This paper presents a descriptive information for the E-Learning, its design and development as well as the application and its management taking into consideration of its major components. In recent times, E-Learning applications has done one major thing: remove distance as a barrier toward learning. E-Learning applications in form of online or virtual classrooms has been embraced especially in environments that have the facility to accommodate it. However, recent trends has caused a lot of reputable institutions to re-examine their respective model: the mode at which education is disseminated to their student population as Online Classrooms increased competitions among institutions all over the world. The aim of this paper is to examine E-Learning, the effects it already has on learning and future implications it might have on the education industry as its recent solutions provide outstanding features that makes people question the importance and relevance of institutions with physical infrastructures. The sample size taken into consideration is the student population of Imam Kadhim (a) College for Islamic Science, Iraq. There are also a couple of E-Learning Systems and software online and for the sake of this paper, Edx.org will be used a reference system.

scholarly journals SISTEM PENGELOLAAN KEARSIPAN Google MELALUI BIG DATA

2020 ◽  
Vol 15 (2) ◽  
pp. 21-20
Author(s):  
Aldha Shafrielda Sihab ◽  
Anugerah Pagiyan Nurfajar
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Secondary Data ◽  
Data Sources ◽  
The World ◽  
Library Studies ◽  
Secondary Data Sources ◽  
Learning Machine ◽  
Crucial Part ◽  
Big Data Technology

Big data is an newest trend that embraces the world of technology and business. It's a data collection so large and complex that it no longer allows it to be managed with traditional software tools. One of the world's companies moving in big data technology is Google.Inc. This company maintains and districts data for various purposes, so its presence is urgently needed. As the development of data in Google has been a crucial part of the digital age, resulting in several breakthroughs. First Google data can hold files effectively as well as easily be accessed by people. Both big data can easily call back specifically through Google's learning machine. The study was conducted using qualitative diskirtive methods with secondary data sources through library studies.

scholarly journals Application of E-learning System in the World of Education

2021 ◽  
Vol 1 (1) ◽  
pp. 127-133
Author(s):  
V Pratiwi ◽  
◽  
S L Rahman ◽  
Keyword(s):  
Management System ◽  
Learning Management System ◽  
Learning Systems ◽  
Learning System ◽  
The Internet ◽  
Literature Study ◽  
Positive Effects ◽  
The World ◽  
Teachers And Students ◽  
E Learning

The purpose of this study is to determine the positive effects of e-learning systems on improving student's understanding of concepts and comparing e-learning with old learning system. Nowadays, the internet is one of the way to find informations easily without reading books and attending classes. The method used in this research is literature study method by obtaining information and data from various sources. The results from this study is that e-learning can improve students' cognitive abilitiesas it is easier to access words, powerpoint, html or PDF in the application. The conclusion is that e-learning can be used as a learning innovation that can help teachers and students using the Software Learning Management System

scholarly journals Users' Emotions Analysis based on Hybrid Feature Extraction Techniques

2020 ◽  
pp. 291-296
Author(s):  
Sulis Sandiwarno
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Systems ◽  
Experimental Results ◽  
Extraction Techniques ◽  
Machine Learning Classifiers ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
E Learning ◽  
Hybrid Feature Extraction

In order to solve some problems of importance of words and missing relations of semantic between words in the emotional analysis of e-learning systems, the TF-IWF algorithm weighted Word2vec algorithm model was proposed as a feature extraction algorithm. Moreover, to support this study, we employ Multinomial Naïve Bayes (MNB) to obtain more accurate results. There are three mainly steps, firstly, TF-IWF is employed used to compute the weight of word. Second, Word2vec algorithm is adopted to compute the vector of words, Third, we concatenate first and second steps. Finally, the users' opinions data is trained and classified through several machine learning classifiers especially MNB classifier. The experimental results indicate that the proposed method outperformed against previous approaches in terms of precision, recall, F-Score, and accuracy.

scholarly journals E-learning systems in higher education institutions: An outlook of their use in the Western Balkan Region

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Blerta Chaushi ◽  
Agron Chaushi ◽  
Florije Ismaili
Keyword(s):  
Learning Environments ◽  
Public Universities ◽  
Learning Systems ◽  
Review Of The Literature ◽  
The Public ◽  
The Core ◽  
The World ◽  
Western Balkan Region ◽  
E Learning ◽  
Balkan Region

This study provides a review of the literature on e-learning systems evolution and environments. The argument is that e-learning systems should be embedded in the core strategy of the institution. To support this premise, studies for e-learning are analyzed and six recommendations are drawn for universities to follow in order to have successful e-learning environments. The main contribution of this study, however, is the identification of the trends and statistics regarding the e-learning usage in the Balkan region. These stats are identified through a survey conducted in 40 universities in 10 countries from this region. The results show that more than 70% of the universities have adopted LMS, which does not fall short behind when compared with universities in the world. Also, the results show that around 64% of the private universities develop LMS in-house, compared with around 38% of the public universities, which have funding from the governments and can purchase vendor based solutions. However, the results from the survey suggest that public universities in these countries are more prone to open-source rather than vendor based.

scholarly journals Towards Data Justice Unionism? A Labour Perspective on AI Governance

2021 ◽  
pp. 267-284
Author(s):  
Lina Dencik
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Autonomous Systems ◽  
Learning Systems ◽  
Worker Productivity ◽  
Labour Movement ◽  
Profound Impact ◽  
Data Infrastructures

The dual occurrences of constant data collection and use of artificial and autonomous systems in the workplace are having a profound impact on workers’ lives. Workers are subjected to constant surveillance that not only monitor worker productivity but factors unrelated to work. At the same time, machine learning systems are using these data to transform how work is being allocated, assessed and completed and as a result, worker lives and value in the workplace and beyond. Yet governance frameworks for AI have thus far been advanced with a noticeable absence of worker voice, unions, and labour perspectives. In this chapter I will discuss how concerns about data and data infrastructures need to be situated as part of a workers’ rights agenda, the role of the labour movement in advancing alternative governance frameworks, and the potential for data justice unionism.

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