Teaching Documents by Dewi Octaviani
Conference Presentations by Dewi Octaviani
In recent years, many higher education institutions have used e-learning systems i.e. Learning Co... more In recent years, many higher education institutions have used e-learning systems i.e. Learning Content Management System (LCMS) as the medium of learning. However, these systems implemented without pay enough attention to the learners’ experiences of the employing the e-learning system. Although it is believed that the e-learning system can improve learning, the features and functionalities of the systems are often underutilized. Therefore, the important issue that requires to be determined is: what are the e-learning features that can enhance learning as an active learning? In order to promote student interaction on e-learning system, this paper explores the concept of meaningful learning pedagogy and particularly focusing on the characteristics of the active learning.
This paper also investigates the potential factors that contribute to active learning in the e-learning system (i.e. Moodle); which focuses on the activities and actions that able to support active learning by lecturers and students. The e-learning data log are observed to measure the number of hits of activities and actions on the e-learning system to determine whether the active learning characteristics have been achieved. The statistical analysis method is used to analyze activities and actions on data log.
Finally, this paper reveals a guideline with activities and actions in an e - learning system that support active learning. Therefore, with appropriate and proper usage of the tools in the e-learning system, learning experiences can be enhanced. This guideline proved to be useful to enhance using active learning technology by the educators.
Keywords: e-learning system; active learning; e-learning activities and actions
The extremely growth of web technologies in education continues today, the use of e-Learning syst... more The extremely growth of web technologies in education continues today, the use of e-Learning system is expected to enhance learning process and give a significant influence to the learners. E-learning allows learners to actively participate in the existing activities in systems and it is vital to evaluate the acceptance of e-learning system. The entire e-learning system has several factors that influenced to the learners acceptance. The objective of this paper is to examine the relationship of information system factors (i.e. system quality, information quality, and service quality) with user satisfaction and system outcome by adopting DeLone and McLean model. Data were collected from surveys of 113 students and analyzed by Structural Equation Model to test the hypotheses of the research model. The result shows that the system quality is strongly influenced to the user satisfaction and the user satisfaction has strongly influenced to the system outcome. Therefore, information quality, service quality and system outcome need to be improved and considered in order to contribute more to the user satisfaction. The limitation and discussion of the theoretical implications are discussed at the end of this paper.
Key words: e-learning, information system success factors, system quality, information quality, service quality.
One of critical success factors of e-learning is a learner’s behavior towards elearning system. D... more One of critical success factors of e-learning is a learner’s behavior towards elearning system. Due to that, it is important to analyze learners’ characteristics when using elearning system based on meaningful learning to define learners’ behavior during online
learning. We first define the meaningful learning pedagogy and implications towards learners’ behavior in e-learning system. Finally we analyze learners’ characteristics and define the cluster of learner’s characteristics by using K-Means cluster method. A case study based on e-learning data log from – students on Computational Intelligence Course at Software Engineering Department, Universiti Teknologi Malaysia. From meaningful learning pedagogy, this paper contributes to describes the characteristics of learners during online learning, the discussion and limitation was describes in the end of this paper.
Keywords: e-learning, meaningful learning characteristics, learners’ behavior, clustering
Introduction
Papers by Dewi Octaviani
Jurnal Teknologi, 2015
One of the critical success factors of e-learning is positive interest of students towards e-lear... more One of the critical success factors of e-learning is positive interest of students towards e-learning. The majority of activities of current e-learning usage are viewing and downloading. These activities are not meaningful with regard to enhancing learning quality. Due to that, the aim of this paper is to analyze students’ usage based on meaningful learning characteristics by clustering students’ activities and actions during online learning. We first define meaningful learning characteristics (as those which are active, authentic, cooperative, collaborative, and intentional) and associate these with e-learning activities and actions. Then, we analyze the students’ e-learning usage and define the cluster of student’s meaningful characteristics by using the K-Means cluster method. A case study has been conducted based on the e-learning log files of 37 students on Computational Intelligence Course at the Software Engineering Department, Universiti Teknologi Malaysia. The result of thi...
2015 International Conference on Science in Information Technology (ICSITech), 2015
The involvement of learning pedagogy towards implementation of e-learning contribute to the addit... more The involvement of learning pedagogy towards implementation of e-learning contribute to the additional values, and it is assign as a benchmark when the investigation and evaluation will carry out. The results obtained later believed would be fit to the domain problem. The results might provide instructional theories including recommendation after reasoning that can be used to improve the quality of teaching and learning in the virtual classroom. Ontology as formal conceptualization has been chosen as research methodology. Ontology conceptualization helps to illustrate the e-learning usage including activities and actions, likewise learning pedagogy in the form of concepts, class, relationships and instances. The ontology constructed in this paper is used in conjunction with the SPARQL rules, which are designed to test the reasoning ability of ontology. Reasoning results should be able to describe the knowledge contained in ontology, as well the facts on it. The SPARQL rules contains triplets to verify if the students are actively engaged in a meaningful way towards e-learning usage. The backward engine is optimized to store the facts obtained from queries. Development of ontology knowledge based and reasoning rules with SPARQL queries allow to contribute a sustainable competitive advantages regarding the e-learning utilization. Eventually, this research produced a learning ontology with reasoning capability to get meaningful information.
Elsevier
Learning is a transformation process which is implicates a plenty of activities, as well as on e-... more Learning is a transformation process which is implicates a plenty of activities, as well as on e-learning. One of the main issues on the education domain modeling is the lack of expertise to analyze if the e-learning actors supporting meaningful learning. Therefore, the integration of information for enhancing information retrieval or supporting meaningful learning, are important for reasoning mechanism. This paper presents an ontology-based and semantic reasoning for analysis of e-learning activities. The core part is we focus on the e-learning activities and actions concerning on the expected competences by incorporate meaningful learning characteristics. The semantics reasoning approaches is to capture the information about the real usage of an e-learning. We first collect informal questions then generate the formal terminology to gather formal questions, followed by specified set of formal axioms as rules. The glossary of Jena rules will subsequently represent onto machine readable language in respect to knowledge construction. Finally, by the knowledge bases the reasoning mechanisms become easier in order to analyze the use of activities and actions in e-learning
Keywords: ontology, semantic reasoning, knowledge construction, e-learning activities and actions, Jena rules.
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Teaching Documents by Dewi Octaviani
Conference Presentations by Dewi Octaviani
This paper also investigates the potential factors that contribute to active learning in the e-learning system (i.e. Moodle); which focuses on the activities and actions that able to support active learning by lecturers and students. The e-learning data log are observed to measure the number of hits of activities and actions on the e-learning system to determine whether the active learning characteristics have been achieved. The statistical analysis method is used to analyze activities and actions on data log.
Finally, this paper reveals a guideline with activities and actions in an e - learning system that support active learning. Therefore, with appropriate and proper usage of the tools in the e-learning system, learning experiences can be enhanced. This guideline proved to be useful to enhance using active learning technology by the educators.
Keywords: e-learning system; active learning; e-learning activities and actions
Key words: e-learning, information system success factors, system quality, information quality, service quality.
learning. We first define the meaningful learning pedagogy and implications towards learners’ behavior in e-learning system. Finally we analyze learners’ characteristics and define the cluster of learner’s characteristics by using K-Means cluster method. A case study based on e-learning data log from – students on Computational Intelligence Course at Software Engineering Department, Universiti Teknologi Malaysia. From meaningful learning pedagogy, this paper contributes to describes the characteristics of learners during online learning, the discussion and limitation was describes in the end of this paper.
Keywords: e-learning, meaningful learning characteristics, learners’ behavior, clustering
Introduction
Papers by Dewi Octaviani
Keywords: ontology, semantic reasoning, knowledge construction, e-learning activities and actions, Jena rules.
This paper also investigates the potential factors that contribute to active learning in the e-learning system (i.e. Moodle); which focuses on the activities and actions that able to support active learning by lecturers and students. The e-learning data log are observed to measure the number of hits of activities and actions on the e-learning system to determine whether the active learning characteristics have been achieved. The statistical analysis method is used to analyze activities and actions on data log.
Finally, this paper reveals a guideline with activities and actions in an e - learning system that support active learning. Therefore, with appropriate and proper usage of the tools in the e-learning system, learning experiences can be enhanced. This guideline proved to be useful to enhance using active learning technology by the educators.
Keywords: e-learning system; active learning; e-learning activities and actions
Key words: e-learning, information system success factors, system quality, information quality, service quality.
learning. We first define the meaningful learning pedagogy and implications towards learners’ behavior in e-learning system. Finally we analyze learners’ characteristics and define the cluster of learner’s characteristics by using K-Means cluster method. A case study based on e-learning data log from – students on Computational Intelligence Course at Software Engineering Department, Universiti Teknologi Malaysia. From meaningful learning pedagogy, this paper contributes to describes the characteristics of learners during online learning, the discussion and limitation was describes in the end of this paper.
Keywords: e-learning, meaningful learning characteristics, learners’ behavior, clustering
Introduction
Keywords: ontology, semantic reasoning, knowledge construction, e-learning activities and actions, Jena rules.