An Integrated Evaluation Approach for E-Learning Systems in Career and Technical Education

Author(s):  
Wenhao David Huang ◽  
Steven R. Aragon

As E-learning is gaining popularity in higher education, its evaluation becomes more critical than ever, to ensure the achievement of intended learning outcome. The effectiveness of E-learning system evaluation under current practices, however, remains questionable. One reason for such uncertainty is the lack of direct measurement while learning occurs since most evaluation data is collected after the learning process. Thus this chapter proposes an integrated evaluation approach for E-learning systems based on Cognitive Load Theory and grounded in the 4C/ID-model. Both direct and indirect measurements will be deployed in the integrated approach in the context of cognitive load. Furthermore all evaluation data can be translated into practical E-learning design solutions by triangulating with the 4C/ID-model. This chapter also suggests that future evaluation framework on E-learning should include factors from attitudinal and social aspects of learning process.

2021 ◽  
Vol 23 (4) ◽  
pp. 390-398
Author(s):  
Neli Borcheva ◽  

The article deals with the issue related to the use of the integrated approach and the integrated cross-curricular interaction in education. It focuses on its advantages for conducting a modern learning process, orientation to specific results and practical orientation of training. Issues of e-learning are addressed. Experiences and good practices of innovative schools in the implementation of integrated cross-curricular interaction are shared.


Author(s):  
Yingqin Zhong ◽  
John Lim

Globalization makes cultural diversity a pertinent factor in e-learning, as distributed learning teams with mixed cultural backgrounds become commonplace in most e-learning programs, which can be study-based (schools and universities) or work-based (training units) (Zhang & Zhou, 2003). In these programs, collaborative learning is supported via computermediated communication technologies and instructional technologies. The primary goal of enhancing learning with technology aids, aligning with the goal of education at all levels, is to engage students in meaningful learning activities, which require learners to construct knowledge by actively interpreting, acquiring, and analyzing their experience (Alavi, Marakas, & Yoo, 2002). In accordance, meaningful learning requires knowledge to be constructed by the learners but not by the teachers. In this regard, collaborative learning, an activity where two or more people work together to create meaning, explore a topic, or improve skills, is considered superior to other individualistic instructional methods (Lerouge, Blanton, & Kittner, 2004). The basic premise underlying this is the socio-learning theory, which advocates that learning and development occur during cooperative socialization among peers and emerge through shared understandings (Leidner & Jarvenpaa, 1995). This highlights the criticality of the communication and collaboration pertaining to an individual’s learning process. Since culture reflects the way one learns (Hofstede, 1997; Vygotsky, 1978), group members’ cultural backgrounds play a significant role in affecting the collaborative learning process (Chang & Lim, 2005). Language, cognitive style, and learning style are some aspects of culture that concern collaborative learning in the short term. Groups which have members of different cultural backgrounds are expected to be availed a wider variety of skills, information, and experiences that could potentially improve the quality of collaborative learning (Rich, 1997). In contrast, a group comprising members of similar backgrounds is vulnerable to the “groupthink” syndrome; when the syndrome operates, members could ignore alternatives, resulting in a deterioration of efficiency in making a group decision (Janis, 1982). Accordingly, it is conceivable that groups formed by members of different cultural backgrounds are inherently less prone to the “groupthink” syndrome. However, the advantages of cultural diversity in achieving meaningful collaborative learning are not easily realized, as the basic modes of communication may vary among different cultures and, in consequence, communication distortion often occurs (Chidambaram, 1992). Collaborative learning systems (CLS) are being increasingly researched owing to their potential capabilities and the associated new opportunities in supporting collaborative learning, in particular for distributed groups involving members of different cultural backgrounds (Alavi & Leidner, 2001). Collaborative learning systems provide the necessary medium to support interaction among learners, and therefore modify the nature and the ef- ficiency of the collaborative learning activities (Mandryk, Inkepn, Bilezikjian, Klemmer, & Landay, 2001). The current article looks into how collaborative learning systems may better accommodate cultural diversity in e-learning groups. In addition, this article discusses pertinent issues regarding the role of a leader in building the common ground among learners in order to maximize the potential of collaborative learning systems when cultural diversity is present.


2021 ◽  
Vol 4 (1) ◽  
pp. 1-12
Author(s):  
Faith Ngami Kivuva ◽  
Elizaphan Maina ◽  
Rhoda Gitonga

Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current e-learning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.


2020 ◽  
Vol 7 (2) ◽  
pp. 88
Author(s):  
Sefiu Taiwo Oloruntoyin

This work investigates the integration of e-Learning systems and knowledge management technology to improve, capture, organize and deliver large amounts of knowledge. First, a model is proposed for the phases of knowledge management. The model is then enhanced with concepts and technology from e-Learning. The model is then used to illustrate real world scenarios that add increasing amounts of knowledge management to an e-Learning environment. The system, AMID promises high interactivity, efficiency and effectiveness of integration of knowledge management and e-learning. In addition, the developed system will enhance technical learning process.


2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.


ARIKA ◽  
2020 ◽  
Vol 14 (2) ◽  
pp. 75-82
Author(s):  
Eneng Fitri Handayani ◽  
Mariati Tirta Wiyata

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.


2015 ◽  
Vol 7 (3) ◽  
pp. 157
Author(s):  
Forouzan Rezaeian Tiyar ◽  
Hooshang Khoshsima

<p>The evolution of technologies leads to the great significance of e-learning in the domain of education. Recognition of the crucial factors which influence learners’ aims towards continued use of e-learning would guide teachers, learners and e-learning developers to increase e-learning use. To this end, the present study investigates the Expectation-Confirmation Model (ECM) factors of Post-Adoption Expectation (PAE) which is explored via using language learners’ post-adoption experiences in the use of e-learning systems. Learning process, tutor interaction, peer interaction, and course design are the four factors identified used for extending the perception of language learners’ experiences in e-learning. The survey method was used to empirically validate the suggested model (ECM) of the present study. A total sample of 120 Iranian university students participated in the study.</p><p>In order to investigate the proposed model, structural equation modelling employing Smart PLS 2.0 was run. The findings indicate that learners’ confirmation of using e-learning has a significant effect on the four aforementioned factors. Learning process and course design are the only two factors that have a significant effect on users’ satisfaction and continuance intention. On the other hand, the results showed that tutor interaction and peer interaction do not have a significant effect on predicting learners’ satisfaction and continuance intention of e-learning systems.</p><p> </p><p>Keywords: e-learning, students’ satisfaction, students’ continuance intention, expectation-confirmation model, post-adoption expectation</p>


Author(s):  
Boryana Deliyska ◽  
Peter Manoilov

The intelligent learning systems provide a direct customized instruction to the learners without intervention of human tutor on the base of Semantic Web resources. The principal role ontologies play in these systems is as an instrument for modeling learning process, learner, learning objects, and resources. In the chapter, a variety of relationships and conceptualizations of ontologies used in the intelligent learning systems are investigated. The utilization of domain and application ontologies in learning object building and knowledge acquisition is represented. The conceptualization of domain ontologies in e-learning is presented by the upper levels of its taxonomies. Moreover, a method and an algorithm intended for generation of application ontologies of structural learning objects (curriculum, syllabus, topic plan, etc.) are developed. Examples of curriculum and syllabus application ontologies are given. Further these application ontologies are used for structural learning object generation.


Author(s):  
Khalid Hamed Allehaibi ◽  
Nasser Nammas Albaqami

Defining, measuring, and achieving quality of e-learning systems are not an easy task. Accordingly, one of the most essential goals for the higher educational institutes is how to reach a high and satisfied level of quality in their learning systems. Achieving such level needs adequate and continuous improvements for the whole e-learning environment elements. Therefore, we aim in our work to construct a unified framework for total quality management system (TQMS) that attempt to satisfy the quality requirements, needs, and standards. The objective of this paper is to present a quality control model for e-learning system that adopts the e-learning platform according to the on-line determination of both user's requirements and global standards. This paper proposed software architecture of quality Management framework for e-learning that could be adopted by different higher education institutes to control the quality of the e-learning process, and assure the quality of the e-learning process outcome. The proposed framework is based on a tri-dimensions quality model. The three dimensions are set of quality requirements for e-learning environment represented in Quality Assurance (QA) policies that will be formalized by using policy based approach, the specifications of e-learning platform that provide learning and teaching activities, and quality control process loop. The architecture for monitor and ensure quality control of the QA policies for e-learning system will deliver the whole learning services in an optimal way. It is also flexible and can be implemented over any e-learning system.


2017 ◽  
Vol 55 (7) ◽  
pp. 996-1021 ◽  
Author(s):  
Ruey-Shin Chen ◽  
I-Fan Liu

Currently, e-learning systems are being widely used in all stages of education. However, it is difficult for school administrators to accurately assess the actual usage performance of a new system, especially when an organization wishes to update the system for users from different backgrounds using new devices such as smartphones. To allow school administrators to conduct upgrades of e-learning systems that take into consideration students' current usage conditions, this study proposed a two-stage system evaluation approach to explore the adoption of new systems. We collected 352 samples in Stage I. The goal of this Stage I was to propose a research model to understand the usage intentions of college students toward campus e-learning systems and also the factors which showed significant differences between PC and smartphone usage. A total of 30 trained students participated in Stage II. The goal of Stage II was to propose a system performance evaluation method to evaluate the performance of the new and existing systems according to the concerned factors of smartphone users after actual system use. Finally, based on our research model and system performance evaluation method, we put forward conclusions and suggestions that schools could use as references for future system procurements and updates.


Sign in / Sign up

Export Citation Format

Share Document