Online Learning Experience Scale Validation and Its Impact on Learners’ Satisfaction

Deshwal, Pankaj; Trivedi, Amit Kumar; Himanshi, H.L.N.

doi:10.1016/j.procs.2017.08.178

Cited by 24 publications

(17 citation statements)

References 16 publications

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“…In an attempt to answer the fundamental questions that guide our research, as well as to define the determinant of student use, satisfaction and success in e-learning, we propose an e-learner success assessment model (e-LSAM). Based on the exploitation of the recent versions of the models presented in the previous section, by adding some factors have been identified from previous research work, such as social interactions (interactions between the instructor and students and interactions between pairs) (Cigdem, Ozturk, & Topcu 2016;El-Hilali, Al-Jaber, & Hussein, 2015;Matzat & Vrieling, 2016;Panadero, 2017;So & Brush, 2008;Temizer & Turkyilmaz, 2012), course flexibility (Barbera et al, 2013;Deshwal et al, 2017;Masrom, Zainon & Rahiman, 2008;Agrawal & Mittal, 2018) and diversity in assessments (Cidral et al, 2018;Lallemand et al, 2015;Radwan, 2014). Figure 1 shows our conceptual model called e-LSAM.…”

Section: Conceptual Modelmentioning

confidence: 99%

An Analysis to Understand the Online Learners’ Success in Public Higher Education in Morocco

Safsouf

Mansouri²,

Poirier

2020

JITE:Research

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Aim/Purpose: This study focuses on the learners’ success toward learning management systems in higher education in Morocco and also proposes a theoretical model to better understand the determinants of learners’ satisfaction, self-regulation and continuance intention to use these systems. For this purpose, variables which may have a positive or negative influence in our model are examined. Background: The latest version of the technology acceptance model, expectation–confirmation model, DeLone and McLean Information systems success model and self-regulated learning theory, have been used. This study proposes a causal model named e-learner success assessment model or e-LSAM. Methodology: In this study, a structural equation model (SEM) approach was used for the empirical validation and testing of correlation hypotheses between e-LSAM constructions. Contribution: This research extends previous literature on the factors that can contribute to learners’ use, engagement, satisfaction and success in an e-learning system, we also propose a causal model named e-learner success assessment model (e-LSAM). Findings: The results indicate that, learner’s success in an e-learning system could be explained by self-regulation and learners’ intention to continue using LMS, which is explained by learners’ satisfaction. The results also show that the system quality, course and information quality, course flexibility, diversity in assessments and social interactions can improve learners’ satisfaction with LMS platforms Impact on Society: This study will enable the university and higher school in Morocco to better understand the critical factors to be undertaken to improve student performance and educational levels. Future Research: This document is a general overview of factors that help to understand learner’s success in an eLearning system; it is not without limitations. Our research did not take into account the effects of demographic attributes such as gender, age, level of education and others. More specifically, Morocco, like any other country, has its own traditions and culture, future research should explore how these aspects influence the success of learners in an e-learning system.

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Section: Conceptual Modelmentioning

confidence: 99%

An Analysis to Understand the Online Learners’ Success in Public Higher Education in Morocco

Safsouf

Mansouri²,

Poirier

2020

JITE:Research

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“…"Education is one of the most leading means of providing social equality and also one of the optimistic as well as a fulfilling experience of an individual's life" [1]. Jika dilihat dari kondisi saat ini, Negara di dunia diguncang dengan wabah pandemi COVID-19, begitu pula dengan Negara Indonesia.…”

Section: Pendahuluanmentioning

confidence: 99%

Pengaruh Online Learning Experience Dan Gaya Belajar Terhadap Motivasi Belajar Peserta Didik SMKN 2 Malang Saat Pembelajaran Jarak Jauh (Pjj)

Syarifuddin¹,

Wijoyo

Wardhono

2021

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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ABSTRAK Pembelajaran jarah jauh (PJJ) kini harus terpaksa dilakukan di berbagai jenjang pendidikan guna menekan perluasan pandemi COVID-19 di Indonesia. Berbagai dampak akibat PJJ pun dirasakan oleh peserta didik khususnya jenjang SMK, karena yang awalnya pelajaran 70% harus melakukan praktik kini harus terpaksa dilakukan secara teori dan daring. SMK yang cukup berdampak adalah SMKN 2 Malang, menurut wawancara dan observasi penulis terhadap beberapa narasumber, mereka merasa malas, banyak rebahan hingga motivasi belajar menurun saat mengikuti PJJ. Tujuan dari penelitian ini adalah 1) Mengetahui pengaruh online learning experience terhadap motivasi belajar, 2) Mengetahui pengaruh gaya belajar terhadap motivasi belajar dan 3) Mengetahui pengaruh online learning experience dan gaya belajar secara bersamaan terhadap motivasi belajar peserta didik SMKN 2 Malang saat PJJ. Penelitian ini menggunakan regresi linier baik sederhana maupun berganda dengan pendekatan kuantitatif, adapun tahap penelitian dimulai dari merumuskan permasalahan hingga penarikan kesimpulan dan saran. Hasil penelitian diperoleh bahwa online learning experience berpengaruh secara positif terhadap motivasi belajar dengan pengaruh sebesar 19,6%. Sedangkan variabel gaya belajar baik visual dan audiotorial masing-masing berpangaruh positif terhadap motivasi belajar dengan besar pengaruh 28,4% dan 13,9%, untuk gaya belajar kinestetik tidak berpengaruh terhadap motivasi belajar peserta didik SMKN 2 Malang. Online learning experience dan gaya belajar secara bersama-sama berpengaruh secara positif sebesar 25,4% dan sisanya dipengaruhi oleh variabel lain diluar penelitian ini.

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“…The basic set up requires screens through which lectures could be delivered, or content is made visible to read, also required are audio devices for delivery of study content as well as communication, discussion, and other interactions. Education is easily accessible to everyone (Deshwal et al, 2017). Different students may use this environment in different tiers, because their learning devices and data transfer rate are different.…”

Section: Implementationsmentioning

confidence: 99%

A Web-Based Learning Environment of Remote Sensing Experimental Class With Python

Sheng

Chen

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The results are encouraging and some recommendations will be extracted for the future.

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Online Learning Experience Scale Validation and Its Impact on Learners’ Satisfaction

Cited by 24 publications

References 16 publications

An Analysis to Understand the Online Learners’ Success in Public Higher Education in Morocco

An Analysis to Understand the Online Learners’ Success in Public Higher Education in Morocco

Pengaruh Online Learning Experience Dan Gaya Belajar Terhadap Motivasi Belajar Peserta Didik SMKN 2 Malang Saat Pembelajaran Jarak Jauh (Pjj)

A Web-Based Learning Environment of Remote Sensing Experimental Class With Python

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